Department of Science and Technology Institutionen för teknik och naturvetenskap Linköping University Linköpings Universitet SE-601 74 Norrköping, Sweden 601 74 Norrköping

LiU-ITN-TEK-A--10/035--SE

Real time sampling ofutilization at Ericsson Test

PlantsMarky Egebäck

Sebastian Lindqvist

2010-06-10

LiU-ITN-TEK-A--10/035--SE

Real time sampling ofutilization at Ericsson Test

PlantsExamensarbete utfört i kommunikations- och transportsystem

vid Tekniska Högskolan vidLinköpings universitet

Marky EgebäckSebastian Lindqvist

Handledare Torbjörn WikströmExaminator Di Yuan

Norrköping 2010-06-10

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© Marky Egebäck, Sebastian Lindqvist

AbstractThis master’s thesis has been written within the field of Electrical Engineering at the De-partment of Science and Technology, Linköping University. The work has been carried out atEricsson’s site in Linköping during the spring of 2010.

The purpose of this master thesis was to construct a model which could capture and presentthe utilization rate of test equipment at a telecom company in general. Since this field hasnot been studied very much in the past, it was decided to study a model from the productionindustry and try to reuse some of the basic ideas from this model.

From this generic model a recommendation is given as to how the model could be used byimplementing a Common Utilization Tool, which could be used to store, configure and presentutilization data from all types of equipment in Ericsson’s test environment. This common uti-lization tool will use measurement modules that will both collect and classify the state of theequipment and deliver the result to a common database.

To this Common Utilization Tool a measurement module has been implemented which samplesBase Station Controllers (BSC) in Ericsson’s test environment state; used, unused and down.This implementation is also validated against real measured data from testers to conclude ifthe results are accurate.

i

Acknowledgments

We would like to express our deepest gratitude to our supervisors Torbjörn Wickström at Er-icsson AB, David Gundlegård and Di Yuan at the Department of Science and Technology inLinkoping’s university. Without your knowledge, support and helping hand we would neverbeen able to complete this thesis.

A special thanks to Thomas Thunell, Anders Hollstedt, Jonas Madsen and the rest of theATD Team for answering all our LTE Util Tool and THC questions. Without you guys thework with the BSC utilization module, which is a big part of our work, would not have beenpreformed as smoothly.

We would also like to thank the rest of the people who helped us create the BSC utiliza-tion module; Ulf Arkad, Tomi Ojala Carlbergh, Jens Lindberg and Samka Nyberg.

Another person that we would like to thank is Liz Foxbrook for greatly improving the lan-guage in the report.

Last but not least we would like to thank the people at our section and department thathas given us so much support and encouragement during the work of this thesis.

Linköping, May 2010.

Marky Egebäck and Sebastian Lindqvist

iii

Contents

1 Introduction 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.5 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.6 Confidentiality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.7 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.8 The GSM Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.9 Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.10 Ericsson test environment - BETE . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Frame of reference 72.1 OEE - Overall equipment efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1.1 Availability Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.1.2 Operational Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.1.3 Rate Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.4 Quality Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.5 Applications of OEE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.1.6 Limitations of OEE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Performance Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3 Sampling theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3.1 Sampling methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.3.2 Sampling period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.3.2.1 Normal distribution . . . . . . . . . . . . . . . . . . . . . . . . . 122.3.2.2 Binomial distribution . . . . . . . . . . . . . . . . . . . . . . . . 14

2.4 Measurement process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.4.1 Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.4.2 Activities in the measurement process . . . . . . . . . . . . . . . . . . . . 15

2.4.2.1 Establish and sustain measurement commitment . . . . . . . . . 152.4.2.2 Plan the measurement process . . . . . . . . . . . . . . . . . . . 152.4.2.3 Perform the measurement process . . . . . . . . . . . . . . . . . 162.4.2.4 Evaluate measurement . . . . . . . . . . . . . . . . . . . . . . . 16

2.4.3 The measurement information model . . . . . . . . . . . . . . . . . . . . . 16

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vi Contents

3 GSM 193.1 GSM specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.1.1 GSM Phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.1.2 Services in GSM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.2 GSM Network Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213.2.1 Radio Subsystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.2.2 Mobile Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.2.3 Base Station Subsystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.2.4 Network and Switching Subsystem . . . . . . . . . . . . . . . . . . . . . . 243.2.5 Mobile Switching Center (Mobile Services Switching Center) . . . . . . . 243.2.6 SMSC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.3 GSM Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.4 Databases and Registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.4.1 Home-location-register (HLR) . . . . . . . . . . . . . . . . . . . . . . . . . 253.4.2 Visitor-location-register (VLR) . . . . . . . . . . . . . . . . . . . . . . . . 26

3.5 Operations Support Subsystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263.5.1 Operations And Maintenance Center . . . . . . . . . . . . . . . . . . . . . 26

3.5.1.1 Telecommunications management network . . . . . . . . . . . . 263.5.2 Authentication Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.5.3 EIR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.6 Radio interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.6.1 Logical Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3.6.1.1 Traffic Channels . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.6.1.2 Control Channels . . . . . . . . . . . . . . . . . . . . . . . . . . 303.6.1.3 GSM Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

3.7 Protocols in GSM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323.8 Addressing and localization in GSM . . . . . . . . . . . . . . . . . . . . . . . . . 33

3.8.1 International Mobile Subscriber Identity (IMSI) . . . . . . . . . . . . . . . 343.8.2 Temporary mobile subscriber identity (TMSI) . . . . . . . . . . . . . . . . 343.8.3 Local Mobile Subscriber Identity (LMSI) . . . . . . . . . . . . . . . . . . 343.8.4 Mobile Station (or Subscriber) ISDN Number (MSISDN) . . . . . . . . . 343.8.5 The Mobile Station Roaming Number (MSRN) . . . . . . . . . . . . . . . 343.8.6 International mobile station equipment identity (IMEI) . . . . . . . . . . 34

3.9 Data services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.9.1 GPRS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

3.9.1.1 SGSN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.9.1.2 GGSN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.9.1.3 Location managemnet . . . . . . . . . . . . . . . . . . . . . . . . 36

3.9.2 EDGE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

4 GSM Evolutions 374.1 WCDMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.1.1 System and network architecture of WCDMA . . . . . . . . . . . . . . . . 374.2 LTE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4.2.1 System and network architecture of LTE/SAE . . . . . . . . . . . . . . . 39

Contents vii

5 Current Solutions 415.1 STP Utilization tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

5.1.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415.1.2 Data collecting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425.1.3 Data presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425.1.4 Evaluation of the tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.2 Utilization tool for the eNodeB in LTE . . . . . . . . . . . . . . . . . . . . . . . . 435.2.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435.2.2 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445.2.3 Data presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445.2.4 Evaluation of the tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5.3 Ericsson Real Utilization Measurement Solution (ERUMS) . . . . . . . . . . . . . 455.3.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455.3.2 Data collecting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455.3.3 Data presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465.3.4 Evaluation of the tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

5.4 ENSIEM adaption for node utilization . . . . . . . . . . . . . . . . . . . . . . . . 475.4.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475.4.2 Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485.4.3 Presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485.4.4 Evaluation of the tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.5 Booking degree as utilization measure . . . . . . . . . . . . . . . . . . . . . . . . 495.6 Other test efficiency indicator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

5.6.1 Fault-slip-through . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

6 General model for utilization measurements 536.1 Efficiency indicators for test equipment . . . . . . . . . . . . . . . . . . . . . . . . 536.2 Equipment Utilization Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . 556.3 The state of the test equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

6.3.1 Measurement methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 576.3.2 Classification of equipment state . . . . . . . . . . . . . . . . . . . . . . . 576.3.3 Time resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

7 Common Utilization Tool 597.1 Schematic model for a general utilization tool . . . . . . . . . . . . . . . . . . . . 597.2 Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

7.2.1 Time resolutions of measurements . . . . . . . . . . . . . . . . . . . . . . 617.3 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 617.4 Common configuration layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647.5 Common presentation layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647.6 KPI reports presentations layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

8 BSC Utilization Module 698.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

8.1.1 Type of test cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 698.2 Pre-study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

8.2.1 Equipment states . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708.2.2 Possible measure points . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

8.2.2.1 Capture real user traffic . . . . . . . . . . . . . . . . . . . . . . . 71

viii Contents

8.2.2.2 Capture operations and maintenance traffic . . . . . . . . . . . . 718.2.2.3 Energy consumption . . . . . . . . . . . . . . . . . . . . . . . . . 718.2.2.4 Measuring inside the node . . . . . . . . . . . . . . . . . . . . . 728.2.2.5 Indirect measuring points . . . . . . . . . . . . . . . . . . . . . . 72

8.2.3 Chose of measuring point . . . . . . . . . . . . . . . . . . . . . . . . . . . 728.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

8.3.1 Base measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738.3.2 Code structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

8.3.2.1 THC Test Case . . . . . . . . . . . . . . . . . . . . . . . . . . . 738.3.2.2 BSC Utilization Test Code . . . . . . . . . . . . . . . . . . . . . 748.3.2.3 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

8.4 Collected data and classification of BSC state . . . . . . . . . . . . . . . . . . . . 758.4.1 Classification of the equipment state . . . . . . . . . . . . . . . . . . . . . 758.4.2 Validation of the classification . . . . . . . . . . . . . . . . . . . . . . . . . 798.4.3 Samples or Counters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 798.4.4 Classifying Function Test . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

9 Utilization modules for other equipment 819.1 UE simulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

9.1.1 UE simulator 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 819.1.2 UE simulator 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829.1.3 Conclusions for the UE simulators . . . . . . . . . . . . . . . . . . . . . . 82

9.2 Protocol analyzers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829.2.1 Tektronix K15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 829.2.2 Nethawk M5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 839.2.3 Proposed solution for packet analyzers . . . . . . . . . . . . . . . . . . . . 83

10 Discussion 8510.1 Possibilities and potentials of equipment utilization measurements . . . . . . . . 8510.2 The value of a Common Utilization Tool . . . . . . . . . . . . . . . . . . . . . . . 8510.3 Weakness of the BSC Utilization Module . . . . . . . . . . . . . . . . . . . . . . . 8610.4 Future work with the BSC Utilization Module . . . . . . . . . . . . . . . . . . . . 8710.5 Future work with the Common Utilization Tool . . . . . . . . . . . . . . . . . . . 8710.6 Future work on the utilization uodules for other equipment . . . . . . . . . . . . 8810.7 Future work in the test environment . . . . . . . . . . . . . . . . . . . . . . . . . 88

11 Conclusion 89

Bibliography 91

A Test Harness Core (THC) 93A.1 Definitions in THC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93A.2 System overview and concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

A.2.1 Resource Factory (RF) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94A.2.2 Test Execution System (TES) . . . . . . . . . . . . . . . . . . . . . . . . . 95A.2.3 Test Tool Middle Ware Subsystem (TTMW) . . . . . . . . . . . . . . . . 96A.2.4 Log Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Contents ix

B Ericssons Base Station Controller (BSC) 100B.1 Base Station System (BSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

B.1.1 TRC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100B.2 BSC Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101B.3 BSC Hardware and Subsystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102B.4 APZ Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

B.4.1 Central Processor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102B.4.2 Regional Processor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103B.4.3 Adjunct Processor Group . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

B.4.3.1 STS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104B.5 OM interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104B.6 Man-Machine Language (MML) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

B.6.1 Command structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Acronyms and glossaries 107

List of Figures1.1 The steps in a software development project . . . . . . . . . . . . . . . . . . . . . 4

2.1 The different states of the equipment . . . . . . . . . . . . . . . . . . . . . . . . . 82.2 The percentage error as a function of the number of samples . . . . . . . . . . . . 132.3 Information model of the measurement process . . . . . . . . . . . . . . . . . . . 17

3.1 GSM Network Architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.2 Flow chart of the activities in the ME. . . . . . . . . . . . . . . . . . . . . . . . . 233.3 TMN elements and the connections in the TMN model. . . . . . . . . . . . . . . 273.4 GSM normal burst structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293.5 GSM frame structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.6 The traffic multiframe used in GSM. . . . . . . . . . . . . . . . . . . . . . . . . . 323.7 GSM signaling protcol structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.8 Architecture of a GPRS system . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.1 Architecture of a WCDMA Network . . . . . . . . . . . . . . . . . . . . . . . . . 384.2 Architecture of a LTE system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

5.1 The second page of the web-GUI that shows the utilization during a time periodof 6 hours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.2 The main page of the web-GUI that shows the utilization for one day . . . . . . 435.3 Diagram over data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445.4 Screenshot of the presentation of utilization . . . . . . . . . . . . . . . . . . . . . 455.5 ERUMS schematic system description . . . . . . . . . . . . . . . . . . . . . . . . 465.6 Screenshot of the presentation using pChart . . . . . . . . . . . . . . . . . . . . . 465.7 Screenshot of the presentation of utilization . . . . . . . . . . . . . . . . . . . . . 495.8 Definition of fault-slip-through . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.1 The OTEE concept, which put Equipment Utilization in a context . . . . . . . . 546.2 Sampling a binary signal that describe the equipment state. . . . . . . . . . . . . 58

7.1 Schematic model over a general utilization tool . . . . . . . . . . . . . . . . . . . 607.2 Database structure for the Common Utilization Tool . . . . . . . . . . . . . . . . 627.3 The main view in ECUT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667.4 The resource view in Common Utilization Tool . . . . . . . . . . . . . . . . . . . 67

8.1 The BSC utilization modules executing environment. . . . . . . . . . . . . . . . . 748.2 Visualization of the collected record . . . . . . . . . . . . . . . . . . . . . . . . . 778.3 Histogram with the number of typed MML commands when there are no traffic . 788.4 Visualization of classified record . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

A.1 Test Harness Core (THC) system components (used with permission by JonasMadsen, Ericsson AB) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

A.2 Resource Manager (used with permission by Jonas Madsen, Ericsson AB) . . . . 97A.3 The ATE GUI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98A.4 Log Session View in THC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99A.5 Log Record View in THC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

Contents xi

B.1 The different BSC configurations . . . . . . . . . . . . . . . . . . . . . . . . . . . 101B.2 The BSC components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103B.3 Possible connections to the BSC OM interfaces . . . . . . . . . . . . . . . . . . . 104

List of Tables2.1 Performance metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2 Error for different number of samples when p=0.5 . . . . . . . . . . . . . . . . . 142.3 Measurment information model and example [14] . . . . . . . . . . . . . . . . . . 18

3.1 Logical layers in TMN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

5.1 Example table over slip through data for each phase . . . . . . . . . . . . . . . . 50

6.1 Equipment efficiency indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536.2 General equipment states . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566.3 Error in indicator when sampling equipment state . . . . . . . . . . . . . . . . . 58

7.1 Table: resource . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637.2 Table: resource_utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637.3 Table: site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637.4 Table: resource_type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647.5 Table: resource_group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 647.6 Table: group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

8.1 Examples of records collected with BSC utilization module . . . . . . . . . . . . 76

9.1 Example of base measures of 15 Nethawk M5 Servers . . . . . . . . . . . . . . . . 84

Chapter 1

Introduction

1.1 BackgroundEricsson AB is a world leading company in telecommunications. The company develops mobiletelecommunication systems for Global System for Mobile Communications, Wideband CodeDivision Multiple Access and Long Term Evolution and also provides services, features andupgrades to these systems.

When a problem in a product occurs the cost of solving it is lower at an earlier stage in thedevelopment. That is why Ericsson carries our comprehensive tests during the developmentof a product. The tests have to be carried out in an environment which corresponds to theenvironment where the products will be used, i.e. a configuration in the network that is similarto the operator’s network, the operator being, in general, the customer of the product.

In the world mobile telecommunications new technologies are rapidly being released, whileprevious technologies are still being developed and used. Ericsson’s range of products has there-fore increased constantly. New products are required to function alongside previous technologiessince operators want to reuse the old systems in order to minimize the cost of new investments.The process of detecting and correcting problems in products will require test equipment, andthe demand for equipment for the tests has therefore also grown constantly. This equipment isvery expensive and many of the products are the same ones that Ericsson sells to its customers.Efficient use of the test equipment is very important to improve the quality of the products inthat more tests can be performed. Also, the costs can be reduced, since new investments maynot be needed.

To increase the efficiency, tools that can measure the utilization of the test equipment areneeded. At present Ericsson does not have a generic model for measuring the use of equipmentin the test environment. Such a tool can be of great help in the decision making process whethernew investments of test equipment are needed or whether existing equipment can be used moreefficiently. Today, there exist a few tools that can measure the utilization of the equipment, butthis applies only to a small part of the equipment. These tools are not consistent concerninghow they define and measure utilization. When the usage of equipment is evaluated it is oftencarried out based on the degree of booking, since all equipment has to be booked, and isdefined in the same manner. The topic of creating a model to measure the utilization on thetest equipment in BUGS Ericsson Test Environment-lab has been discussed for a long time atEricsson. As we mentioned earlier, test equipment is very expensive and therefore a resourcethat must be utilized as much as possible.

1

2 Introduction

Employees performing tests cannot report the utilization, since there is too much equipmentand too many test activities being carried out. Furthermore, it is not efficient to monitor usagemanually, and the measurements need to be performed automatically. A valid model thatpresents the utilization of the equipment could allow better scheduling and planning of the testactivities.

1.2 PurposeThe purpose of this master’s thesis is to construct a model for measuring the utilization of testequipment at Ericsson’s test plants, mainly at the test site in Linköping.

Since the utilization will not be useful without a clear definition as to what the term meansin general, the first part of the study will try to define a generic definition for all type of testequipment. During this work the equipment will be studied in detail to find efficient and feasibleways of conducting these utilization measurements. The study ends by creating a prototypethat can be used to measure and present the utilization rate of the devices that the master’sthesis focuses on.

1.3 ObjectivesA definition of utilization is needed for the model. The definition should be constructed to meetthe information requirements (needs) of the end users. The definition also needs to be suitablefor the different types of equipment, i.e. a model that is generic for all equipment. The termgeneric also implies that the model presents a utilization rate that is comparable for all differenttypes of equipment. How the data is sampled and collected will have to be adjusted dependingon the equipment. The time period, which can correspond to the sampling rate, should be aslow as possible with respect to aspects minimizing the interference on the actual tests and thelimitations in data storage and representation.

The thesis will also suggest how the data should be transferred from the equipment, as wellas stored and presented in a safe and time efficient way. If software has to be installed in anypart of the equipment it has to be carried out while taking the safety issues into consideration.

1.4 MethodArticles, literature and other master’s thesis will first be studied to obtain a theoretical back-ground for the thesis. The architecture and system overviews of GSM, WCDMA and LTE willbe studied and presented. The theoretical part will also contain theories about utilization andsampling and capturing of data. If there is any previous work directly related to the subject ofthis thesis, it will be studied.

It is important to understand how Ericsson have technically implemented the componentsof the mobile systems technically, in order to get a view over the range of test equipment and adeep knowledge in how the testing process is carried out. This has to be done within Ericsson,mainly by interviewing the employees and surveying the test environment. Both formalizedinterviews and discussions will be conducted with the individuals involved with the testingand test equipment. The test environment and the equipment will also be studied in sufficientdetail.

1.5 Scope 3

The concept will be evaluated by implementing a prototype based on the general model.The implementation will be validated against real, manually collected, utilization data fromthe testers of the equipment.

1.5 ScopeSince time is limited for this master thesis the implementation part of the model for measuringutilization will be limited to a subset of the equipment in the testing environment. We willfocus on one of the Linköping site’s most important pieces of equipment; the BSC. The studywill also investigate how measurements can be carried out on other types of equipment. Themain motivation why the BSC has been chosen is that it is based on older technology whichdoes not use ip for delivering user data, and that previous studies on this platform have provedit difficult to determine the usage.

1.6 ConfidentialitySome parts of the thesis are considered to be of a confidential nature by Ericsson and havetherefore been edited to hide the sensitive details. The values of the resources utilization ratehave been change but since it is not the specific values that are of interest in the thesis butrather how it is defined, collected and presented.

Also some of Ericsson suppliers and there tools that are used at Ericsson’s test plants hasbeen denoted supplier 1, 2 and simulator 1, 2.

1.7 OutlineAfter this introductory Chapter we will present the frame of reference that we have found inthis area. In this Chapter we will give a brief introduction to Overall Equipment Efficiency,performance measures, sampling theory and a general measurement process. Since this reportwill focus on GSM test equipment we will, in Chapter 3, present the basic concepts of the GSMnetwork and briefly describe the components in WCDMA and LTE. These chapters will providethe necessary background that was used to create the general model on how Ericsson shouldwork with utilization data for their test equipment. The generic model will then be presentedin Chapter 6.

The next Chapter will present the authors’ recommendations at to how a common utilizationtool for storing, configuring and presenting utilization data from this test equipment should beorganized with the necessary collector modules.

The report will follow with a description of this implementation for a Base Station Controllerutilization measurement module. In this Chapter we will also present the working process ofthis work and the analysis of the output from the module.

The last part of this report will focus on a discussion and conclusion of our study and wewill also present how the work with a common utilization tool could continue.

1.8 The GSM OrganizationAt Ericsson in Linköping software is being developed for LTE and GSM. In GSM it is BSC andBSS that are being developed in separated departments with these names [4]. In this chapter

4 Introduction

the organization and project structure is briefly described. The departments have differentfunctions in the GSM design projects or have support and maintenance functions. The testequipment that is needed and the type of test cases that are carried out differs between thedepartments.

Ericsson is organized into four main business areas; CDMAMobile Systems, Global Services,Multimedia and Networks. Within the business area Networks there is a development unit formobile radio network named DU Radio. The next level below in the organization hierarch isthe Product Development Unit (PDU) where GSM RAN is one unit. The PDU GSM RAN hasthe following departments that use real Base Stations Controller (BSC) in their operations atthe test site in Linköping:

BSS & BSC System is technical responsible for BSS and BSC. BSS & BSC I&V isresponsible for BSS and BSC integration and verification. BSC Design contributes to thedevelopment of new features and products in GSM/GPRS/EDGE. The department design,implements, function tests and maintains the features and products. BSS & BSC PLMhas third line support at BSC software projects from due to its being General Available forall customers until the maintenance responsibility of Ericsson expires. The department is alsoresponsible for packaging of software upgrades from design to customer.

1.9 TestingTesting is a main part of the development of new products in mobile networks. It is importantthat the product is tested in real networks that correspond to the networks of the customersand that the product is tested under similar conditions as it will be used in. In this sectionthe test process within a product development project is described. When a problem with aproduct is discovered during a test a TR(Trouble reports) is written to the person responsiblefor the design of the code that caused the problem. There are four levels of TR; A, B, C and D.The level of the TR specifies how severe the consequence of the fault is. If a fault is discoveredand a TR with the level A is written it denotes that the fault has to be solved before any testwork can continue.

Function test (FT)

System Integration Test (SIT)

System Robustness Test (SRT)

System Verification Test (SV)

First Office Application (FAO)

Product Introduction Complete (PIC)

Ready For Acceptance (RFA)

General Aviliability (GA)

BSC Design BSC&BSS I&V BSC&BSS System BSC&BSS I&V FOA customers

Figure 1.1. The steps in a software development project

Figure1.9 shows a normal product software development project within DU RAN. The stepsis described below:

• Function Test. The function is tested independently. Almost all function testing thatis carried out in simulates or emulates hardware at BSC Design.

1.10 Ericsson test environment - BETE 5

• System Integration Test. Functions are integrated together and tested.

• System Robustness Test. This is an early load test and early system verification.After this test the product is accorded the status: PIC (Product Introduction Complete)and no more software code can be added.

• Feature Test. A specific feature is tested in a real environment at BSS I&V with thefocus on that the feature works as it was intended.

• System Verification Test. After the PIC approval, BSS I&V carries out a full scalesystem verification test and after the System Verification Test the product obtains thestatus RFA(Ready for acceptance).

• First Office Application. In this stage the product is released to FAO-customers, whocarry out validation tests and therefore receive a discount on the product. After the FAOthe product obtains the status GA (General Availability), which means that it is releasedon the market. When a product obtains status GA, there cannot be a TR with level Afrom earlier tests.

1.10 Ericsson test environment - BETEThe organization that owns, runs and configures the test environment, is called BETE (BUGSEricsson Test Environment). Earlier it was a separate company, but is now part of Ericsson.The test plants are spread around the world. In Sweden the larger test plants are in KistaStockholm, Linköping, Gothenburg, Karlskrona and Lund. The users of the test equipment,the development projects, book the test equipment and pay for the time they have used it.The relationship between BETE and the projects is a supply-customer relationship, which isintended to provide a situation where the projects do not book more equipment than necessaryand where BETE do not purchase equipment that will be unused. BETE purchase equipmentbased on forecasts of the demand for equipment from the customers (development projects).

Equipment is deprecated over three years and is one part of BETE’s expenses. Anotherpart of the expenses goes to salaries for the work forces that administrate , setup, maintainand support the equipment. Other expenses are guarantees that are direct costs that cannot bedepreciated. When a new investment in equipment is made, the depreciation for the equipmentis only one part of the total expenses. After three years, when the depreciation of equipment iscomplete, there are still costs for the equipment in terms of work force salary and guarantees.To reduce the expenses for BETE, investments have to be decreased and some of the existingequipment has to be scrapped.

The demand of equipment in a project is rather unique. A specific configuration of hardwareand software is needed that suits the tests that will be carried out. Therefore a STP (Systemtest plant) is constructed that contains one BSC in GSM or one Radio Network Controller inWCDMA and other components needed for the tests. Other components can differ a great dealand can be a number of Base Station Transceiver Station and traffic generators. One STP isconfigured to satisfy the requirement of a project. The STP is then booked in a system calledBAMS and from the booking time in the system the payments is calculated . In BAMS, STPsand other equipment are booked with a minimum booking time of less than one hour. Sincethe project lasts for several months one STP is often booked for the same time period. WhenBETE needs to carry out maintenance BAMS allows a booking for such event, even though itis not widely used.

Chapter 2

Frame of reference

Chapter Introduction

The Chapter contains the results of the study of literature, articles, books and other documen-tation used in this thesis. The theories, concepts and methods is the foundation for the generalmodel are presented later in Chapter 6.

2.1 OEE - Overall equipment efficiencyThe concept of OEE was proposed by S. Nakajima in ”Introduction to TPM: Total ProductiveMaintenance” in 1988. The definition and measurement of equipment productivity has beendeveloped by the Semiconductor Equipment and Materials International (SEMI) [8]. It is usedwithin the production industry companies to measure how efficient the production equipmentis utilized.

OEE contains four different ratios concerning the efficiency of equipment. The four ratiosare multiplied together to achieve a total measurement over how efficiently equipment is used.By separating the efficiency measures into four ratios, it is easier to see where action should betaken to increase the overall equipment efficiency.

Loading time is defined as the time the equipment is planned to be used. Weekends andholidays are withdrawn from the total time to obtain the loading time. The authors of [23]suggests that total time is used instead of loading time. Total time is all available time, 8760hours per year, which is the maximum potential time equipment can be used. However thereare some questions concerning using all 8760 hours per year. There can be legal restrictions thatlimit the number of hours per day that can be use for production. It can also be economicallyinefficient, in case of low demand, which does not motivate a night shift in the production. Inthese cases and for similar reasons it can be argued that this time should be subtracted fromthe total time. When total time is used, OEE can be referred to as TEEP (Total EquipmentEfficiency Performance).

7

8 Frame of reference

To calculate OEE the status of the equipment has to be monitored over time. The statusof the equipment can be defined as in Figure 2.1 [23]. The Non-schedule state is when the

Non-schedule state

Schedule down state

Unscheduled down state

Engineering state

Standby state

Productive state

Total time

Equipment uptime

Figure 2.1. The different states of the equipment

equipment is not intended to be used. This can be due to weekends or holidays. The scheduledown state is when the equipment cannot be used because of maintenance and setup time.The down time is also categorized into an unscheduled down time, which is all the down timethat occurs unexpectedly. The Engineering state is when experiments are performed on theequipment to improve its performance. When the equipment is up but not operating it is inthe standby state. The reason for not operating can be due to missing operators or lack of rawmaterial. An operator can be missing because of breaks, lunches or meetings. The productionstate is when the equipment is producing items as is intended.

The four underlying matrices for OEE are defined by SEMI, in document E79-0200 [8]:

OEE = Availability ∗ (Operational ∗Rate) ∗Quality (2.1)

2.1.1 Availability Efficiency

Availability Efficiency = Equipment Uptime

Total T ime(2.2)

The ratio shows the available time the equipment can be used compared to the total availabletime. Non-scheduled time is the time where the equipment is not scheduled to be used. Thisis lost time since it could be used for production. Both scheduled and unscheduled down timedecrease the availability of the equipment. Unscheduled down time can be repairs and scheduleddown time can be maintenance of the equipment.

2.1.2 Operational Efficiency

Operational Efficiency = Production T ime

Equipment Uptime(2.3)

The production time is the time that the equipment is carrying out the activity that itis intended to do. This is as opposed to the potential maximum production time, which isthe equipment uptime. The time when no production is being carried out can be due tolack of material, to the fact that no operator is available or that engineering experiments arecontracted. The operational efficiency and rate efficiency is often combined to one indicatorcalled Performance Efficiency.

2.1 OEE - Overall equipment efficiency 9

2.1.3 Rate Efficiency

Rate Efficiency = Theoretical Production T ime

Production T ime(2.4)

The rate is the speed of the production. A theoretical production time has to be calculated,which is the lowest possible time for producing the actual number of items. It is then comparedto the real production time for the items. A lower production rate gives fewer produced items,which lowers the efficiency.

2.1.4 Quality Efficiency

Quality Efficiency = Acceptable units

Units started(2.5)

The Quality Efficiency indicator illustrates inefficient equipment usage due to low qualityof the items. If the quality of a unit is lower than the acceptable limit the item is rejected.In some equipment the production of an item is aborted before it is finished if the quality isunacceptable, in order to prevent unnecessary processing. The indicator does not capture thiseffect by saving equipment time, since it regards all started items and finished items as havingthe same production time.

2.1.5 Applications of OEEThe main purpose of OEE is to provide a comprehensive measurement of the equipment ef-ficiency. The method tries to cover all the factors that affect the efficiency but also factorsthat are independent from the equipment itself as discussed in [8]. OEE will decrease if thereis a lack of input material, which improvements in the equipment cannot influence. If specificequipment is considered to be a bottleneck the OEE indicator will show if improvements in theequipment are possible. The indicator will also highlight within which area action should betaken.

The OEE can also be of great help in investment decisions as discussed in [15]. If a com-pany have low equipment efficiency and no performance indicators it is likely that they see noother solution than to make new investments to handle capacity problems. With the use ofOEE, existing equipment and plant can be evaluated and improved before new investments areconsidered.

2.1.6 Limitations of OEEIn the OEE formula there is an estimation of the theoretical production time per item unit.This estimation can be a source of error since a theoretical production time often is difficultto estimate and includes subjective evaluations. Another objection against OEE is that almostall types of equipment used in production have to be down for maintenance and repair for aperiod of time. This might result in that OEE is not able to achieve 100 % in practice and itis not certain that the value itself is a correct and fair measurement of the overall equipmentefficiency; it can be hard to establish the practical top limit of the OEE. Nevertheless OEE canbe useful as an index for comparisons between the efficiency of equipment before and after animprovement. Also it is still useful for pointing out where improvements should be carried out.

An additional limitation of OEE is the quality of the collected data. Data has to be collectedregularly and the equipment state has to be detected by using the data. The variables thatvary over time need to be sampled and they are downtime, set-up time, production time per

10 Frame of reference

unit, number of items started and accepted number of items. The theoretical production timeper unit is constant and needs therefore be set only once. This is the data needed for calculatedOEE according to the definitions above. Possible errors and bias in collecting and interpretationof data in these variables will give errors in the OEE value.

2.2 Performance ManagementTo evaluate the performance in a complex organization quantitative measurements are needed.Many companies use measurements called indicators to evaluate the performance. The indica-tors are values that are based on data collected from within the organization. The measurementsare often presented on a dashboard or scoreboard to the management, at a strategic or at atactical level. The way different companies use performance measurements differs a lot andall companies have their own interpretations and implementations. In this chapter a defini-tion of three performance indicators are presented and discussed. These three indicators ofperformance measures that can be used in an organization are [20]:

1. Key Result Indicators (KRI).

2. Performance Indicators (PI).

3. Key Performance Indicators (KPI).

KRI measures how well you have done in the past [20]. It is measured within a time periodof a month or quarter and there should not be more than ten to twenty KRIs. Examples ofKRIs are: customer satisfaction, market share and profit. However the measurement does notshow which actions that should be carried out in order to improve performance in the future.That is the big difference compared to KPI which indicates what should be done to improveperformance. When the unit for a measure is in money it is likely that the measure is a KRI,since the profit or return of an investment shows the outcome of an action and not the actionsthat need to be initiated. A report including the KRI is suitable for a board or managementresponsible for strategy decisions.

PI is a indicator that tells you what to do to increase performance [20]. Compared to KRIit focuses on one particular area of performance and they can be both strategic and tactical.Organizations can have up to 80 PIs and they should be well defined. Examples of PI canbe, for an airline company, be the percentage of lost luggage and for a hospital the percent ofinfected patients after surgery. A PI gives a clear view over what needs to be done to increaseperformance, but they are limited to one area and are not crucial for the overall strategydecisions.

KPI show you what to do to increase performance dramatically [20]. They should be moni-tored at a regular basis, since they are the most interesting indicators from a management pointof view. If a indicator is calculated every month or quarter it cannot be of such great interestthat it qualifies to be a KPI. Examples of KPIs are: number of patients waiting for treatmentat a hospital or number of minutes delay in average for an airline company. Both these exampleshow what should be done to improve performance. The hospital needs to lower the numberof patients in the queue waiting for treatment. It will give a domino effect in the performancesince the quality for the patient will increase because of lower waiting time. The patients willalso have less risk for complications since they can be treated earlier. For the airplane companylate planes means higher costs, lower customer satisfaction and higher fuel consumption. Asthese two example shows a real KPI should affect several of the critical success factors (CSF)

2.3 Sampling theory 11

and give clear information regarding intervention. In literature authors have suggested up to10 or 20 KPIs per organization [20]. It is rarely needed or not even possible to have more than10 KPIs. Of course the number of PIs is much higher.

Table 2.1. Performance metrics

Metrics Numbers Monitored DefinitionKRI 10 Monthly Tells you what you have donePI 80 Daily Tells you what to do

KPI 10 Daily Tells you what to do to dramat-ically increase performance

In Table 2.1 the performance indicators are shown together. When working with KPIs it isimportant that they are introduced in a carefully way and that time is allowed for evaluation.After some period of time and evaluations it is possible that the KPIs need to be modified.Since the number of KPI should be small it is important to evaluate the use of them and ifthey are not used the production of them should be stopped[17].

2.3 Sampling theorySampling defines how a representative subset of observations can be chosen from a total pop-ulation of observations. The reason for taking a subset of data and not collecting the entirepopulation can be various. The population can be large which makes it time and resourceconsuming to capture it all. It may also be that the measuring interferes with the objects andby using sampling the interference to the population is minimized. A sample should be rep-resentative for the population and if the numbers of samples are too small it will not capturethe main characteristics of the population. Consequently there is a tradeoff between tryingto capture the essence of the population and minimizing the number of samples. Samplingshould define the quantity, frequency and location of data to be sampled [24]. It is also used intelecommunication and signal processing when measuring a continuous signal into a numericalsequence.

The population can be a set of objects or a variable that changes over time. In a manufac-turing factory a sample of the produced items can be tested to insure that the batch has goodquality. It may not be efficient to test all the produced items or not even possible if the qualitytests consume the items. In that case it is important that the samples are chosen in a way thatthey are a representative subset of all produced objects. An example of a continuous variablethat is sampled is the speed of a vehicle which is fed into the the speedometer. The speedometerwill show an instantaneous value of the speed and can be sampled to get an average speed overa period of time. The average speed will be more accurate if the signal is sampled with higherfrequency [19].

If the speedometer is an instantaneous sample type the trip meter is a cumulative sampletype. The cumulative type can also be called a counter and it adds values to a variable overtime. The counter can be zeroed at some event, for instance when a restart occur. The counterwill provide information about what has happen between the two samples of a variable, sincethe exact increase or decrease can be calculated if the counter is not reset. How the counter haschanged during one time period cannot be known, unless the function of the variable is knownin advance.

12 Frame of reference

2.3.1 Sampling methodsData can be sampled either event driven (pushed) or time driven (pulled). If the sampling isevent driven it can denote that the sampling frequency is dynamic and changes due to someevent. An event driven sampling can also include information on how to react when certainevents occur [19]. Rules can be set up as to how to change the sampling plan under specialcircumstance.

Pulled sampling is much more common and especially systematic sampling, where the sam-pling is conducted systematically. Sampling variables in the time domain means to samplewith the same time interval and frequency. If the population is a set of objects, systematicsampling can, for example, be to sample every tenth object in an order. However if the ob-jects are arranged in a systematic way, this sampling method risks giving samples that are notrepresentative of the population. In some cases this can be dealt with by sampling randomly,although it also has drawbacks. The choice of sampling method that is most suitable dependson the characteristics of the population.

2.3.2 Sampling periodAll measures do not have to be sampled at the same rate [19]. The measure type affects thesuitable sampling period. A measure for a cumulative variable that show the average changeof value over a time period will not give higher accuracy even though more sample per timeperiod are used. It is shown by the following: Let Xi = X(ti) be a strict increasing function,where Xi is the sampled value at ti for i = 0,1,...,n and where n is the number of samples. Theavarage increase from ti to ti+1 is Xi+1−Xi

ti+1−ti and the sampling interval, ti+1− ti = ∆, is constantfor all i.

1n

n−1∑i=0

Xi+1 −Xi

ti+1 − ti= 1n∆

n−1∑i=0

Xi+1 −Xi = Xn −X0

n∆ = Xn −X0

tn − t0(2.6)

Equation 2.6 shows that if the average change of one variable over the interval tn − t0 shallbe calculated, it is enough to sample the counter at the beginning and at the end of the interval.

fs > 2B (2.7)

When sampling a time continuous signal into a time discreet signal the equation 2.7 statesthat the sampling frequency required to capture all information in the signal, fs is the samplingfrequency and B is the bandwidth of the signal. The bandwidth of a signal is the highestfrequency in the signal.

2.3.2.1 Normal distribution

Small sampling periods provide a great deal of data to be stored, although, it is true thatthe accuracy increases with the number of samples. To illustrate the relationship between thenumber of the samples and the accuracy of the measurement the following example is presentedwhich is partly described in [19]. If the stochastic variables X1, X2, ..., Xn are normally dis-tributed and independent, with standard deviation, σ, and expected value, µ, the estimationof the expected value, X, is in the interval with probability 1 - α.

P (−λα/2 <X − µσ/√n< λα/2) = 1− α (2.8)

2.3 Sampling theory 13

where X = 1n

∑nj=1 Xj . µ can then be described in the following way

µ = X ±λα/2σ√

n. (2.9)

The interval can be interpreted as a sampling error from the estimated expected value andthe error in percent, e%, is now introduced. Since an error is an absolute value and the intervalis symmetric, only one side has to be calculated.

X +λα/2σ√

n= X(1 + e%

100). (2.10)

The quotient between the standard deviation, σ, and the estimate expected value, X, canbe expressed as a constant C = σ

µ since the error is a percent of uncertainty in estimating µ,not the actual value. The number of samples can now be presented in terms of C, e% and λαand e% in term of the others:

n =(100λα/2C

e%

)2(2.11)

e% =100λα/2

√nC

n(2.12)

Equation 2.12 shows that the error increases when the ratio between standard deviation andthe expected value, C, increases. If the number of samples increases the error will decrease asshown in Figure 2.2.

Figure 2.2. The percentage error as a function of the number of samples

14 Frame of reference

For exponential distribution C = 1 since the standard deviation and the expected value isequal for such distribution. If that is the case Figure 2.2 shows that about 400 samples areneeded to get a estimation of the expected value giving an error of less than 10 percent with aconfidence of 95 percent. If the ratio between the standard deviation and the expected value,constant C, is larger, even more samples are needed to get an error less than 10 percent.

2.3.2.2 Binomial distribution

A binomial distribution is a discrete distribution that shows the probability of a number ofpositive outcomes from a number of independent attempts. The probability for each attemptis the same. The probability functions for a binomial distribution are:

P (k) =(nk)pk(1− p)n−k for k = 0,1,...,n. (2.13)

Where k is the number of positive outcomes, n is the number of attempts and p is theprobability for a positive outcome. The expected value of the distribution is np and the standarddeviation is

√np(1− p). When sampling a population that is binomially distributed, it is the

probability, p, that is unknown. The following example shows how the error of estimate p canbe calculated. Let X1, X2, ..., Xn be discrete independent stochastic variables that can be either0 or 1 with the probability p. The set of discrete variables will be binomially distributed asin function 2.13. An estimation of the probability is defined as p = 1

n

∑ni=1 Xi, which is also

an estimation of the expected value if the probability function is divided by with n and thestandard deviation then becomes p(1− p). The distribution can be approximated as a normaldistribution if np(1 − p) is greater than 10[7] and the estimated expected value, p, is in anequivalent interval as in 2.8:

P (−λα/2 <p− pσ/√n< λα/2) = 1− α (2.14)

which gives,

p+ λα/2d = p(1 + e). (2.15)

where, d =√p(1− p)/n. The variable e is the maximum error in the estimation of p with

a confidence of 1 - α.

e = λα/2√p(1− p)/n (2.16)

Function 2.16 shows the maximum error in estimating the probability, p, of a binomialdistribution. The error will be greatest when p = 0.5 since it gives the highest value of thefunction p(1 − p). When the number of samples increases, n, the error decreases. Table 2.2shows the error for different numbers of samples. The number of samples has to be almost 100before the error is less den 10 percentage points with a confidence of 95 percent.

Table 2.2. Error for different number of samples when p=0.5

n=5 n=10 n=100 n=500e (α = 0.05) 0.438 0.310 0.098 0.044e (α = 0.01) 0.576 0.407 0.129 0.058

2.4 Measurement process 15

2.4 Measurement processISO (the International Organization for Standardization) and IEC (the International Elec-trotechnical Commission) have specified an international Standard for the process of carryingout measurements in system and software engineering [14]. The standard identifies the activitiesand tasks required for implementing and improving measurements in a project or organization.The purpose of the measuring process is to make measurements that support effective manage-ment of a project and show the quality of a product.

2.4.1 MeasuresThree measurements are defined in the document.

• Base measure

• Derived Measure

• Indicator

The base measure is a quantity attribute of an entity that can be measured. The entitycan be a process, product, project or resource. For example a base measure can be the numberof worked hours, lines of code or defect products. A derived measure is a function of two ormore base measures. An indicator is a measure based on several derived measures and basemeasures. It gives an estimation or evaluation of the information that is needed for answeringthe question that initiated the measuring process. An indicator can for example be the averageproductivity in a project or the average quality in a product.

2.4.2 Activities in the measurement process2.4.2.1 Establish and sustain measurement commitment

In the first activity the scope of the measurements is identified. The scope can be just asingle project, a functional area or the whole organization. It is also important to identifyall stakeholders and that the purpose of the measurements is presented to them, since it isinformation that directly or indirectly can demonstrate their performance. Different areaswithin the measurement process shall be allocated resources. The number of people needed forthe areas differs and depends on the size and structure of the organization.

2.4.2.2 Plan the measurement process

The next activity to carry out is to identify the information need. The information need issomething that is important for the organization to know about and should be based on thegoals, risks, constrains and problems[14]. The kind of questions that are of interested can be:“what is the productivity in a project?”, “is the quality of a product sufficiently good ?” or“how do the employees experience the work environment?”. If several information needs areidentified, which is natural, they have to be prioritized and the most important shall be selected.It is often a good idea to involve the stakeholders in the process of selecting information need.

When the information need is identified, the measures shall be selected. All the potentiallyuseful measures can firstly be identified and from the resulting list a number of measures canthen be selected. Data collecting, analysis and reporting procedures need to be defined. Datacolleting includes when and how to collect data. The storage of data and the requirements fordata verification shall also be determined.

16 Frame of reference

2.4.2.3 Perform the measurement process

Firstly the methods for measuring attributes need to be implemented. There can be toolsthat more or less automate the collecting of data or report procedures where data is manu-ally collected. Often the most cost efficient way to implement data collecting is to slightlymodify current processes or reuse earlier work[14], i.e. to collect data according to previouslydetermined methods. The data shall be stored with other information needed for verifying,understanding and evaluating data. When enough data is collected it needs to be verified.The base measures are used for calculating the derived measures according to their definedfunctions. The derived measures are then put together into an indicator measurement. Theindicator shall be interpredted into a information product that meet the information need. Theindicators cannot be directly used to meet the information needs since they exist in a contextthat needs to be taken into consideration. The interpretation of the indicator should includethe stakeholders and it should result in an information product that meets the informationneed.

The information product shall be reviewed. When reviewing the information product it isimportant that the results are meaningful and that they enables improvements to be carriedout. It is often useful, with qualitative information, to interpret and understand the informationproduct. The result can then be communicated to the users. Feedback from the users andstakeholders should be collected and used to improve the information product.

2.4.2.4 Evaluate measurement

The last activity is to evaluate the information product and the measurement process andidentify potential improvements. The evaluation should be based on the base and derivedmeasures, the information product and the user feedback. The evaluation may lead to thatsome measures no longer are useful if they do not contribute to the current information need.

Improvements to the information product can include changing the time resolution. Poten-tial improvement in the measurement process is often a trade-off between higher cost in theprocess and higher quality in the product.

2.4.3 The measurement information modelTable 2.3 and Figure 2.3 present the measurement information model in [14]. Table 2.3 showsthe components needed for the model and Figure 2.3 the relationship between them. The infor-mation model can be of great help when planning, carrying out and evaluating measurements.

2.4 Measurement process 17

Attribute

Attribute

Base Measure

Base Measure

Measuring method

DerivedMeasure

DerivedMeasure

Measurement function

Indicator

(analysis) Model

Information product

Interpretetion

Entity

Information needs

Figure 2.3. Information model of the measurement process

18 Frame of reference

Table 2.3. Measurment information model and example [14]

Description ExamplesInformationNeeds

Insight necessary to man-age goals, risks and prob-lems.

Evaluating the efficiency in aproject, estimating the quality offuture projects or estimate thestatus of a project

Measurable Con-cept

An abstract relationshipbetween attributes ofentities and informationneeds.

Project performance, risk, matu-rity and quality etc.

Relevant Entities An object that has rele-vant attributes that canbe measured.

Products(e.g Source code, testcases, design documents), pro-cesses(e.g. design process,testing process), project andresources(e.g. programmers,tester, equipment)

Attributes A property or characteris-tic of an entity.

Code blocks, data counter, list offault in a project

Base Measures The measurement of anattribute.Defines methodfor carrying it out.

The total number of code lines,the amount of data sent overone interface, the total fault ina project

Measurementmethod

The definition of how anattribute is quantified intoa specific scale.

Count the number of lines in allcode blocks, read the value of thedata counter, count the numberof faults

Type of Mea-surementMethod

The definition of howthe quantification is per-formed.

Subjective (human decision is in-volved), objective (only logicaland numerical rules is used)

Scale Allowed values of the basemeasures.

Integer, discrete, continuous

Type of scale The relationship betweenthe values on the scale.

Nominal, ordinal, interval andratio

Unit of Measure-ment

The unit of the measure-ment.

Hours, meters

Derived Measure A function of two or sev-eral base measurements.

Code line Productivity

MeasurementFunction

Function for the derivedmeasurement.

Divide Lines of Code by Hours ofEffort

Indicator An indicator which is ameasurement that givesan estimation or evalu-ation of the informationneed.

Average productivity

Model The algorithm or calcula-tion that outputs the indi-cators with base measuresand derived measures asinputs.

Calculates the average mean andstandard deviation for all projectproductivity values

Decision Criteria The threshold where ac-tions should be taken if itis exceeded.

Indicator below a certain limitrequires further investigation

Chapter 3

GSM

Chapter IntroductionIn this chapter the basic components in a GSM network will be described. The reader will havethe possibility of getting an overall view of the components, the way they are used and howthey are connected to each other.

The Global System for Mobile Communications is a set of European Telecommunication Stan-dards Institute standards that define the system components and infrastructure for a cellularsystem. GSM is of today the world’s most used system for mobile communications. GSMservices are currently installed in over 218 countries and have approx. 3,450,410,548 users [5]providing a coverage to more than 80% of the world’s population [6].

GSM is an evolution of the first generation systems, e.g. Nordic Mobile Telephone andAdvanced Mobile Phone Service, which were analog mobile systems and provided a limited setof service. The second generation systems are digital and can therefore supply more services athigher capacity and quality.

One major issue regarding the 1G-systems was that there existed several national analogsystems which were not compatible with each other, making mobility between different countriesimpossible. Therefore an international standardization group was formed to avoid this situationfor the new 2G Public Land Mobile Network systems called Groupe Spéciale Mobile in 1982 atthe Conference of European Posts and Telegraphs. The introduction of a digital system alsogave rise to some other advantages like:

• Increased capacity compared to analog technology. This is achieved by betterutilization of the available radio frequencies.

• Quality of services and security. Better quality than the 1G-system and bettersecurity by enabling encryption of the networks traffic.

• Reduced cost for infrastructure and therefore cost per user. By standardizingand limiting the number of system components the costs will be reduced.

• New services. New features such as data transmission, SMS and fax were developed.

19

20 GSM

• Improved mobility between networks. To support international mobility the identifi-cation and numbering plans were based on ITU recommendations. Also the modificationsto the exiting Public Switched Telephone Network networks were minimal.

The worldwide GSM standard was developed for implementation in different frequency bands toprovided better access to the network. Therefore different substandard were formed: the GSM900 (GSM in the 900 MHz band) (GSM 800 in the US) and the DCS 1800 (Digital CellularSystem in the 1800 MHz band) (PCS 1900 in the US). The GSM 900 is used in Europe, Asiaand the Pacific Area and is designed to give good radio coverage even in the countryside outsidethe urban areas. To achieve better capacity the DCS 1800 standard is used in crowded areaswhich permits smaller cells and faster reuse of frequencies. DSC 1800 was renamed GSM 1800in 1997.

3.1 GSM specifications

An important feature of GSM is that it is platform-specific and does not specify any hardwarerequirements and therefore gives the designers the possibility of providing the actual function-ality [3]. Instead the standard specifies the different network functions, nodes and interfaces indetail. Therefore an operator gains the advantage of being able to buy equipment from differentvendors.

3.1.1 GSM Phases

When the GSM development started a decision was made to split the specification work intotwo parts. The reason for this was that the need for continuous development was anticipatedin the early stages, in order to get the products out to the market as soon as possible [28].

During 1990 the final specifications of GSM phase 1 were published by ETSI which becameresponsible for the standardization. This included over 6000 pages of documentation of GSMspecifications that defined the standard. The Phase I specifications included such services as:basic telephone calls with ciphering, data transfer, Short Message Service and other phoneservices, such as call forwarding and call barring. The SMS was first considered as an ”unnec-essary” feature but has, in later years, achieved great commercial success and is, of today, oneof the most used services in mobile communications.

The first GSM Phase I network was installed in 1991 in Radiolinjas network in Finlandwhere the first GSM call was made [9]. The commercial launch of GSM was a success aftersome initial problems with handsets, as with other launches of new mobile equipment, and by1993 GSM was installed in 36 networks in 22 countries [27].

During the deployment of the Phase 1 networks the work preceded with standardizationPhase 2 features. Some of the new features were developed as a reaction to experiences gainedduring the deployment of the first generation GSM. The new services were focused on supple-mentary features which will be described later.

When the GSM market grew, customer demand grew even more for new services. To managethis growing demand a new phase in GSM standardization started. This phase, called, Phase2+, included a new way of transmitting data in the network. The earlier data bearer serviceused the old way of delivering data in a telecom network, circuit switch, whereas the new phaseintroduced a packet switched method, General Packet Radio Service; more on this later.

3.2 GSM Network Architecture 21

3.1.2 Services in GSMThe GSM system was constructed to interconnect with other voice and data services integratedin other existing networks like Integrated Services Digital Network and PSTN. This, togetherwith the fact that the people who developed the system earlier were working with older telecomproducts has made that the basic concepts in GSM are derived from the ISDN standard. InGSM there have therefore been defined three types of service categories:

• Bearer services

• Tele services

• Supplementary services

The bearer services are a telecommunication service that gives the user the possibility totransmit signals at a certain capacity between the networks access points. GSM defines differentservice types for data transmission where the original GSM standard used a circuit switchedmethod allowing data rates of up to 9600 bits/second. We will later discuss the enhancementsthat have been carried out in later versions of GSM phases. The data services can be sub cat-egorized into two parts, transparent and non-transparent. The transparent mode will interfereas little as possible with the transmission and only forward error check. Non-transparent modealso adds flow control. GSM is primarily aimed for voice communication and the goal was todeliver high quality encrypted sound for security reasons. For this purpose the Teleservices areused to give the user the functions needed to communicate with any other user inside and out-side the network. The standard also includes other types of Teleservices such as an emergencynumber that could be used in the whole of Europe, SMS, Enhanced Messaging Service and aGroup 3 fax service.

Supplementary services are, as in ISDN, included in order to enhance the tele and bearer ser-vices. Examples of services are user identification, call waiting, call forwarding and multipartycalls.

3.2 GSM Network ArchitectureIn order to supply the services described above, the GSM PLMN system is divided into anumber of components that are designed to handle the different network functions. In order tomake the GSM system as standardized as possible the recommendations for GSM do not onlyspecify the air interface but also the infrastructure and its components. This gives the operatorflexibility by allowing them to integrate components from different vendors into their network.

Figure 3.1 illustrates the different components in GSM. The mobile station communicateswith the network through the radio interface to a cells antenna that is connected to a Base Sta-tion Subsystem. The BSS communicates with the Network and Switching Subsystem througha Mobile Switching Station. From the NSS the information is routed to other parts of the GSMnetwork and also to the non-GSM systems such as the PSTN. The NSS, like all other sub-systems, is also connected to the Operations Support Subsystem which includes the functionsneeded to manage and run the maintenance of the network.

The network architecture could be divided into three subnetworks; the Radio Access Net-work, the Core Network and the Management Network. In GSM standard these parts aredenoted as subsystems: the Radio Subsystem, the NSS and the OSS which hieratically dividesthe network.

22 GSM

BTS BTS BTS

BSC BSC

MSC/VLR

PSTN, ISDN

GMSC HLR

AbisAbisAbis

A A

MS MS MS

UmUm Um

EIR AUC OMC

O

OSS

NSS

BSS

IWF SMSC

Figure 3.1. GSM Network Architecture.

3.2.1 Radio SubsystemThe radio subsystem connects the network users with the core network where the informationcan be routed to its receiver. The RSS contains the Mobile Station and the BSS and is connectedthough the A interface to the NSS and to the OSS via the O interface. The A interface is astandardized circuit switched connection often based on the Pulse Code Modulation-30 with acapacity of 30 telephony calls at 64 bit/s connections each (total 2.048 Mbit/s). The O interfaceuses the Signaling System 7 protocol suite and has the main purpose of setup and tear downof calls, number translation, billing mechanisms and other related services.

3.2.2 Mobile StationThe mobile station component corresponds with all the devices, including the actual UserEquipment, but also software that is constructed to communicate with the network. The MSincludes two separate parts. Firstly the Mobile Equipment, which contains a Terminal Equip-ment which could be a PDA or a computer connected to the ME, and secondly a SubscriberIdentity Module. The SIM card is the user’s identity in the network and stores all informa-tion about the user that is needed for connecting and using the network. The ME also storesidentity information about the user through the International mobile subscriber identity seechapter 3.8.6. The ME, therefore, only stores information about the handset and about thoseservices that the hardware supports, the so-called class-mark. The SIM-card on the other handcontains information regarding available service and authentication of the user. For securitypurposes the SIM card contains a Personal Identification Number and a Personal UnblockingCode code that can protect the user against unwanted calls in the case of a theft. The MSalso stores a authentication key, Ki, that is used with a Authentication algorithm, A3, which isimplemented in the SIM for authenticating the ME when it is requesting resources. To identify

3.2 GSM Network Architecture 23

the MS a International Mobile Subscriber Identity number is stored and during the time thatthe MS is connected to the network the SIM also stores some temporary information such asthe cipher key Kc and a Temporary mobile subscriber identity and the Location Area Identitywhich are used to keep track of a mobile’s location in the network.

The main features of the MS are:

1. Radio transmission termination

2. Radio channel management

3. Speech encoding/decoding

4. Radio link error protection

5. Flow control of data

6. Mobility management

7. Performance measurements of radio link

A flow chart of the activities in the ME is shown in figure 3.2.2.

Transmitter Modulator

Burst formating

Chiphering

Interleaving

Channel Coding

Speech Coding

Segmentation

A/D Conversion

Reciever Demodulator

De-Chiphering

De-Interleaving

Speech Decoding

D/A Conversion

Microphone

Speeker

Equalization Adaptive

Figure 3.2. Flow chart of the activities in the ME.

3.2.3 Base Station SubsystemThe BSS contains two parts: the Base Station Transceiver Station and the Base Station Con-troller and is used to connect the MS to the NSS. The BTS contains antenna, amplifiers,filters and signal and protocol processing components to support the connectivity to the MS.Speech coding and decoding and rate adaptation due to radio link changes is carried out by thetranscoder/rate adapter unit called the Transcoder and Rate Adaptation Unit or Transcoderand Rate adapter Controller. The speech in GSM is encoded using 13 kbps (full rate) or 12.2

24 GSM

kbps (enhanced full rate) or in some rare cases 5.6 kbps. These rates are clearly different fromthe standard 64 kbps Pulse Code Modulation (PCM) and since the GSM network communicateswith the PSTN the TRAU is used to convert the GSM coded speech to 64 kbps or GSM codeddata transfer. The TRAU can be included in the BTS in the BSS but it is common to locatethe TRAU near the MSC in order to reduce the traffic load in the interface between the BSCand the MSC. The interface is then known as the Ater interface. The Ater interface is vendorspecific which implies that the BSS and TRAU must be supplied by the same manufacturer.

The BTS can be located in the center of a cell or in the edges between multiple cells togive coverage to more than one cell at a time using sectorized antennas. The connection to theMS is called the Um interface (ISDN U interface for mobile) and the connection to the BSCvia the Abis ISDN interface. One part of the Abis interface is not standardized in the GSMspecifications, the Operations and Maintenance Link. This link is very vendor specific due tothe internal design of the BTS being proprietary, causing low compatibility between differentmanufacturers.

In order to keep the BTS small, which helps deploying them in a crowded urban environ-ment, as much intelligence and control as possible is located in the BSC. The BSC has thereforeresponsibilities for reservations of radio resources, allocation and release, and Mobility Man-agment functions like traffic measurements, location management of the MS and handovermanagement. A BSC is most often connected to many BTS making it a centralized controlunit for a large geographical area.

3.2.4 Network and Switching Subsystem

The NSS is the core network connecting several BSC with each other and the PSTN. TheNSS is also responsible for handover between different BSC and includes functions for theinternational roaming of a user. The subsystem also stores subscriber information on availableservices, charging and accounting.

3.2.5 Mobile Switching Center (Mobile Services Switching Center)

Several BSCs are connected to the Mobile Switching Center which forms the backbone in thenetwork. The MSC includes functions for path searching, data forwarding and service featureprocessing. Many of the MSC functions are similar to an ordinary telecommunication switchsuch as ISDN but since the users are mobile the MSC includes more functionality for radioresource reservations and the mobile management, handovers and location registration. Aspecial type of MSC is the Gateway MSCis used to connect the core network to other networkssuch as a PSTN. In the GMSC Internetworking Functions are used to connect the network topublic data networks (PDN) often using the X.25 transmission. IWF also contains functions tohandle services for delivering fax messages. The IWF therefore conducts protocol adaptationsand rate conversions to make communication between services in the ME and services in differentnetworks possible. The GMSC is also the first node that an external service reaches and istherefore responsible for locating the mobile user in the network through the HLR.

The MSC also contains information that is used for charging the customer for the serviceshe has used. All the current charging rates are stored in the MSC and this is applied to acurrent call and used at the networks billing center.

3.3 GSM Areas 25

3.2.6 SMSCThe Short Message Service Center is the node in GSM which is responsible for storing andforwarding SMS between two MS. When a SMS is sent from a MS the SMS is first routed tothe SMSC service center which stores the message. When the service center has stored themessage the SMS MSC tries to deliver the message to a MS within the network through theMSC or to a different MS located in a different network. In the latter case the message isdelivered to a SMS interworking MSC located in the foreign network that is responsible forstoring and forwarding the SMS to the receiving MS.

3.3 GSM AreasThe GSM network structure can be divided into separate geographical areas; cell, locationarea, MSC/VLR Area and glsPLMN. The structures of these areas are an important issue inall cellular networks because the users are mobile and they are used to monitor their movements.

• Cell The cells are the smallest geographical units of the network. This area is covered bya BTS and is assigned a unique Cell Global Identity (CGI) which identifies the cells fromeach other.

• Location Area The Location Area is a group of cells and defines an area where thesubscriber is located and is identified by Location Area Identity. The LA is introducedin a GSM network to reduce the signaling load of updating and finding a subscriber’slocation within the network. Each time a user changes from one cell to another the MEchecks if the new cell belongs to a different LA. If the cell belongs to a new LA the user’snew position is updated to the network. If a user is called, a paging request is broadcastedto all the cells in the location area.

• MSC/VLR service area The MSC/VLR service area is a geographical area that con-sists of several LA that are connected to one MSC. To route an incoming call to the MEthe users current MSC must be stored and retrieved.

• PLMN The PLMN service area is the operators’ entire network cells and is defined as thetotal area of where the operator has coverage and is therefore the highest geographical areain the GSM network architecture. The term international roaming is used for describingwhen a user changes PLMN.

3.4 Databases and RegistersSeveral databases are defined in the GSM standard for management of the networks users andtheir location; the Home-Location-Register and Visitor-Location-Register. The HLR and VLRare queried by the networks nodes to retrieve information about registration and localization.

3.4.1 Home-location-register (HLR)The Home-Location-Register is a common node and often considered one of the most impor-tant nodes in a mobile network. The HLR contains information of all the subscribers of thenetwork services, including information on each user’s subscribed services, restricted servicesand telephone numbers, the Mobile subscriber ISDN number and the PSTN associated number

26 GSM

and authentication data. Stored in the HLR is also temporary information like the MobileSubscriber Roaming Number, Local Mobile Subscriber Identity and current MSC and VLR theME is connected to, if it is available. By using this information it is possible to easily route anincoming service to the MEs closest MSC and BSC, more on this in section 3.8. The HLR isalso responsible for all the users in networks traffic information that is later used for accountingand charging.

3.4.2 Visitor-location-register (VLR)The Visitor-Location-Register is a database connected to the NSS and is used to speed-up theretrieval of information needed by the MSC and BSC. When a MS enters a new MSC/VLRservice area the permanent information about the subscriber and MS is copied from the HLRand therefore avoids frequent access to the networks HLR. The VLR also stores some owntemporary data like LAI and the TMSI. To further shorten the access time a VLR is oftencollocated with a MSC.

3.5 Operations Support SubsystemSince all mobile networks are often very complex and contain many entities, GSM included, it ishard to manage each of these entities individually. In GSM the Operations Support Subsystemhas been implemented to give the functions to operate, conduct maintenance on the networkand ensure security in a centralized manner. The OSS has a connection to all the elements inthe GSM network and contains the nodes Authentication Center, Equipment Identity Registerand Operations and Maintenance Center.

3.5.1 Operations And Maintenance CenterThe Operations and Maintenance Center is the management and control part of the OSS.The OMC implemented on the Telecommunications Management Network to ensure the GSMnetwork design philosophy is compatible with the fixed wired network

3.5.1.1 Telecommunications management network

The Telecommunications Management Network concept was defined by the International Telecom-munications Union. This concept describes the TMN as a separate network the interfaces witha telecommunications network at multiple access points. The basic requirements for the TMNthat were stated by the ITU are [3]:

• Centralized

• Separated from the telecommunications network

• Connected to the nodes in the telecommunications network via standardized interfaces

TMN contains five basic elements (blocks) that are defined by the TMNs functional model[29].

Network Element. The NE are the actual nodes in the network, HLR, VLR, BSC etc. inGSM. The functions that the NEs performs are called network element functions and are divided

3.5 Operations Support Subsystem 27

into two separate groups, primary and management functions. The primary functions are thetelecommunication functions and the management functions described by TMN.

Operations System. All the management in TMN is handled by OS through the OS function.Separate OS functions cover the functions like billing, management, measurements. The OS ismost often a part of a OMC and if the network is large the network may have several OMCs.

Workstation. The WS acts as an interface to where the operator can communicate with theTMN through WS functions. Through this interface the status and maintenance functions areavailable.

Meditation device The MD acts as a bridge between the OS and the nodes’ various NE. Thecommunication is delivered through standardized interfaces.

Q-Adapter The QA is used to communicate with other non TMN compatible nodes. The QAis therefore used as a translator between these nodes and the TMN. The TMN also includes adata communications network between the NEs, OSs and other elements using a WAN or LANconnections. In GSM the OMC often uses a X.25 connection to the MSC and BSC [29].

The TMN elements are shown in figure 3.3. Many of the connections and functions of the blocksare not a part of TMN and are therefore placed in the border between the TMN and the restof the telecommunications network.

OSF WSF

MD

QAF NEF

TO OTHER TMNs

CONNECTION TO NON TMN NODES

OPERATOR INTERFACE

TMN

Figure 3.3. TMN elements and the connections in the TMN model.

To simplify the hierarchy of the TMN the functions has been divided into layers, like theOpen Systems Interconnect model, see table 3.1.

See GSM System Survey [3] for more information about OSS, particularly Ericsson’s OSSimplementation.

3.5.2 Authentication CenterAll authentication and encryption parameters in the GSM network are handled by the Authenti-cation Center. These parameters help the network to avoid fraud and make the communications

28 GSM

Table 3.1. Logical layers in TMN

Business Management Contains functions used to manage the business aspects of thenetwork, billing and accounting.

Service Management Provides functions to handle the services in the network: def-inition, administration and charging of services.

Network Management Has a view of the entire network. Is used to configure routes,monitor link utilizations and other performance metrics.

Element ManagementThe lowest level where the actual nodes are covered. Includesfunctions for alarm monitoring, backup, logging and mainte-nance of the software and hardware.

between the subscribers as confidential as possible. When the MS registers to the network theSIM card information it is used to create a shared secret that is authenticated by the AuC.After the authentication has passed encryption parameters are passed to the MS. The AuC isoften a part of the HLR since their tight integration.

3.5.3 EIRThe Equipment Identity Register is used to store information about all IMEIs of the MS thatare or have been registered to the network. This information is used to block connections toMS that have been reported as stolen or have some kind of hardware or software defect. Sincemany of these defects can affect the networks performance, by for example transmitting toolong sequences and therefore cause interference with other transmissions, they must be blocked.Since the IMEI contains not only serial number information but also the make and model theHLR block all ME constitute this black list.

3.6 Radio interfaceThe radio interface, the physical layer (OSI Layer I) in GSM, the Um, uses a Space divisionmultiple access scheme with cells that reuse the available frequencies, which maximizes thecapacity and performance of the network. The uplink and the downlink in the cells are separatedusing Frequency division duplex with one frequency band for communication from the MS tothe BTS, i.e. uplink, and one band for communications between the BTS and the MS, i.e.downlink. To separate the users, a Time Division Multiple Access scheme in combination withFrequency division multiple access is used, which allows multiple users to share the commonRF channel in both a timesharing and frequency sharing scheme. Finally a Gaussian MinimumShift Keying is used for modulating the digital transmission to the air interface which gives agross data rate of transmission rate of 270.83 kbit/s per carrier frequency [9].

The available frequencies in GSM 900 are divided into 124 full duplex pairs with 200 KHzcarriers and a 200 KHz guard frequency to avoid interference. In GSM 1800 more channels areallocated, 374 carriers, to increase the capacity of the network. To preserve MS the power theuplink, channels are always placed in lower frequency bands (45 MHz lower in GSM 900 and 90MHz in GSM 1800) due to the fact that less energy is needed to transmit in lower frequencies.To lower the power needed an even more discontinuous transmission mode is used to make itpossible to not transmit any signal when no data is in the processes of being transmitted [29].

3.6 Radio interface 29

In GSM each cell is given a range of RF channels, implying both uplink and downlink,usually one to three but it could be more if the cell has a high demand. The cells are thendivided into sectors where one BTS is responsible for several cells. The carriers are furtherdivided into TDMA-frames consisting of eight so called time slots, numbered 0 to 7. Each timeslot provides a separate Time-division multiplexing channel available for a MS to use. Eachof these time slots occupies the medium for 576.9 µs giving a total frame length of 4.615 ms.To prevent the MS transmitting and receiving at the same time the uplink is delayed threetimeslots. Though, when long transmitting delays occurs, due to long distances between theMS and the BTS, a timing advance factor is introduced to assure that the uplink informationreaches the BTS at the exact instance of time. GSM supports a timing advance factor of 63bits (23 ms) resulting in a maximum distance of 35 km between the BTS and the MS. This canbe extended by transmitting a full time slot plus the timing advance value earlier [26]. Thistiming advance factor is calculated in the BTS by doing measurements of the traffic from theMS. To avoid the need of a full duplex MS the uplink time slot numbering is delayed threepositions from downlink.

During one time slot, data is transmitted in radio bursts. GSM defines five different typesof burst in two categories, using the full duration and or a shorter duration of the time slot.The short duration burst, the Access Burst uses a longer guard sequence and is used for theinitial setup between Random Access channel described later.

The full duration normal burst is for transmitting information for the traffic and controlchannels. The burst has the length of 148 bits with 2*57 bits of actual data and 26 bits oftraining information which is used to for example to calculate the timing advance value andtraffic measurements, see Figure 3.4.

1 3 1 57 3 8.25 57

Tail Data Flag Flag Data Tail Guard

Burst (148 bits 0.546 ms)

Time slot (156.25 bits 0.577 msec)

26

Traning

Figure 3.4. GSM normal burst structure.

The tail bits are all set to zero and are used to increase the performance of the receiver byramping the transmission power up or down. The (stealing) flags indicate if prior data portionis carrying user data or signaling information. The training data is a predefined bit pattern inthe middle of the burst and helps the receiving part to adapt to current conditions of physicalmedium, multipath propagation, fast fading etc. The four other full definition burst types areas follows: frequency correction burst allows fine tuning of carrier frequency, synchronous burstallows exact time synchronization between the MS and the BTS, dummy burst is used whenno data is to be transmitted and supports power measurements for quality monitoring.

The GSM standard also defines an optional slow frequency hopping sequence where the MSand BTS can negotiate to change carrier frequency by a predefined sequence.

3.6.1 Logical ChannelsIn GSM the physical channels are divided into two separate classes of logical channels, TrafficChannel and Control Channel, depending on which type of information that is delivered.

30 GSM

3.6.1.1 Traffic Channels

The Traffic Channel is used to carry user information (speech, data etc.) as either circuit-switched or packet-switched. GSM specifies to categories, full-rate TCH (TCH/F) and half-rateTCH (TCH/H). TCH/F gives 22.8 kbit/s of raw data rate and depending of which codec isused to encode the user’s voice, the actual data rate varies, e.g. from the original specification,13kbit/s of speech in data and the rest for error correction data. The TCH/H enables thepossibility for two connections to share a timeslot and therefore double the amount of calls itis possible to connect, but at a lower quality. The latest technology VAMOS extents capacityeven further by allowing four MS to transmit in one timeslot using the TCH/H principle.

3.6.1.2 Control Channels

To support the communication of data in GSM air-interface a set of Control Channel aredefined. The CCH control tasks such as: medium access, allocation of traffic channels andmobility. GSM defines three sets of control channels:

• Common control channels The CCCH handles the initial connection setup events.

– Paging channel. The PCH is used to inform a MS of an incoming call or service.Downlink.

– RACH. The RACH is used by the MS to request resources. A slotted aloha multipleaccess scheme is used to allow all MS in the cell to communicate with the BSC.Uplink.

– Access Grant Channel. AGCH is used by the BSC to grant the request thatthe MS requested on the RACH. The grant permits the MS to use a TCH or aStandalone dedicated control channel, which will be described later. Downlink.

– Notification channel. NCH is used for group-call and voice broadcast services.

• Dedicated Control Channels. The DCCH is primarily used for the MS to connectwith the network. The DCCH are bidirectional and are therefore used by both the MSand the BSC.

– Standalone dedicated control channel. The SDCCH is used for communicationbetween the MS and the BSC when the a TCH is not used. This occurs for examplewhen the MS send SMS and to authenticate and register to a TCH setup.

– Slow associated control control channel. Each TCH and SDCCH has an as-sociated SACCH. This is used to carry system information like channel quality andsignal power level messages between the BTS and the MS. These messages are usedto determine when a handover between two BTS should be initiated. The SACCHis also used for SMS when a call is connected.

– Fast associated control control channel. When more signaling information isneeded a FACCH is used. This occurs when time critical information like handoverinformation is exchange and when non-voice data has to be delivered at a higher datarate. The FACCH uses capacity reserved for TCH and therefore steels bandwidthfrom the user data [29].

– Cell broadcast channel. Cell broadcast messages is sent in the same time slot asthe SDCCH through the CBCH. Only downlink.

3.6 Radio interface 31

• Broadcast Control Channel is used to carry broadcast information between the BTSand the MSs in the cell. This information includes the cell identification number, thefrequencies that are available inside and the neighboring cells and special cell optionssuch as when frequency hopping is used.

– Frequency control channel. The FCCH is used to send frequency corrections tothe MS

– BCCH. The general information to access the cell resources are carried on theBCCH. It is also used to carry the configuration of the CCCH.

– Synchroization channel. The SCH is used to carry time (frame) correction infor-mation but also contains the Base-Station Identity Code.

3.6.1.3 GSM Mapping

The GSM standard defines a frame structure which enables the different channel types tobe transferred on the physical medium. The frame structure enables the system to schedule(multiplex) the use of the timeslots described in section 3.6. The time based multiplexing canallow the mapping to occupy the complete physical medium or as a part of it. The framestructure gives priority to the data of different TCHs and associates these with a dedicatedTDMA-channel whereas the signaling channels have to share the use of one channel. Of theeight available timeslots in a cell the frame numbered 0 is used to carry signaling informationand the remaining seven (numbered 1-7) are mapped to carry one (or two if half-rate is used)MS. If several frequency carriers are available within a cell the logical control channel is mappedto the Beacon frequency (the first available frequency in the cell) in time slot 0 and if needed 2, 4and 6. The mapping procedure in GSM uses a complex hieratical structure defining multiframe,superframe and hyperframes, see Figure 3.5 on top of the basic frames.

Frame

hyperframe = 2,047 superframes (3 h 28 min 53.76 s)

superframe = 51 traffic superframe or 26 control channels (6.12 s)

traffic multiframe = 26frames (120 ms)

control multiframe = 51 frames (235.4 ms)

0 1 2 4 5 6 7

4.616 ms 0.577ms Burst

3

Figure 3.5. GSM frame structure.

The basic frames are grouped to form so called multiframes which can consist of eithertraffic or control information:

• Traffic multiframe. The TCH are ordered into multiframes consisting of 26 normalbursts. Of the 26 available bursts 24 are used for actual TCH, one for the associatedSACCH and one idle frame, see Figure 3.6. The sequence illustrated is repeated in the

32 GSM

time slot that is assigned to the MS, i.e. the slot marked 1, sending one of the 26 frames ineach TDMA channel. This makes the total length of the traffic multiframe (26*4.615 ms)= 120ms. The idle period introduced at the end of the multiframe is often used by theMS to scan other neighboring cells for handover purposes. If FACCH is used half of thedata portion of eight consecutive bursts is used, indicated by the Stealing flags describedabove.

• Control multiframe. The control multiframe is used to carry the control signalingchannels BCCH, CCCH and the SDCCH. The multiframe consists of 51 bursts and istherefore repeated each 235.4 ms.

26 frames = 120 ms T = Traffic channel

A = Associated Control I = Idle

T T T T T T T T T T T T A T T T T T T T T T T T T I

Figure 3.6. The traffic multiframe used in GSM.

Above the multiframe a superfame is defined which takes up 6.12 s. The superframe consistsof either 51*26 multiframes or 26*51 depending on which type of multiframe is used giving atotal of 2048 frames. The superframe gives the MS the possibility to at least scan the alldifferent frame types once.

3.7 Protocols in GSMGSM uses a layered protocol structure for the peer-to-peer communication between the differentnodes. The signaling protocol stack is used for handling the mobility, radio resource andconnection management functions needed for the different functions in the network to work.The four different stacks for the nodes in the BSS and NSS is shown in Figure 3.7.

The protocol stack is divided into three interface dependent layers; Layer 1 (Physical), Layer2 (Data Link) and Layer 3. The functions in Layer 3 has no direct similarity in the OSI modelsince it includes functions from several layers and can better be described as a messaging layer[21].

The physical layer over the Um (between the MS and the BTS) handles all radio relatedactivities like creating bursts, multiplexing through TDMA frames, synchronization of the trans-mission, encryption and management of channels described in section 3.6. The layer also takescare of channel coding and error detection/correction. The channel coding uses a Forward ErrorCorrection with a high level of redundant information to assure a error free connection betweenthe MS and BTS.

The data link layer between the MS and BTS interface uses a protocol named LAPDm.This protocol is a striped down version of the Link Access Protocol protocol which is usedfor link access the ISDN D-channel. The LAPDm provides the functions for flow control anddelivering frames in the right order but does not provide error correction since this is coveredby the physical layer.

Layer 3 consists of three sublayers; Radio Resource Managment, Mobility Managment andConnection Management.

The RR sublayer is implemented in the BSS to establish a link between the MS and theMSC. The layer covers the functions needed for the physical connection, management and tear

3.8 Addressing and localization in GSM 33

SS7 SS7

PCM PCM PCMPCMRF RF

LAPD LAPD LAPDLAPDm m

RR

RR

MM

CM

MM

CM

BSSMAPBSSMAP

BTSMBTSM

RR

Figure 3.7. GSM signaling protcol structure.

down. The MM sublayer is implemented on top of the RR sublayer between the MS and theMSC to support registration, authentication and other mobility related functions.

The CM sublayer is responsible for sending SMS over a SDCCH and SACCH and thesupplementary services provided by GSM. CM also provides functions for call establishment,selection of type of service and call release.

At the BTS the RR sublayer is changed to the Base Transceiver Station Management. Themain purpose of the RR at this point is to allocate and reallocate traffic channels, initial access,paging and other radio resource and mobility management functions.

From the BSC to the MSC the signaling protocol is changed to the SS7 signaling systemthat is used in the rest of the NSS. Here the RR functions are controlled through the BaseTransceiver Station Managment Application Part.

3.8 Addressing and localization in GSMAs in other wireless communication systems it is crucial to be able to locate the different nodesin the system. This is even more important in a global system like GSM where the user is notonly connected in the operators own network but also in other operators networks. Thereforethe GSM defines a set of addresses that are used for locating not only the MS but also the users.The identity information that is stored about the user is, as mentioned earlier, stored in theSIM-card and the ME stores identifiers of the equipment that is used. This makes it possible todevelop further service to make the user independent of the accessibility or type of connection,mobile or fixed, and instead route the call to the best service that the user is connected to [9].

The worldwide localization in GSM is made possible by periodically performing a LocationArea Update even if the equipment is not used. This LA update is processed and stored inthe HLR. As mentioned earlier, a LAU is also preformed when the MS is connected to a BSC

34 GSM

that belongs to a different VLR than before. Another term for the change of VLR roaming andGSM supports three different types: within the network (LAU), national roaming (often notsupported due to regulations from the operators), and international roaming.

3.8.1 International Mobile Subscriber Identity (IMSI)The IMEI is used to identify the subscriber of the network. IMSI uses a 15 decimal digitnumber which consists of mobile country code (MCC), mobile network code (MNC) and amobile subscriber identification number (MSIN).

3.8.2 Temporary mobile subscriber identity (TMSI)To strengthen the security in GSM the system supports the use of a TMSI to avoid using theIMSI which could identify the user. The VLR assigns a 4*8 bit TMSI number which the MSstores on the SIM-car. The TMSI is only valid within the a cell and therefore the TMSI togetherwith a LA allows the network to uniquely identify the MS.

3.8.3 Local Mobile Subscriber Identity (LMSI)The LSMI is used as an alternative searching key to gain faster database access times. The MSis assigned a new LMSI when it enters a new MSC/VLR service area that is stored in the HLR.

3.8.4 Mobile Station (or Subscriber) ISDN Number (MSISDN)The MSISDN number is the user’s phone number and is stored in the MS SIM-card. TheMSISDN follows the ITU-T standard with a country code, national destination code, and thesubscriber number. The use of the MSISDN also gives some extra security by hiding the IMSI.

3.8.5 The Mobile Station Roaming Number (MSRN)The MSRN is a temporary ISDN number which is location dependent and is used to hide theidentity and location of a user. The VLR assigns the MSRN and is passed to the MSC whenit is needed. The MSRN has the same structure as the MSISDN and is generated so that therouting to the MSC responsible for the MS can easily be determined.

3.8.6 International mobile station equipment identity (IMEI)The IMSEI international identifies a MS. The number contains information about the manufac-turer and the date when the unit was manufactured. The network also stores information aboutthe ME through the IMEI i.e. when a unit is reported stolen or other ways black listed fromthe network. The operator can also be notified if a user uses obsolete hardware that should beexchanged.

3.9 Data services3.9.1 GPRSGPRS brings packet switched services to GSM networks. Packet switch access is suitable forburst traffic that is common in many services at internet today, for example browsing the web.

3.9 Data services 35

In the case of a circuit switch access is used the channel utilization would be very low. InGPRS a varying number of timeslots can be used for both downlink and uplink. There is atotal of 8 timeslots that can be used and the channel coding per timeslot is 9.05, 13.4, 15.5or 21.4 kbit/s depending on the radio link conditions [26]. In GPRS there are three classes ofMSs for simultaneous use of both packet switched and circuit switched services. Class A fullysupports the use of packet switched services and circuit switched services simultaneous. ClassB supports the use of both services but not at the same time and a MS of class C can onlysupport one type of service[9].

BTS

BSC

MSC/VLR

Internet

GGSN

HLR

MS

EIR

SGSN

BTS

MS

GGSN

Gb

Gn Other GPRS NetworksGp

Gi

Gf

Gs

Gr

Gc

Figure 3.8. Architecture of a GPRS system

3.9.1.1 SGSN

The Serving GPRS Support Node is responsible for delivering packets from the Gateway GPRSSupport Node to the BSC, which forwards the packets to the BTS which sends them over theair interface to the MS. One SGSN handles a set of nodes and keeps track of the location ofthe MS. The current SGSN to which a MS belongs is stored in the HLR, which receives thisinformation over the Gr interface. The SGSN is also responsible for attach/detach of MSs andtheir authentication and logical link management [9]. For authentication the SGSN can queryEIR the IMEI to make sure that the MS is allowed to be registered to the network. For thecircuit switched services in GSM a location management already exists. It can be reused inGPRS, for example location updates for GSM and GPRS can be combined. In these cases theGs interface is used for communication between the SGSN and the MSC/VLR.

3.9.1.2 GGSN

The GGSN routes data packets out from the mobile network to Internet and other packetswitched data networks. The node converts the network address, ip address at Internet, intoa GSM address for incoming packets[9]. The packets are routed in the mobile network to the

36 GSM

SGSN responsible for the service area where the MS is located. The interface between theSGSN and GGSN is called the Gn interface if the two nodes are in the same PLMN and Gpif they are in separated PLMN. When the GGSN needs to know the location of a MS, it canask HLR about which SGSN the user belongs to. This is carried out over the logical interfacecalled Gc, as shown in Figure 3.8.

3.9.1.3 Location managemnet

For packet access the MS can be in three states:

• IDLE

• READY

• STANDBY

When the MS is in state IDLE its location is unknown to the GPRS-network. There hasto be one attachment to the network initiated by the MS before the user transfers to READYstate. The MS can perform a GPRS detach procedure which takes it back to state IDLE. Instate READY the transmission of data, both uplink and downlink, is carried out and the MSmakes an update about its position for every cell movement. If no transmissions have occurredfor some time, a timer expires and the MS is enters STANDBY state. In this state the MSsends an update about its position every time it changes Routing Area[9]. A RA is a subsetof cells within one LAs in GSM. The RA is then paged when the cell location is needed andthe MS switches to state READY as soon as it starts sending or receiving packets again. If notransmission takes place in STANDBY state a STANDBY timer will expire and the MS willstart over in IDLE state.

One problem with this solution of location management in GPRS is that the attach processcan only be initiated by the MS. This means that push services can only be used when the MSis in READY and STANDBY state. When a MS is in STANDBY state it will send the locationupdates every time it changes Routing Area, which will consume battery power from the MSand radio resources.

3.9.2 EDGEThe improvements made in GPRS were, as presented in the previous section, the possibilityof using several timelost for one user. In Enhanced Data Rates for GSM Evolution futherimprovements were carried out, by allowing a dynamic modulation which improves the datarate. The main change in EDGE is in the air interface where MS with, good radio conditions,can use a higher modulation scheme. In GPRS and GSM the modulation is Gaussian minimum-shift keying which has one bit per symbol. In EDGE the MS can switch to 8-PSK that hasthree bits per symbol [9]. The bit rate is up to 59.2 kbit/s for one timeslot compared to GPRS,that could have up to 21.4 kbit/s for one timeslot The data rate is three times higher withEDGE.

In EDGE more improvements are introduced in the air interface. Hybrid automatic repeatrequest is used which makes retransmissions more efficient. Instead of retransmitting the wholepacket, more redundant data is sent, which increases the chances for the receiver to decode themessage correctly. However there are a few effects on the GSM system architecture when EDGEis introduced. The interface between BTS and BSC, called Abis, only supports 16 kbit/s pertraffic channel [9]. Since edge supports higher data rates, several traffic channels in the Abisinterface are allocated for one EDGE channel.

Chapter 4

GSM Evolutions

Chapter IntroductionThis thesis is mainly focused on GSM networks, although this Chapter presents the latest tech-niques for mobile communication developed at Ericsson. The standardized techniques WCDMAand LTE are described from a general perspective.

4.1 WCDMAAfter GSM the next generation of mobile network is the third generation, mobile network 3G.It is generally called UMTS (Universal Mobile Telecommunications System) and the first andmost widespread one is the release99, which was standardized by the 3GPP [16]. WCDMA is adevelopment of GSM and supports, in the release99, packet switch data rates of 0.384 mbit/s.After release99 the development of WCDMA has continued and HSPA (High Speed PacketAccess) is one extension of WCDMA where speeds of up to 14 mbit/s are supported in the firstphase [12].

4.1.1 System and network architecture of WCDMAIn WCDMA there is a separation of function between the RAN (Radio Access Network) andthe CN (Core Network). The mobility management is hidden from the CN in the RAN andthe different types of Radio Access Networks, with the same separation, can be connected tothe Core Network. This allows a combined CN for both a GSM and a WCDMA RAN network.Many operators already have a GSM network and an investment in WCDMA is therefore lowersince the two networks can share the same CN. Handovers between GSM and WCDMA are alsosupported, which provides better total coverage for the end user because the networks mightcover different areas. Since WCDMA and GSM shares the same Core Network, the differencein nodes is in the Radio Access Network. Figure 4.1 shows the logical network of WCDMA anda description of the nodes and interfaces that differ from GSM follows.

37

38 GSM Evolutions

NodeB NodeB NodeB

Internet

RNC RNC

MSC/VLR

SGSN

PSTN

GMSC GGSN

HSS

Gi

Iub Iub Iub

Iur

Iu_cs

Iu_cs

Iu_ps

Iu_ps

Figure 4.1. Architecture of a WCDMA Network

Node B is the logical node in WCDMA that correspond to the BTS in GSM. The name’Node B’ was a temporary name during the process of standardizing WCDMA but was neverchanged [16]. It is responsible for coding, interleaving, modulation and other physical layerfunction. Radio resource functions, such as power control, are also performed in NodeB.RNC (Radio Network Controller) is responsible for several NodeB’s and is connected to thecore network and other RNC. A RNC is one anchor point in the network, which means that itis a fixed point in the network from point of view of the core network, even though the userswitches cells. If the user moves to a cell controlled by a NodeB that belongs to another RNCa concept of serving and drift RNC is introduced in order to maintain the original RNC as ananchor. The first RNC remains the serving RNC even if the user moves to a NodeB controlledby another RNC, which becomes drift RNC.HLR stores the information about the subscriber in a database. It contains information aboutwhich services that are allowed for the user and status about services such as call forwarding andcall waiting. It also stores information about where the user is located, e. g. which MSC/VLRor SGSN.Uu interface is the wireless radio connection between the UE and NodeB. In this thesis therewill be no focus on the wireless part. However a lot of research on mobile communication isbeing carried out within this area. In WCDAM the multiple access is achieved by spreadingthe signal into a wide signal of 5 MHz and separating the users by adding a unique code foreach user.Iu is the interface between the UTRAN with the CN. It has one interface for circuit switched(CN) traffic and one for packet switched (PS) traffic. In this interface information betweenthe RAN and the CN is exchanged for many functions. Some of these functions where the Iu

4.2 LTE 39

interface is used are paging, hard handover and location reporting. By having standardizedinterfaces UTRAN and the CN from different manufactures can function together.Iub interface connects NodeB with an RNC. In GSM this is not a standardized interface and inLTE this interface does not exist, since NodeB and RNC are combined to one logical node. Bystandardize this interface the possibility for manufactures to specify on creating NodeB opens.Iur interface is used when two RNC need to communicate with each other. The interface wasmainly intended to be used for soft handovers. It can also be used for exchange information forGlobal Resource Management.

4.2 LTEAfter WCDMA the LTE (Long Term Evolution) is a development towards the fourth generationmobile communication system. The operators call this technology 4G, however Ericsson, whichis a leading developer in this field, claim that LTE is a step towards 4G. It is likely that Ericssonwill call LTE advanced the "real" 4G. In LTE the allocated spectrum can vary from 5 MHz upto 20 MHz. It uses OFDMA (Orthogonal Frequency Division Multiple Access) in the downlinkand has a flat IP-based structure [12]. Other targets, when designing LTE, were low round-triptime, high mobility and high capacity. The peak data rate in LTE is from 100 mbit/s withoutMIMO and 326.4 mbit/s with 4x4 MIMO in the downlink [25].

4.2.1 System and network architecture of LTE/SAEThe system architecture of LTE is different to that of WCDMA, shown in Figure 4.2. Ithas fewer nodes and supports only IP-traffic (Packet Switched). LTE does not have the samesplitting of functions between RAN and CN that there is in WCDMA. One reason for this is thefact that a node corresponding to the RNC does not exist in LTE. Instead most of the functionshandled by the RNC are moved to the NodeB that are now called eNodeB in LTE. Withoutan RNC node, the anchor point is moved to the Core Network that now has to handle mobilityin the network. In LTE there are no macrodiversity requirements in the RAN. Macrodiversitymeans that a signal from several transmitting antennas is combined to an improved signal. Thisis used in WCDMA, for example, in soft handovers between two cells and controlled by theRNC.

eNodeB The eNodeB inherits the functionality from the NodeB and most of the function-ality of RNC from the architecture of WCDMA. An eNodeB handles several cells and performsradio link functions like modulation, demodulation, interleaving etc. [12]. In addition to thisthe eNodeB is in charge of radio resource functions, which are handover decisions and schedulin-gof the shared medium among users.EPC In LTE the Core Network is dramatically changed compared to GSM/WCDMA. Thenew CN was called Evolved Packet Core (EPC) and the work of designing it is called SystemArchitecture Evolution (SAE) [12]. The objectives of SAE is to have a packet switched domain(IP-based) and to minimize the number of nodes. The Core Network in LTE is reduced toone logic node called EPC and one additional node, HSS, which corresponds to the HLR inGSM/WCDMA. The EPC has to handle mobility and acts as the anchor, e.g. the fixed pointfrom a view outside the network. When a handover occurs the EPC has to know which is thenew eNodeB and start forwarding packets there. It has to a logical connection between an EPCand all the eNodeB’s in the Network.HSS Home Subscriber Server (HSS) has a similar function as does the HLR in GSM/WCDMA

40 GSM Evolutions

eNodeB eNodeB eNodeB

Internet

EPC (Evoloved

Packet Core) HSS

S6

S1

S1 S1

X2 X2

SGi

Figure 4.2. Architecture of a LTE system

and is connected to EPC through an interface called S6.X2 This interface connects the eNodeB with each other. The interface is mainly used foractive-mode mobility [12]. When a handover takes place the eNodeB uses this interface tocommunicate with the neighboring cells that belong to another eNodeB.S1 The connection between the eNodeB and EPC has the interface called S1. This interfacecorresponds to the Iu packet switched interface in GPRS/WCDMA.

Chapter 5

Current Solutions

Chapter IntroductionThe topic of measuring the utilization of the test equipment have been explored earlier atEricsson. This Chapter presents the previously used tools and methods for estimating theusage of test equipment that were encountered during the work with the thesis.

5.1 STP Utilization toolThe utilization tool measures the utilization of System Test Plants used in Wideband CodeDivision Multiple Access. A STP is a set of nodes booked to a specific project. One STP inWCDMA usually contains one or several Radio Network Controllers and other nodes like RadioBase Station.

5.1.1 DefinitionsThe utilization is calculated for a six hour period for each STP and up to four levels of utilizationare defined; none, low, medium and high usage. The level of usage is derived from several basemeasures:

• The number of shell connections to the RNC. The shell connections are: telnet, moshell,SSH, FTP, Serial and OSS.

• Whether the settings in the node have changed.

• The number of registered UE’s to the node.

These variables can then be combined into logical expressions that have to be true if acertain level of utilization is considered to be reached. One example could be:

(number of shell connections > 3) AND (number of changed settings > 0) -> usage = high

41

42 Current Solutions

5.1.2 Data collectingThe number of active UE in the RNC at the movement is sampled every 15 minutes. The valuesare saved in a file for each day. Once every hour a script is scheduled to collect log files from theRNC’s with information about the number of shell connections and changed settings. Monode,which is a part of Moshell, is used to create, parse and then delete the log files at the node.The parsing script extracts the shell connections and the changed settings during the last hour.This data is then saved in a log file, one for each RNC and day. From the log files a final datafile is assembled, which contains the values over a six-hour-period for the shell connections, thenumber of changed settings and the number of registered UE for all RNC’s. It is from this filethat the web-GUI calculates the usage level according to the specified rules.

5.1.3 Data presentationThe web-GUI in the tool is the same that is used in LTE eNodeB utilization tool but with afew differences. The tool can display four degrees of utilization, however in practice only twolevels are used: unused and high, which are shown in Figure 5.2 and Figure 5.1. When the pageopens the calculation of the usage is carried out according to the specified rules. This gives theopportunity of changing the logical expression and recalculating the usage. The level of usagethat is displayed for one day is the highest level of usage for one 6 hour period that day. Forexample in Figure 5.1 "2010-01-02 Saturday" has high level in the 0-24 hour period, since the12-18 hour period has high level of usage.

Figure 5.1. The second page of the web-GUI that shows the utilization during a time period of 6hours

5.1.4 Evaluation of the toolThe tool can use a lot of base measures to determine the level of usage. This allows the tool tocapture different types of activity in the nodes. However it has been shown, according to GuidoHüpohl, who is the creator of the tool, that the rules were too complicated. Therefore the toolonly calculats the number of shell connections and the number of changed settings. A methodfor defining the classification rules is probably needed, if a more complicated expression is used,though with more complicated expressions it is probably true that several kinds of activitiescan be detected.

5.2 Utilization tool for the eNodeB in LTE 43

Figure 5.2. The main page of the web-GUI that shows the utilization for one day

The model has been verified, by testing whether the log files are correct and that they areprocessed in the way it was intended. Whether the limits then gives correct results is notvalidated. The limits are determined from experience. Although the tool is good at collectingdata and has an informative GUI, the weakness lies in how to specify the rules and limits.Knowing how to interpret the base measures is essential for the quality of the output from thetool.

The level of usage is calculated for a six-hour-period, even though the data is collected everyhour. There is no reason for not having the same time resolution for the base measures andthe derived measures. Data could be collected every sixth hour, which would minimize theinterference of the testing or the level of usage could be calculated every hour, which wouldincrease the accuracy of the measurements.

5.2 Utilization tool for the eNodeB in LTEThe tool for measuring the utilization at eNodeB is today running and delivering indicators ofthe usage of eNodeB’s, also called LTE RBS, at Ericsson test plants in Kista and Linköping. Itwas first launched in mars 2009 and uses a modified version of the web-GUI from STP utilizationtool and the automatic test environment THC. It contains a data collection part and a datapresentation part.

5.2.1 DefinitionsThe sampling period is 2 hours. The state of the equipment can be used, unused and that nodata was collected or that the collecting of data is disabled. The states are derived from thefollowing three base measures:

• The number of bytes transferred over the S1 interface.

• The number of datagram’s transferred over the O&M (Operation and Management) in-terface. This interface is used by the operator to manage the eNodeB and is also usedduring testing.

• The number of restarts is included since the two other counters are zeroed when a restartoccurs.

If one of the base measures exceeds a threshold the state is classified as used. The thresholdsfor the variables are not fixed, although the default values of the thresholds are 125000 bytesfor S1 interface, 5000 datagram’s for O&M and two numbers of restarts during a period of twohours.

44 Current Solutions

5.2.2 Data collectionTo collect data the tool uses an existing test automation framework called THC (Test HarnessCore). THC is used for automated testing environment where secure connections to all eNodeBexist. Data is colleted by running a test case from a THC-server that fetches, parses, validatesand stores the data in a database according to Figure 5.3. The commands are transferred usingMoshell to the LTE eNodeB. It is in the THC server the processing of data takes place. Theprocessing consists of calculating the difference in the counter for an interface, since the previousvalue, and determininge the state of the eNodeB. If the connection could not be established itis set to no data. This test script is executed every second hour.

THC Server

Utilization database

Testscript: - Get data - Process - Store in DB

SHH/SFTP eNodeB

eNodeB

eNodeB

Figure 5.3. Diagram over data collection

5.2.3 Data presentationFrom the database the utilization data is presented in a web-GUI. At the main page there isa view over one month and the utilization in percent for each day and each eNodeB is shown.The percentage value is calculated by dividing the number of used hours by 24. The number ofused hours will always be a multiple of two, since each two hour period strictly has one state.The user can click on a specific day and node, which allows the user to obtain informationconcerning usage distribution for that day and get the values and thresholds for the variables.The average values of the utilization for the last 30 days are also calculated for each eNodeB.In this calculation a decision is made regarding how to consider a node that is disabled andwhen the state is no data. Both when a node is disabled and when no data could be collected,the node is considered to be unused. In Figure 5.4 an example of the view of the main page isshown.

5.2.4 Evaluation of the toolAn evaluation of the tool has not yet been carried out. . The thresholds have not beencalibrated, though this would probably improve the accuracy . It is clear that, for most of thenodes, it is the threshold for S1 that is exceeded when the node is considered to be used. Fornodes with less usage and a lot of missing data it is the restart variable that exceeds its thresholdmost of the time. The datagram counter over the O&M interface rarely exceeds its thresholdand when it does the S1 interface also has high traffic. The utilization of a node appears todepend on the type of test performed at the node. If that is the case, which requires deeperinvestigations, the thresholds may be set depending on the type of tests that are performed at

5.3 Ericsson Real Utilization Measurement Solution (ERUMS) 45

Figure 5.4. Screenshot of the presentation of utilization

the node. The presentation is not clear on whether the non-use time was unused at the time, ifno data could be collected or that the node was disabled. The status no data is unclear in thetool. It is in fact defined in the calculations as not used, which in this case is probably validbut it is not motivated. It cannot obviously be used when it is down, however one may arguethat it should be withdrawn from the total time before calculating the utilization.

5.3 Ericsson Real Utilization Measurement Solution (ERUMS)ERUMS is a tool that measures the utilization rate of network nodes by analyzing the IP-trafficto and from the node.

5.3.1 DefinitionsThe base measure is the number of filtered IP-packets to a node during a five minute period.It is used for the derived measure that shows the utilization for the interval according to thedefinitions below:

• O packets equals 0 % utilization

• 1-10 packets equals 50 % utilization

• 11 packets or more equals 100 % utilization

5.3.2 Data collectingA schematic picture of a system where ERUMS is used is shown in 5.5. The traffic in theIP-interfaces from the node is mirrored in switches, which forward the packets to a centralswitch that then forwards the packets to a Linux server. The server first filters out the packetsthat have an IP-address that matches the IP-address of the nodes that are being monitored.Packets that do not indicate activity in a node are rejected due to predefined rules. Thesepackets can be keep-alive messages or particular port numbers that are of no interest. Theremaining packets are logged in a MySQL database. Every fifth minute the collected packets inthe database are converted into statistic data since the packets themselves are not interesting,

46 Current Solutions

but only the number of packets per time interval. The received packets are stored in the mainmemory, which is another reason for frequently calculating the statistics from the traffic dataand then deleting the data.

Node

Node

Node

Node

ERUMS-

Server (Linux)

Backbone Network

Mirror switch

Mirror switch

switch

Figure 5.5. ERUMS schematic system description

5.3.3 Data presentationThe presentation is carried out at the ERUMS Linux server in a web-GUI where the usercan generate graphs of the statistics. The pChart PHP library is used to generate the graphsthat can present the utilization of one or several nodes during different time intervals. Thepresentation over the daily usage for one node during one month is shown in Figure 5.6. Theutilization can be presented both as a percentage of utilization per time interval or as thenumber of packets per time interval.

Figure 5.6. Screenshot of the presentation using pChart

5.4 ENSIEM adaption for node utilization 47

5.3.4 Evaluation of the toolERUMS only measure on IP-interfaces, which has a drawback since nodes that do not have IP-interfaces or nodes where traffic goes over other interfaces are impossible or difficult to measure.On the other hand it makes the tool flexible since it is possible to measure all nodes that dohave IP-interface. It is the filtering of packets that needs to be adapted for different nodes; theother part of the system is general for all type of nodes.

Another problem with ERUMS is that it is not possible to analyze interfaces with highdata rates. For each five minute interval the traffic is kept in the main memory. For exampleif ERUMS can use 1 GB of internal memory it will run out of memory within less than fiveminutes if the data rate exceeds 26.7 Mbit/s. The data rate into the server is the sum ofthe data rates for all mirrored interfaces. The problem can be solved by increasing the mainmemory or decreasing the interval between the statistic calculations.

The tool requires special hardware for the nodes that shall be monitored. Switches that canmirror interface are more complex than ordinary switches which make it expensive to scale upthe solution. The tool does not use any internal base measures in the nodes, which means thatit cannot discover activity that takes place within the node. However since mobile networksconsist of nodes that communicate with each other, it can be claimed that they are only in usewhile they are communicating.

The motivation for defining the utilization in five minute intervals to 0, 50 or 100 percentis unclear. The most basic definition is to only regard the equipment as either used or unusedin one interval. It is difficult to interpret what a percentage number of utilization during onetime interval actually means.

5.4 ENSIEM adaption for node utilizationThe tool is an ENIQ-based SIEM application called ENSIEM (Ericsson Network Security Infor-mation and Event Management). ENIQ (Ericsson Network IQ) is an Ericsson product designfor Performance Management in a multi-technology network and is a part of Ericsson OSSproduct. It can collect data from variety of network elements and create reports about thecondition in the network.

It is planned that the tool will replace STP utilization tool which today are used for mea-suring utilization of RNC and MGW nodes in Ericsson test plant in Jorvas Finland. Thesenodes are WCDMA products which are based on Cello, a generic platform for telecom appli-cation having ATM, TDM or IP transport [22]. The main purpose for a new tool is mainlybetter security management and report possibilities. The tool is created at BUGS Ericsson TestEnvironment which influences the objectives for the tool. Better security management meansthat unbooked equipment usage should be detected, since users pays for test equipment timeto BETE based on the booking of the equipment.

5.4.1 DefinitionsThe tool uses the same definition of utilization as the Utilization tool for STP in WCDMA, see5.1.1, which are; no, low, medium and high usage. The base measures are also the same:

• The number of shell connections to the RBS. The shell connections are: telnet, moshell,SSH, FTP, Serial and OSS.

• If the settings in the node have changed.

48 Current Solutions

• The number of registered UE’s to the node.

In this tool the usage is measured per hour. The level of usage is classified depending of thevalues of the three base measures. It is almost solely the number of registered UE´s that decidethe level of utilization for each hour. The default thresholds for the number of registered UE´sis 1 - 99 for low usage, 100-499 for medium usage and > 500 for high usage.

5.4.2 Data collectionData collection agents are software running on ENIQ Server that collected the base measures.Data can be collected from the monitored nodes using: SNMP polling, collect and parse logfiles and parse command printouts. It is then transferred using FTP, sFTP, SSH or Telnetdepending on what is supported by the node. The collected utilization data is stored in anENIQ database, where all data from the monitored nodes are saved.

5.4.3 PresentationThe user interface is the ENSIEM dashboard, which is a web based service which uses theBusiness Objective reporting capabilities to create rapports and provide a GUI. Data for thepresentation is stored in the ENIQ database. The GUI over the utilization per day is shownin Figure 5.7 where the usages is mapped into the four levels per day. The daily usage level isdetermined by the number of hours the equipment is considered used that day, independent ofwhat the level of usage per hour low, medium and high is now considered the same. 0 -3 hoursis no usage, 4-6 hours are low usage, 7-10 hours are medium usage and 11-24 hours are highusage.

Business Objective is a business intelligent solution production from SAP. It is used foranalytics, dashboards, visualization and reporting. For the utilization tool the dashboard func-tionality is used which includes features like flexible report selection and the possibility to share,save and schedule reports. In Business Objective access control is implemented, which is im-portant since users only should be able to see and generate reports with information that theyare allowed to access.

5.4.4 Evaluation of the toolThe strength of ENIQ-based utilization tool is that it uses ENIQ-server that are intended forcollecting data from nodes and that it is flexible in presenting the utilization data. By usingalready existing solutions many features are already implemented, like the access control. Byusing ENIQ-server already established connections to the nodes can be used, which simplifiesthe implementation of the tool. An additional strength of the tool is that the booking andscheduling information of the equipment is available for the tool to use. It is not used today;however it can be used to apply specific classification rules. For example if a STP is bookedfor function test specific classification rules for function test can be used with lower thresholdscompared to load test.

The drawbacks of the tool are the same as for STP utilization tool for WCDMA nodes. Thedefinition of utilization is not motivated and it is not possible to interpret what low utilizationone hour means. The different levels of utilization per hour is not used when calculating theutilization per day, which can indicate that the different level of usage (low,medium and high)is unnecessary.

5.5 Booking degree as utilization measure 49

Figure 5.7. Screenshot of the presentation of utilization

5.5 Booking degree as utilization measureEricsson is evaluating the utilization of test equipment today by calculating the booking ratein the asset management system used by BETE, called BAMS. It is important for BETE sincethe customers (testers) only pay for the time the equipment is booked. The indicator of thebooking degree is calculated in a naïve method by taking the average of the booking degree forall STPs. The value or cost of the STPs is not considered when calculating the indicator. Ifthe booking degree for each STP was weighted with its individual cost in calculations of theaverage booking degree the indicator would better meet the information need of BETE.

In the asset management system, BAMS, the booking of STPs can be done in quarter ofone hour, however the customers use to book one STP for several months. For that reason thebooking degree is a poor indicator of the real time utilization efficiency of test equipment. Ifthe booking of equipment would be done on more detailed level the booking degree would be agood indicator of the equipment utilization.

5.6 Other test efficiency indicator

5.6.1 Fault-slip-throughDuring a test process it can be difficult to measure how efficiently the tests are being performed.If only the found faults were to be calculated, that indicator would depend on the total numberof faults in the software. The total number of fault in software is very difficult to estimate,

50 Current Solutions

which makes an indicator that only calculates the found faults misleading. Therefore fault-slip-through measurements are more accurate in indicating the efficiency of software testing.

Function test (FT)

System Integration Test (SIT)

System Robustness Test (SRT)

System Verification Test (SV)

First Office Application (FAO)

BSC Design BSC I&V BSC System BSS I&V FOA customers

Fault should be detected

Fault is detected

Fault-slip-through

Figure 5.8. Definition of fault-slip-through

The later a fault is found the higher is the cost of fixing it [13]. To fix faults after deliverycan be a hundred times more expensive then to find and fix them in an early test process.Fault-slip-through is measured by comparing in what phase the fault was found with the phasein which it should have been found. One example is shown in Figure 5.8 where one fault thatshould have been found in the FT-phase is instead found in the SV-phase. The phase where thefault should have been found is defined as the phase where it is most cost efficient to find thefault, which is almost always - the earlier the cheaper. However some faults cannot be detectedin early phases due to the complexity of the faults. Where the fault should have been foundis estimated by the person that reports the fault, the tester, or the person that corrects thefault, the developer. It is important that a definition for these estimations is specified and thatthe developers and testers are educated properly [18]. When all faults are categorized they canbe assembled in a Table that shows where the fault was found and where is should have beenfound. Table 5.1 shows one example of how the fault-slip-through can be presented for thetest process. DIDDET (Did detect) means in what phase the fault was detected and SHODID(Should detect) means in what phase the fault should have been detected.

Table 5.1. Example table over slip through data for each phase

DIDDET: Design FT SIT SRT SV FOASHODET:

Design 27 13 12 7 8 4FT - 21 13 17 9 12SIT - - 37 18 7 9SRT - - - 21 15 3SV - - - - 32 10

FOA - - - - - 41

Ericsson is today using fault-slip-through, or fault slippery, when test operation in a project

5.6 Other test efficiency indicator 51

is evaluated. Studies of the test operation at Ericsson have shown that, according to the fault-slip-through measure, there is a possible improvement in the Function Test by 32 % and 39 %and in design phase by 85 % and 86 % for two case studies of software project at Ericsson [18].

Chapter 6

General model for utilizationmeasurements

Chapter IntroductionThis Chapter presents a general model for measuring the utilization of test equipment. Themodel is based on the theories from the Frame of reference in Chapter 2 and the analysis ofprevious tools in Chapter 5.

6.1 Efficiency indicators for test equipmentThe model suggests a differentiation of the efficiency indicators for test equipment into thefollowing two indicators:

Table 6.1. Equipment efficiency indicators

Indicator DefinitionEquipment Utilization Efficiency Shows the proportion of time the

equipment is used.Test Performance and QualityEfficiency

Shows the efficiency of the testoperation when the equipment isused.

The two indicators can be combined into a theoretical indicator called Overall Test Equip-ment Efficiency (OTEE) that captures the overall effective usage of test equipment. Theseparation into these two indicators allows a distinct definition of utilization that only requiresa classification of the equipment usage into discrete states. To calculate the equipment uti-lization only the states ”used” or ”idle” have to be classified, however if the metric is split

53

54 General model for utilization measurements

up into additional metrics other states of the equipment have to be monitored. In Figure 6.1the concept of Overall Test Equipment Efficiency is illustrated graphically. The separation ofOTEE gives a context to the Equipment Utilization Efficiency which is the scope of this thesis.If the Equipment Utilization Efficiency is low OTEE will also be low, however if Equipment Uti-lization Efficiency increases, the Test Performance and Quality Efficiency can decrease whichleaves the OTEE unchanged. This can be exemplified with the following scenario. One teste-quipment is used once every day for a test that lasts for twelve hours. The test performedon the equipment has high Test Performance and Quality Efficiency which is estimated to 80percent and the equipment is used for 50 percent of the time. It gives an OTEE of 40 percent.One day the test fails when it is almost finished and the test is repeated. The equipment willthen be utilized for 100 percent. The Test Performance and Quality Efficiency is half of whatis was the previous day, 40 percent, since the utility of the test is equal although it last for twicethe time. The OTEE will be unchanged at 40 percent this day too. The example shows thatincrease in Equipment Utilization Efficiencydoes not necessaryly have to increase the OverallTest Equipment Efficiency, however it gives an upper limit of OTEE.

Overall Test Equipment Efficiency

Test Performance and Quality Show how effective the tests are performed in the equipment and the quality.

Equipment Utilization Show the proportion of time the equipment is used.

Not the scope of this thesis. Availability rate Operational rate

Figure 6.1. The OTEE concept, which put Equipment Utilization in a context

The concept of OTEE, shown in Figure 6.1, illustrates the fact that the definition of equip-ment utilization exists in a context of other performance and quality measurements, which areneeded for the whole picture of how efficiently the equipment is being used. If only EquipmentUtilization Efficiency is used for this purpose it will give an over estimation of the overall equip-ment efficiency. To get the real overall efficiency the Test Performance and Quality Efficiencyis needed. It is not the scope of the thesis, however here follows a few examples of ways toestimate it:

• Number of activities completed compared to the maximum number possible.

• Comparison of the actual test result and the expected test result.

• Measuring the degree of utilization from the equipment, if it is possible to achieve this.

The performance efficiency is highly dependent on the equipment and the activities that areconducted. No detailed description will be presented in the scope of this thesis.

6.2 Equipment Utilization Efficiency 55

6.2 Equipment Utilization EfficiencyThe equipment utilization indicator is calculated by dividing the time the equipment been usedby the total time. The indicator can be separated into one indicator that shows the proportionof time the equipment is available and another indicator that shows the proportion of time theequipment is used compared to the available time. The indicators are inherited from the OverallEquipment Efficiency concept described in Chapter 2.1 and they are Availability Efficiency andOperational Efficiency and multiplied together they become the Equipment Utilization.

The utilization of equipment is modeled by classifying the equipment state for each timeperiod. It means that the equipment is only allowed to have one state per time period andthat there are a limited number of possible states. To calculate Availability Efficiency andOperational Efficiency at least the following three states have to be classified; used, idle orunavailable. The definitions for the Efficiency indicators are presented in Equations 6.1 and 6.2where N is the number of time periods and all time periods are of equal length.

ai ={

1 if the equipment is available for time period i0 otherwise

ui ={

1 if the equipment is used for time period i0 otherwise

i = 1,...,N

Availability Efficiency =∑Ni=1 aiN

(6.1)

Operation Efficiency =∑Ni=1 ui∑Ni=1 ai

(6.2)

Equipment Utilization =∑Ni=1 uiN

(6.3)

The Availability Efficiency shows the proportion of time that the equipment is availablefor testing compared to the total time. If the value is low it can be due to the fact that theequipment is down and maintenance has to be carried out or that the equipment is not bookedfor testing. Operation Efficiency shows the proportion of time that tests are carried out on theequipment compared to the time the equipment is available for testing. If the value is low itmeans that less testing is carried out compared to what was planned or that the equipmentwas booked up for a longer time than was needed for the tests which were planned. The reasonwhy fewer tests were carried out can be due to failure of other equipment needed for the testsor that the testers have to prioritize other tasks.

At Ericsson test environment the projects and departments that uses test equipment haveto book it for usage. With the information about the equipment booking status other efficiencymeasures are possible. Other interesting indicators can be; used time compared to booked timeand down time compared to total time. The down time indicator can be used in negotiationswith supplier in the case of non Ericsson manufacture equipment.

56 General model for utilization measurements

6.3 The state of the test equipmentThe general states for the test equipment are the following:

Table 6.2. General equipment states

State Description

Down Unbooked Equipment is not available for testBooked Equipment is down when it is intended

to be used

Idle Unbooked Equipment is not planned to be usedBooked Equipment is planned to be used but is

not

Used Unbooked Not allowed test in the equipmentBooked Test or test related activities in the

equipmentNo data The state of the equipment can not be

classified

The state Used is when the equipment carries out the activity it is intended for. For testequipment it is obviously Used when a test is being carried out, but also for all the activitiesnecessary for the carrying out the test and for analyzing the results. The type of activities thatmake the equipment considered to be used differs depending on the equipment. The state Idleis when the equipment is available for test but it is not used. Equipment is considered to be instate down if no tests can be carried out due to equipment failure or maintenance. In Ericssontest environment almost all equipment has to be booked before the testers are allowed to useit. The booking system is central in the asset management of Ericsson as described in Chapter1.8 and unbooked can therefore be consider to be an additional state of the equipment whichmakes the state a combination of usage status (used, idle or down) and the booking status(booked or unbooked). If the equipment lacks booking status or that the information is notpossible to access it can be neglected. The booking status of the equipment mainly contributeswith information about when unbooked equipment is used and provides an explanation for whyequipment is idle in the case of it being unbooked.

The equipment utilization indicator equations 6.1 - 6.3 require information that specifieswhether the equipment is available and if it is being used. Depending on whether the bookinginformation exists the states mapping to this information differ.

If booking information exists:

• Equipment is available when it has state: (used, booked), (used, unbooked) and (idle,booked).

• Equipment is used when it has state: (used, booked) and (used, unbooked).

If no booking information exists:

• Equipment is available when it has state: used and idle.

• Equipment is used when it has state: used.

6.3 The state of the test equipment 57

6.3.1 Measurement methodsThe equipment state has to be decided for each time period in order to calculate the efficiencyindicators described in Section 6.2. There are no general methods for deciding the equipmentstate that can be applied to all types of test equipment, there are however similar approachesthat can be used. Data has to be collected for all types of equipment and from the collecteddata the equipment state has to be classified.

An attribute is a property of one entity that can be measured and gives information that canbe use to classify the equipment state. The entity is often the equipment itself and the attributecan be traffic in an interface, status in the equipment that changes when activities occur orinteraction with the equipment that indicated activate. If the entity is not the equipment, theattribute can be of more indirect character, like which type of test (function test, load testor feature test) is planned in the equipment or what project has booked the equipment. TheISO/ETC measurement process standardization document, described in section 2.4, label itbase measure. The base measure can be either a sampled value or a counter value. If sampledvalues are used there is uncertainty in the classification of the equipment state, which will bediscussed later.

6.3.2 Classification of equipment stateAll the base measures for one time period and one piece of equipment is a record, which isused in the classification of the equipment state. The record is inputted in an algorithm thatoutputs the equipment states (used, idle or down) for the time period. It can consist of logicalexpressions or decision trees which can be constructed by experts of the equipment that knowhow it is used. Another approach is to collect records of base measures and at the same time letthe users of the equipment manually write down the state of the equipment. This gives alreadyclassified records which can be used for building up the classification rules.

6.3.3 Time resolutionThere are two times of interest in the general model for utilization measurements. These are thetime period for which the state of the equipment is determined and the time period between thecollecting of data. The time resolution of the equipment state is a trade-off between minimizingthe interference of the test operations and having a high accuracy in the equipment state.Lower time periods will usually have high cost since more resources have to be used. If thetime period for determining the equipment state is f the time that the equipment is in a statei, Si, have to be greater than twice f to be sure that the state is discovered. This is similar tothe Nyquist - Shannon sampling theorem illustrated in equation 2.7. In Figure 6.2 an exampleof an equipment that is idle for nearly two time periods although no time period is classified asidle since, in this example, it requires idle for the entire time period.

If only counters are used in the base measures the accuracy will not increase if the countersare collected with higher frequency as shown in Section 2.3.2. There is therefore no pointin having a time period for the data collecting that is smaller then the time period for theequipment state. However if samples are used in the base measures there is an uncertainty inthe base measures that decrease with higher sampling frequency. Which sampling frequencythat is needed depends on the distribution of the variable that are being sampled. For a slightlyvarying variable it is often sufficient to sample just once or only a few times. A distribution ofa base measure with high variance will give high error in estimating the state of the equipmentunless more then hundred samples are use (see Section 2.3.2). If an error in the individual

58 General model for utilization measurements

Used

Idle time

Equipment classified as: Used

Equipment classified as: Used

Equipment classified as: Used

Equipment classified as: Used

Figure 6.2. Sampling a binary signal that describe the equipment state.

equipment states can be accepted the error of the indicator will be smaller the more statesthat are used to calculate the indicator. In Table 6.3 the error in estimating the equipmentutilization is calculated using equation 2.16 where the equipment states (idle, used) are sampledwith different time periods and the utilization is calculated over different times. The calculationrequires an assumption that the state samples are considered to be binomially distributed. Inthe Table α = 0.95 and p = 0.5 which is the value of p that gives the highest error (see 2.3.2.2).

Table 6.3. Error in indicator when sampling equipment state

Equipment Utilization Sampling periodindicator calculated for one: 15 min 30 min 1 hour 2 hourDay 10.0% 14.2% 20.0% 28.3%Week 3.8% 5.4% 7.6% 10.7%Month 1.8% 2.6% 3.7% 5.17%Quarter 1.1% 1.5% 2.1% 3.0%

The calculation in Table 6.3 shows that even a sampling period of 2 hours gives an error lessthan 11 percent when estimating the Equipment Utilization over one week. The assumptionin the calculation that the state samples are binomially distributed is probably a worst casescenario. The interpretation of the result is that when samples have to be used instead ofcounters, sampling does not need to be carried out so often if the information requirement isthe utilization over a week or more.

The time resolution also determines the amount of data that needs to be stored. If theequipment state is decided per hour there will be 8760 record per year for one piece of equipment.The number of nodes or equipment in the whole test environment is several thousand andthe total number of records per year will be more than ten million. The size of the recorddepends on the information that is stored. One record can be either the base measures, thederived measures (Equipment states in this case) or both. If the amount of data becomes largerthan what is manageable it can by aggregated together and only the indicator can be saved.Detailed historical information about the utilization is not needed but rather the indicators forbenchmarking and comparison are required.

Chapter 7

Common Utilization Tool

Chapter Introduction

In this Chapter there will be a discussion on how a common utilization tool, the EricssonCommon Utilization Tool, chould be implemented with separated collector measurement mod-ules but with a common storage, configuration, presentation and Key Performance Indicatorsmeasurements.

7.1 Schematic model for a general utilization tool

As seen from the discussions in the previous chapter, the general utilization model consists ofa general definition of equipment utilization, general efficiency indicators and general states fortest equipment. However the method of collecting data and making measurements to classifythe specific test equipment state into one of the general states is not specified in the generalmodel.

Figure 7.1 shows a schematic model over a general utilization tool that we propose fortest equipment. Since it is hard to develop a utilization collector that will be able to detectall types of activities the utilization tool should consist of separated modules. The samplingmodule is specific for each type of equipment; however in many cases it is possible to reusesampling modules for new equipment. The data collection and equipment state classificationfollow the same methodology. The output from the sampling modules is the general equipmentstates for each time-period, which is stored in a generic database. Since data is stored in thesame database, in the same format and with the same definitions a common presentation viewis possible. Regardless of the type of equipment it can be presented in a similar graphicalinterface and using the same performance indicators, which have been suggested earlier.

59

60 Common Utilization Tool

Equipment A state sampling module

Equipment B state sampling module

Equipment Z state sampling module

Collect data

Equipment state classifier

Base measures

Equipment attribute

Equipment state (idle, used, down)

Generic utilization database

Performance indicators: Equipment Utilization Equipment Availability Equipment Operational

Node

Node

Node

Node

Common utilization presentation

Figure 7.1. Schematic model over a general utilization tool

7.2 Modules

The use of separated collector modules for determining the predefined states of the equipment isneeded to assure that the utilization is as accurate as possible. The use of the separated moduleswill also make it easier to distribute the development of the collectors to different organizations.This means that, for instance, the GSM part of Ericsson can, as we have done, implement aBSC module and for example BETE to design a module for the RBS platform. This will alsopermit the reuse of the Utilization tools that have already been developed throughout Ericssonorganizations. These tools could, at startup, deliver data to both the old location and to thenew common storage location.

The module should be constructed to first collect the base measures and with help fromthese classify the state of the equipment into derived measures. This derived measures shouldthen finally be delivered to the utilization database in the format stated by common guidelinesthat specifies the state of the tested resource. This should include information on equipment,time-period, utilization state and a comment for the utilization record.

The implementation of a module could be carried out in a variety of ways but we supportthe idea of using a pre-made system that communicates with resources such as Test HarnessCore. This will significantly reduce the development process and maintenance time needed tomeasure utilization. Since a tool such as THC is developed to support "all" equipment that isunder test, and to support the testing of equipment, this tool will provide many of the featuresneeded to collect the data. Another important benefit of using THC is that sites that do notalready have THC implemented get an environment deployed that can be used to automate the

7.3 Database 61

tests performed at the site.

The common utilization tool could provide a code structure that could be used to developthe test case and the test code to the module. To support reuse of code written for theindividual utilization modules a common repository such as eForge should be used to store allcode developed for the modules.

7.2.1 Time resolutions of measurementsThe requirements of the time resolution of the measurements will probably differ between dif-ferent kind of equipment and organizations. Therefore the common utilization tool should notset any strict rules on this subject. Our recommendation is that records should be stored onan hourly basis. This recommendation is based on our supervisor at Ericsson, Torbjörn Wick-ström’s, experience on how the equipment could be shared between testers but this resolutionis also preferred in the BETE Asset Management System system. This will also reduce theamount of storage needed to store the utilization records. In cases where this possible, likewhen test cases need to collect the utilization has a execution time longer than one hour orwhen this resolution isn’t needed it is better to lower the resolution than to interpolate. Thissince this interpolation of the data will force more data to be stored.

In cases where you need to sample data at a higher resolution than one hour we recommendthat this information should be stored in a separated table or database to keep the maindatabases structure intact and to keep the performance at the presentation layer.

7.3 DatabaseSince the overall aim of the Common Utilization Tool is to define a uniform base for theutilization of test equipment, is it convenient to store the utilization data at one commonsource. This will force the different developers and users of utilization measurements to thinkof utilization for different types of equipment in the same way. This will also aid the use of thesame KPIs for all types of resources and to create a common presentation GUI.

The requirements for data storage of the utilization data for a common utilization tool arequite high due to the large number of resources (entities in the database). Therefore it isimportant to keep a simple structure and reduce the information needed to be stored. Thestructure is very important because a poor database structure will reduce the performancesignificantly. The number of operations the database has to perform during a transaction ofdata will grow quickly, if the number of users, and therefore the load, grows fast and couldmake the database overloaded and unusable. Since Ericsson today has approximately 71 sitesand say that each have 500 items that they would like to measure in 24/7, this would give 71 *500 * 24 = 852 000 records/day. All these records will pretty soon require a large storage area.

A solution to this problem is to separate the information that is stored into distributeddatabases. The central storage utility should, in this scenario, only have to store managementinformation on a daily or monthly basis to produce the required KPI measurements and pre-sentation at a lower resolution. Together with this centralized storage regional site specificdatabases where collector modules could send these data will be used. These local databaseswill then consolidate on a daily basis to a central database. In the local database the modulespecific data could also be stored like the dns-name if it should be measured etc. By doing thisthe performance of the system will be higher and will keep the information needed for classi-fication; limits, attributes and other previous state variables. This will also make it simpler

62 Common Utilization Tool

to design individual collector modules, because the designer can make the decision as to whatinformation to store independently and what other modules are needed.

The required data that has to be stored about a resource in the common utilization tool is:

• Unique identifier

• Site

• Type of equipment

• Stakeholder (could be project or domain etc.)

And for the resource utilization:

• Timestamp (measurement period)

• State (used, unused, down)

Another important type of information to store is whether the resource is booked or not. Thisinformation could either be stored in the database or fetched on demand by the presentationGUI. Which solution that should be chosen depends on how this information is going to beused. Since this information will be needed for accurate KPI measurements on the utilizationwe recommend that this type of information is stored together with each utilization record.

Using the requirements above we propose the structure for the generic database model to beas the model shown in Figure 7.3. In this model we store and collect data for resources, type,groups and utilization. The design is flexible so that more features could be added if needed.

Figure 7.2. Database structure for the Common Utilization Tool

The resource table is show in Table 7.1. Resource utilization data is stored in the tableresource_utilization, see Table 7.2. This table will store the state, if the resource is bookedand a text comment for each utilization record. The booking state should be queried fromthe BAMS database when the resource utilization is collected. This will limit the amount of

7.3 Database 63

information needed to be collected from this database. The resource type information is usedto filter the number of entities in the presentation of resources in the WebGUI. The type ofresource could be, for example, BSC, RNC or a protocol analyzer. Since each resource canonly be of one specific type the type_id is stored for each resource. The type table is shown inFigure 7.4. Since this type of information would vary with time, it is not possible to change thisthrough the GUI. The site table stores information on which site a current resource belongs to,see 7.3 To support the feature that resources could be related to each other, a group table isintroduced. This table could group the resource by project, domain or responsible department.This information is set by the GUI. And since this information is changeable over time theresource and groups are connected by a many-to-many relation, see Table 7.6 and 7.5.

Table 7.1. Table: resource

Name: Type: Purpose:id BIGINT(20) Identificationname VARCHAR(45) Name of the resourceshow TINYINT(1) Describes whether the resource should

be shown in the WebGUIcomment TEXT Comment for the resource. Shown in

WebGUI.type_id BIGINT(20) Type identification

Table 7.2. Table: resource_utilization

Name: Type: Purpose:resource_id BIGINT(20) Identification of the resourcestartdate DATETIME Start time and date for the measure-

mentenddate DATETIME End time and date for the measurementstate INT State of the resourcebooked SMALLINT(1) If the resource is booked or not

Table 7.3. Table: site

Name: Type: Purpose:id BIGINT(20) Identificationname VARCHAR(45) Name of the groupcomment TEXT Comment which is shown in WebGUI

64 Common Utilization Tool

Table 7.4. Table: resource_type

Name: Type: Purpose:id BIGINT(20) Identificationname VARCHAR(45) Name of the groupcomment TEXT Comment which is shown in WebGUI

Table 7.5. Table: resource_group

Name: Type: Purpose:resource_id BIGINT(20) Resource identificationgroup_id BIGINT(20) Group identificationstartdate DATE The date the when the resource started

belonging to the groupenddate DATE Start The date the when the resource

ended belonging to the group

Table 7.6. Table: group

Name: Type: Purpose:id BIGINT(20) Identificationname VARCHAR(45) Name of the groupcomment TEXT Comment for the group. Shown in We-

bGUI

7.4 Common configuration layerThe Common utilization tool should include a configuration layer that could be used to configurethe resources. To use a common configuration layer puts some requirements on the design forthe module specific storage of resources. These databases must provide a table that lists theresources, with their name, a status as to whether the resource should be measured. If themodule also includes adjustable levels this should be able to configure with the configurationlayer.

7.5 Common presentation layerThe main requirement of the utilization presentation layer was to present the utilization ingeneric manner for all types of resources. Since a presentation layer for STP-utilization tool,which also is used for the LTE Util Tool and parts of the ENIQ tool, was available we startedby investigating whether this could be used. This should serve the purpose of showing theutilization in structured way where all types of resources could be treated equally. But wesoon realized that the performance of the tool did not meet our requirements. The versionused for LTE utilization, which uses some KPI has a very long response time. The solution

7.6 KPI reports presentations layer 65

that has been used to manage this problem is to use a static version of the page that could befetched instead of the on demand generated page. This approach works for this purpose but wewanted to have a flexible structure where the resources could be combined in numerous wayswhich would make this solution impractical. We also wanted to avoid using the outdated CGIprogramming language of which we, furthermore, have no previous knowledge.

Therefore we decided to create a new presentation WebViewer for our BSC utilization mod-ule that could be used as a presentation layer for the common utilization tool. We decided touse the same base structure as the tool we evaluated since this way of showing utilization seemsto work quite well. We also added the requirement to make the tool fast so that many resourcescould be show at once without a long response time. Since we have some previous knowledgein JAVA and Servlet/JSP development we made the choice to use this technology as the basefor the presentation layer.

The main presentation of resource utilization, see Figure 7.6, shows the utilization on adayly basis combined with a set of KPI for a specified month. The utilization record for aday shows the utilization as a percent value calculated by dividing the state used by the totalnumber of records for the specific day. This will, if the collector has been able to measureduring all hours of the day, be records with state used divided by 24. The KPI measures shownare, see Section 6.2 for more details:

• Average usage (equipment utilization) per day

• Average usage this month

• Average usage last 30-days

• Operational efficiency this month

• Availability efficiency this month

The choice of resources to be shown in the GUI can be set by updating the show parameterin the resource table, see 7.1. In the standard view the resources are shown grouped by thegroup to which they belong. If the user is only interested in a specific group the user can filterthe results by choosing a specific group and only show the included resources.

The second main view is the resource view, see Figure 7.6. This view shows the specifiedresource, year and months utilization records. In this more detailed view the individual utiliza-tion records states, are presented on an hourly basis. The utilization comment is also availableby hovering with the mouse pointer over a record.

Since one of the main motivations of construction was to construct a fast implementation,designing the code used has been of most importance. This is done by limiting the amountof traffic to the database and instead using the great indexing capabilities a modern relationaldatabase provides. Therefore many of the calculations that are needed to produce the statisticsare produced by the database itself by creating smart Structured Query Language queries.

7.6 KPI reports presentations layerThe presentation WebGUI will provide a good presentation of the utilization on a daily andhourly basis. From a management perspective these types of presentations will not be veryinformative. The common utilization framework should therefore provide more customizedreports. For this purpose the KPIWeb Tool could provide a useful platform for generatingreports and graphs. The reports should be configurable to include resources on a STP or

66 Common Utilization Tool

project level and with a variable time period. The generated report could consist of a table ofresource utilization, KPIs and could be further visualized by connecting a graph to show thetrend.

Figure 7.3. The main view in ECUT

7.6 KPI reports presentations layer 67

Figure 7.4. The resource view in Common Utilization Tool

Chapter 8

BSC Utilization Module

Chapter IntroductionThis Chapter present the results of the work which has been carried out for implementing theBSC utilization module that is developed for the common utilization tool, described in Chapter7. Ericsson BSC see Appendix B.

8.1 BackgroundThe work to construct a model to measure the utilization level on the BSC in the BETE labenvironment is a topic which is very important for the Linköping site. This is because a largepart of the site is developing the software and testing the software for it. The number ofBSCs available for test is huge and possibilities to lower the total spending for this resource issignificant. Ericsson has conducted previous studies and has concluded that it is hard to measurethe utilization on the BSC due to the internal structure and the way tests are performed. Thecollector should be able to detect all the types of test cases that are used on the BSC.

The BSC has the modular structure with a Central Processor and several Regional Pro-cessors that share the activities preformed in the node. This structure allows the CP to beidle even when the Regional processors are under load. The CP can also perform maintenanceoperations which gives some internal load, even if the node would be considered idle (unused).

Another big issue when implementing a utilization collector for the BSC is that the testenvironment is very complex and many of the activities would generate a lot of load on thenode.

8.1.1 Type of test casesDuring the development of new features for the BSC it is important both to verify that the newfeature works and that other functionally of the equipment are not affected by the new code.To guarantee this a lot of different test cases are produced to both test the BSCa and otherconnected nodes. These types of test can be categorized into:

69

70 BSC Utilization Module

• Load tests. During this type of test a lot of Circut switched and Packet switched trafficis generated. The generated traffic can either come from real nodes MS through the BTSor through a TSS. This type also covers most System & Verification tests where test casecheck if the new features introduces any faults on exist code.

• Function tests. These types of tests often only check a small part of the software.During these test cases many statistical functions are disabled and the load on the BSCis low. A high load is often not needed to test a single function and will probably makeit harder to test this small part.

• Feature tests. A specific feature is tested in a real environment at BSS I&V with thefocus on that the feature works as it was intended.

• Upgrading. Upgrading of CP, RP dumps and upgrading of the Adjunct ProcessorGroup. This type of test is mostly performed by the Product Line Maintenance depart-ment.

8.2 Pre-studyThe authors started the work for implementing the BSC utilization module by performing apre-study on how the BSC works and what types of measurement points exists. This was donebe reading documentations and discuss with our supervisor at Ericsson, Torbjörn Wikströmand with Greger Hultman, a representative from the BETE organization.

8.2.1 Equipment statesDuring the pre-study we gathered information on which types of states the BSC could be in,these are:• BETE pre-configuring of the nodes.

• Configuration of the TSS and other equipment needed to perform the test case.

• Configuration of the BSC.

• Test. The actual test execution.

• Idle (waiting for next test case). During this stage the node could be used for a separatetest case.

• Troubleshooting. In this state the node can appear idle in a traffic point of view but thetester could checks alarms, logs and configuration errors in the node or in surroundingnodes.

• Idle (waiting for feedback from designer). During this test case the node is often left inthe state it currently is in, so that the test can continue or if more information is needed.

• Down. This state often consists of maintenance work done by BETE. During this statethe node is often unreachable.

• Follow up. During this stage the node could often be considered idle if the tester is notstudying logs in the BSC.

To get as a good view as possible all the states above should be able to detect and form thisdetermine if the equipment is used or not.

8.2 Pre-study 71

8.2.2 Possible measure pointsResults from the pre-study rendered the following measuring points that we have evaluated:

• Capture real user traffic (traffic that is generated by the users of the system)

• Capture the traffic on the Operations and Maintenance interfaces

• Check energy consumption

• Using more advanced measure points internally in the node

• Indirect measuring points

8.2.2.1 Capture real user traffic

By capturing traffic on the BSC we could create a generic measuring point for all types ofnode at the test site. Since the main purpose of a mobile network is to route traffic betweenthe different nodes in the system it should be possible to capture activities by listening on thenodes interfaces. All nodes in the network are furthermore designed to communicate usingstandardized interface which would make it easy to develop generic measuring tool. A collectorfor this measuring point could be constructed as the Ericsson Real Utilization MeasurementSolution tool with the change that a switch which can handle GSM traffic and mirror this trafficto some kind of equipment that can count the traffic.

This tool will be able to detect all activities that involve some sort of traffic and could suita load test environment quite well but all test activities that generates a low traffic load willnot be detected. The configuring part of a test campaign will also be undetectable since thisphase involves low or no outgoing traffic on the node. Because of this problem, a collector forthis measure point will not cover all types and stages involved in the testing environment.

8.2.2.2 Capture operations and maintenance traffic

The traffic that is generated on the Operations and Maintenance interfaces comes from testersconnecting to the APG, CP and Regional processors through terminal clients and test scripts,when a OSS is configured to gather statistics or upgrade the node. These activities are all relatedto tests and are therefore a good measurement point to combine with the user generated trafficmeasurement point. These measurements will cover all test cases where the load on the OMinterface is high and most troubleshooting activities. Load test with a low amount of traffic onOM interfaces will however be hard to detect.

To get a measure of utilization it will therefore be necessary to combine this measuring pointwith some other method, such as the user traffic measuring point. This will probably covermost scenarios in the test environment and be a strong indicator of whether the node is usedor not. But the cost of the tool will then probably be higher than the gain and consequentlynot feasible.

8.2.2.3 Energy consumption

When a central processing unit operates the power dissipation (the process of consuming elec-trical energy) will vary during time due to the actual load in the node. Energy is dissipated byboth the internal switching of transistors and due to the heat that is generated. The manufac-tures of processors presents two measures for the power consumption of the CPU, the thermal

72 BSC Utilization Module

power during normal load and the maximum thermal power. Measuring the actual thermalpower would therefore give a measure of how much load the node is under. The BSC consists ofseveral processors and other devices, such as switches, that would consume more energy duringhigher loads.

The major problem with this measuring point is that the change in power consumptionwill probably be dependent on how the BSC is configured. The configurations of the BSC inthe test environments are diverse and changing and will therefore make this kind of measuringhard to calibrate. Another problem is also, as with the user traffic measuring point, that thetroubleshooting and configuring stages will be hard to determine and correctly classify as used.The solution would also be expensive to scale up since it requires hardware for measuring theenergy consumption.

8.2.2.4 Measuring inside the node

This method involves measuring the utilization by checking parameters inside the node. Mostof the current utilization tools developed for the BETE test environment use this approach.Most equipment used in cellular networks is constructed to be configured and monitored froma separate node like the OSS and functions for this purpose are therefore included in the node.The BSS is no exception from this and includes many statistical measurements, including usertraffic that could be used to measure the utilization. The BSC has a OM interface to theAPG, CP and all Regional processors included in the node. This measuring point will thereforeprovide the possibility of measuring both user traffic and capture commands and printouts thetester carried out on the OM-interface.

8.2.2.5 Indirect measuring points

The BSC in the BETE environment is always grouped together with other nodes to form aSTP. It is therefore possible to check if these nodes have been used to determine whether theBSC has been used. If one, for example, checks the logs for the TSS and find out that this hasgenerated traffic for a BSC the BSC had probably been used during this time. This of coursewould not give a complete view of the current situation but certainly help to determine theutilization of the node under test.

8.2.3 Chose of measuring pointBased on the results from the pre-study the authors decided to develop a software base solutionwhich checks the parameters inside node. This method seams to cover most of the differenttest cases and states that the test can be in. This solution is also related to the lowest cost ofinvestment because this does not involve investing in new equipment. A software implantationwill also be more scalable when you want to measure on new nodes that are added to testenvironment.

8.3 ImplementationThe design choice of using a software implementation that connects directly to the node a newstudy was started in order to find the appropriate base measures that will provide the derivedmeasure of utilization. This work was carried out by discussing the matter with the testers fordifferent departments in the GSM organization. The departments were chosen so that mostof the stages in the development stage of the software was represented. The departments that

8.3 Implementation 73

were included were BSC Design, BSS & BSC I&V and PLM forming a reference group for thedevelopment of the BSC utilization module.

8.3.1 Base measuresCommunication with the CP in the BSC is carried out through Man-Machine-Language com-mands. A command either changes the settings of the BSC, prints out information about thestatus or initiates a command, e.g. a restart. By printing MML commands the following basemeasures can be revived.

• Number of calls last minute, with the command plldp.

• Number of channels allocated to the cells, with the command rlcrp. The channels thatare counted are with this MML command are: BCCH, CBCH, SDCCH and TCH.

• Number of on demand channels for PS traffic, with the command rlgrp.

The typed commands are stored in an audit log at the APG which is the input/outputsystem for the BSC. Since MML command is the method for the tester to communicate withthe CP the audit log is an interesting attribute of the node. However OSS also types MMLcommand for checking if the node is up. These commands are filtered out and the reamingcommands are counted which gives the following base measure:

• Total number of types MML commands

The BSC can store statistics internally. These counters are called Statistics and TrafficMeasurement and contains many different types of statistics and traffic measurement. Theproblem with STS counters is that they do not have to be activated. A number of counters arehowever activated at almost all BSC since they are used for test KPI. The counters are groupedinto object types and counters in the object types BSC, CELLQOSG and CELLQOSEG givesthe following base measures:

• Total number of connected calls

• Total kilobyte in uplink and downlink

8.3.2 Code structureTo make the code as generic as possible we decided to construct a code structure that has abase test code. This base test code states the rules for which methods that should be availablefor the specific test that communicate with the different nodes. To implement this structure asimple Java inheritance structure for the test cases and test codes was created borrowing ideasfrom ATE team that created the LTE UtilTool.

8.3.2.1 THC Test Case

The main purpose of the THC Test Case GSMUtilizationTestCase is to create test cases foreach resource that should be measured for utilization. The test case is developed to be able tosupport different types of resources in the GSM family like BSC and OSS. This is achieved byusing an inheritance model for the test cases sharing a common super class with a method thatcan be used by all utilization modules.

The test cases in THC is generated by (Figure 8.1):

74 BSC Utilization Module

1. Send a request to the database for all resources.

2. Receive resources and prepare test codes.

3. Request resources from the resource manager.

4. Resource manager returns and reserves the resources for test (this is a non blockingreservation).

5. The Java Virtual Machine (JVM) schedules the test codes for execution with a Parallel-TestScheduler.

JTEXResource Manager

BSCUtilDatabase

Database

GSMUtilTestCase

JVM

BSCUtilTestCode 1

BSCUtilTestCode 2BSCUtilTestCode 3

BSCUtilTestCode n

...

...

1.

2.3.

4.

SQLCORBA

Figure 8.1. The BSC utilization modules executing environment.

8.3.2.2 BSC Utilization Test Code

The test code written in THC is as stated above implemented in inheritance structure to beable to reuse some of the code when a new module is created. The GSMBaseTestCode (superclass) includes the execution() method that is called by THC when the test code is executed.This method executes the methods needed to perform the test. For the utilization test codethe following steps are performed when measuring the nodes utilization:

1. Get resource name

2. Set start and stop time

3. Equipment initiation

4. Get data from database (if previous data is needed for calculating a change)

5. Get limits

6. Check equipment setup

7. Fetch data

8. Validate data

8.4 Collected data and classification of BSC state 75

9. Parse and store data

If some sort of error occurs during the execution an equipment clean up method is available toremove any temporary items or reset any state change on the node.

These steps could be considered general for measuring at all types of nodes. Not all of thesteps will be needed for all equipment and thus no code has to be included in this method. Forthe BSC utilization module all the methods except equipment initiation and check setup areused.

The THC test code for the BSC are connecting to the nodes using the MML resourcefactory that is pre built in THC for communicating with, for example, a BSC using the MMLlanguage. In this factory there are tools available for parsing the printout for MML commands.This makes it easy to interpret the result from the function executed.

A parser for the STS counter values was not available in the MML factory or in the THCrepository and had to be developed. During the development the authors came across parserimplemented in the Perl script language which we converted to JAVA code which could be usedfor this purpose.

8.3.2.3 Database

The BSC utilization module needs a database to store the resources that are included in theutilization measurements. This database includes information about the address to the resource(used to fetch the appropriate THC resource), whether the node is disabled for measurementsand a comment that is displayed in the common presentation layer if the resource is disabled.

The database also stores the different base measurements thresholds for indicating usage.They are stored for each BSC including a description that could be used when the limits areset.

8.4 Collected data and classification of BSC stateThe BSC utilization module has collected data from nine BSCs from different departmentsduring one month, which gave approximately five thousand records. Table 8.1 presents some ofthe records that the BSC utilization module has colleted. From each record the state of the BSChas to be classified. The base measures are either counters or samples. Since counters are moreaccurate they should be used primarily. In cases where the traffic counters in the BSC, calledSTS, are not activated the samples have to be used instead. When deciding the classificationrules for the BSC, the users of the equipment were interviewed. From the interviews it wasdifficult to determine the classification rules and therefore the choice was to start collectingdata and decide the rules based on the collected data.

8.4.1 Classification of the equipment stateThe least number of states of the BSC that need to be classified are down, idle or used. TheBSC is considered down if it not possible to access or login to the BSC. The states used and idlehave to be classified from the values of the base measures. Figure 8.2 shows the records thathave been collected with the BSC utilization module. In the Figure the number of typed MMLcommands and number of connected calls for each record are plotted. The records containadditional base measures, such as the data traffic, but the most significant base measures arethe two which have been plotted. In the collected data there were no records with PS traffic

76 BSC Utilization Module

Table 8.1. Examples of records collected with BSC utilization module

Counters SamplesBSCnumber

Totalnumberof con-nectedcalls

Total kBin up-link anddownlink

Totalnumberof typesMMLcom-mands

Numberof con-nectedcalls lastminute

Numberof chan-nelsallocatedto thecells

Numberof Ondemandchannelsfor PStraffic

64 4190 0 0 109 336 048 12049 5666558 50 217 616 18598 not active not active 225 0 316 081 7992 10420 0 139 239 1490 not active not active 0 0 316 0110 0 0 0 0 644 0101 1714 354 0 27 37 0120 3643 0 0 78 332 0

without CS traffic. That is why the classification will focus on the number of calls (CS traffic).It is clear that if there is traffic the number of connected calls is high. In the interval between10 and 1000 number of connected calls there are very few records. It means that the limit forclassifying the equipment as used, based on the number of connected calls, can be somewherein that interval. The Figure also shows groupings of records that represent different types oftest cases. Testers often use scripts for executing test cases which generate the same number ofMML commands. There are more records which are not shown in Figure 8.2 since they havemore then 10 000 calls or more then 200 MML commands during one hour.

For the records where STS is active about 20 percent of them have zero number of calls andthen it is the number of commands that has to decide the state of the equipment. In Figure8.3 a histogram illustrates the number of MML commands when the CS traffic is zero. Thereis no clear line in the histogram, which make it difficult to decide where the threshold shouldbe. There is a peak at 6 commands, which is difficult to interpret and further investigationsare needed before a threshold for these base measure can be decided.

The first hypothesis for classification rules is that the number of calls have to be higher than10 and the number of MML commands have to be higher than 5 if the BSC is considered tobe used, otherwise is it classified as idle. The rule is shown in Equation 8.1. With this rule itis possible to classify all BSCs that have the counter active. The BSCs that do not have thiscounter active have to be classified with other base measures and will be discussed later. It isoften BSCs where Function Test is performed that do not have STS counters active.

BSC is used if:Number of typed MML commands > 5

OR Number of connected calls > 10idle otherwise

(8.1)

With the classification rule that is presented above the operational efficiency is 87.0 percentand the availability efficiency is 97.5 percent for the BSC that have the counters active (non

8.4 Collected data and classification of BSC state 77

Figure 8.2. Visualization of the collected record

78 BSC Utilization Module

Figure 8.3. Histogram with the number of typed MML commands when there are no traffic

8.4 Collected data and classification of BSC state 79

Feature Test BSC). This gives an equipment utilization efficiency of 84.5 percent.

8.4.2 Validation of the classificationA validation of the classification rule was carried out by letting a person responsible for the testcases carried out in one BSC during one week, write down what activities that were carry outin each hour. During that week the BSC sampling module collected data from the same BSCfor each hour. It gave classified records which are shown in Figure 8.4. The Figure shows thatmost of the records are easy to classify since they have high traffic, however when the number ofcalls is zero it is more difficult to classify the records. The state No activity corresponds to theBSC being idle and the three other states corresponds to it being used. The already classifiedrecords in figure 8.4 show that a classification with one limit for the each base measure willhave a maximum accuracy of two misclassified records. That is if the rules for used are:

1. Number of typed MML commands > 6 or Number of connected calls > 0

2. Number of typed MML commands > 30 or Number of connected calls > 0

Figure 8.4. Visualization of classified record

Using the rule in Equation 8.1 there are three misclassified records in figure 8.4.

8.4.3 Samples or CountersWhen data has been collected it is possible to evaluate the difference between using counters orsamples. There are two attribute of the BSC that can be either a sample or a counter and that

80 BSC Utilization Module

is the number of calls and the amount of data traffic. Almost three thousand records have beencollected and from them the following analysis regarding sampling the traffic has been carriedout:

• In 8.3 percent of the records the number of calls during one minute is zero when thecounter for the number of calls is greater than zero.

• In 22.2 percent of the records the number of on demand channels for PS traffic is zerowhen the counter for PS traffic is greater than zero.

The bullets above indicated that if the number of calls is sampled, 8.3 percent of recordswith PS traffic will be misclassified. For the data traffic the error will be 22.2 percent if thecounters for data traffic are not active. The reason for the large difference between the CS andPS traffic depends on how the attribute is sampled. For CS traffic the sample is the numberof calls for the last minute. The sampling of PS traffic indirectly shows the data traffic bycounting the number of on demand channels that are used for PS traffic. These channels areused when the PS traffic becomes more intense which means that this measurement may misslow intensity PS traffic. However it was the best possible way to sample PS traffic that wasfound.

8.4.4 Classifying Function TestThe type of test for which it is difficult to measure utilization is mainly the function test. Whenfunction test is carried out, the traffic levels are very small. Here follows some suggestions fora better capturing function test:

• More intelligent parsing of the audit log by looking for specific commands that indicatesfunction test.

• Using different classification rules for function test.

In function testing there are specific commands that are used, for example the command"Test System" is used for to activate a system in the BSC used in function test. Whether thiscommand has been typed can be filtered out from the audit log. The base measure that shows ifthere are channels allocated to the cells can also be used to discover function test. The collecteddata show that this variable changed more in a BSC that in a function test.

Chapter 9

Utilization modules for otherequipment

Chapter Introduction

The Common Utilization Tool presented in Chapter 7 suggested various collector modulesdepending on the type of equipment to be used. Possible ways of implementing collectormodules for some other pieces of test equipment is presented in this Chapter.

9.1 UE simulatorsAn investigation about the possibility to measure the utilization of UE simulators used in LongTerm Evolution has been recently conducted at the site in Linköping. The two tools used todayare, in the thesis, called UE simulator 1 from supplier 1 and UE simulator 2 from supplier 2.In this section the conclusions from the report are presented for the two UE simulators.

9.1.1 UE simulator 1UE simulator 1 simulates the 3GPP LTE mobile terminal but requires a traffic simulator ofthird layer functionally called LTEsim. In the report no solutions were found where basemeasures can be measured directly in the UE simulator. The interfaces of the LTEsim couldnot give sufficiently accurate measures and the UE simulator 1 is difficult to access remotely.Two approaches for measuring activates in the node where investigated. The first solution wasto implement counters in UE simulator 1 that counts the number of tests executed, the numberof data or packets sent to UE simulator 1 or the number of commands sent to UE simulator1. The values of the counters will then be the base measures. The drawback of implementingcounters in UE simulator 1 is that it puts a load on the system, which can affect the test casescarried out and there is a cost of implementing it.

81

82 Utilization modules for other equipment

The second solution explored in the report is to have a network tap or port mirroring thatfilter and count the relevant packets in the network between the LTEsim and UE simulator1. The solution is equivalent to one of the previous tools used for utilization measurements,ERUMS see 5.3.

9.1.2 UE simulator 2UE simulator 2 also simulates UEs in LTE. The equipment is controlled from a WindowsXP instance and external traffic generators are often used. In the test plant in Linköping aninstance of Windows XP runs on a VMware ESXi server. The traffic generators are in a virtualWindows XP system, in a virtual Linux system or an external non-virtual data generator. Thereare several entities that have attributes that can be measures, however no suitabled attributesthat could be measured were found.

The report proposes two solutions for utilization measurements. The first solution is toimplement utilization logging in the UE simulator 2. However the requirement for this featurecompetes with other features required by Ericsson in the negotiations with the supplier. Theother solution is to have a network tap and traffic analyzer as described in the discussed solutionsfor UE simulator 1. A drawback of such solution is the cost for switches that have a networktap or port mirroring.

9.1.3 Conclusions for the UE simulatorsThe investigation presents two solutions for measuring the utilization of UE simulator 1 andUE simulator 2. One of the solutions is to implement a logging feature in the equipment, whichhas to be done by the supplier of the tool. It is possible to have it as a requirement for theequipment, in negotiations with the supplier. For both UE simulator 1 and UE simulator 2 aproposed solution is to tap or mirror the traffic in a switch and forward it to a server wherethe packets are filtered and counted. It is a general method which can be used for all kindsof network nodes with IP-interfaces. It is a solution that already exists within Ericsson calledERUMS and is described in 5.3.

9.2 Protocol analyzers9.2.1 Tektronix K15When communication and network products are developed and tested a useful tool is a protocolanalyzer that analyzes the traffic over an interface or in a network. Tektronix protocal ana-lyzer K15 is a tool for protocol analysis used at Ericsson test environment. It is a compact-PCIcompliant platform that runs Windows XP Embedded operating system where a software appli-cation captures the packets, analyzes the packets and lets the user interact through a graphicaluser interface. The equipment has to be physically connected to the interface that is beinganalyzed. The users (testers) are almost always logged in remotely through Remote Desktop,however it is possible to login on site. There is a newer protocol analyzer from Tektronix calledK18, which is a solution with a central server for analyzing and probes for capturing the traffic.The proposed utilization measurements for Tektronix are focused on K15 henceforth.

The users of the computer login remotely and start the capturing of data. The data is eitherstored at the hard drive in the compact-PCI or stored on a Server or at the testers PC. Theanalysis of the captured data can be carried out in real time while the capturing take place or

9.2 Protocol analyzers 83

it can be carried out later, after the capturing is completed. This means that a user can login,start the capturing of data, then log out and let the capturing of data continue. When enoughdata is captured, which can take several hours, the user logs in again in the computer, stops thecapturing and analyzes the data. Through interviews with the staff responsible for TektronixK15 the following base measures are suggested:

1. Is the application for capturing and analyzing running?

2. Have any users logged in?

3. Is the data being written to the hard drive?

4. Are data transferred from Tektronix K15?

The base measure number one gives the most accurate measure of the packet analyzerusage. Only one instance of the application can be running one at a time, which means thatit is important that the users quit the application when they are finished. As long as theapplication is running no other tester can use the equipment and it can be considered to byused. The base measure of checking if a remote login has occurred is a fairly good indicator ofusage, since users have to log in to start capturing the data. That measure will not capturewhen data is being captured without any logged in users. That state of the equipment can bedetermined by a combination of base measures three and four.

9.2.2 Nethawk M5Nethawk protocol analyzer M5 is a protocol analyzer software that runs on a Windows Server2000. It has similar functionality as Tektronix K18 and is used for the same purposes. Theservers are located in the test laboratory and the tester login to the server remote using softwarefrom Citrix. The remote log in procedure in the Server will not allow the application to continueto run if the user logs out. One experiment was carried out where the number of logged inusers were measured and whether the M5 application was running. It was carried out twice forfifteen servers and the results are presented in Table 9.2.2. The measurements are carried outby using PsTools, although they can be carried out with the Windows commands tasklist andquser.

In Table 9.2.2 two samples of base measures are shown for fifteen of the Nethawk servers.The samples demonstrate that it is a promising method for measuring the utilization of theseservers. As described earlier the application cannot run if a user is not logged in, which is shownby the experiment result in Table 9.2.2. However a user can be logged in without running theapplication and for that reason the straight forward way to classify the equipment state is todeclare that the equipment is used when the M5 application is running. From the result inTable 9.2.2 the equipment utilization is 46 percent although too few samples are made in thisexperiment for statistical confident results.

9.2.3 Proposed solution for packet analyzersThe base measures with the highest potential for both Tektronix K15 and Nethawk M5 is tofind out whether the application for data capturing and analysis is running. The classificationof the equipment becomes very straightforward. If the application is running the equipment isclassified as used and if not it is classified as idle. The problem with such solution is if the userforgets to close the application. It is today very important that they close the application sinceit is not possible for more than one instances at the same time to be run.

84 Utilization modules for other equipment

Table 9.1. Example of base measures of 15 Nethawk M5 Servers

Sample day 1 Sample day 2Server number Numbers

logged inusers

M5 applica-tion is run-ning

Numberslogged inusers

M5 applica-tion is run-ning

Nethawk01 1 Yes 1 YesNethawk02 no data no data no data no dataNethawk03 0 No no data NoNethawk04 0 No 1 NoNethawk05 1 No 1 YesNethawk06 no data No no data NoNethawk07 no data Yes no data YesNethawk08 0 No 0 NoNethawk09 2 No no data YesNethawk10 1 Yes 0 NoNethawk11 1 Yes 1 YesNethawk12 0 No 0 NoNethawk13 1 Yes 1 YesNethawk14 1 Yes 1 YesNethawk15 1 Yes 1 Yes

The measurement method is the mapping of the fact that the application is running or notinto a binary value that are the base measures. The measurement method can be of eitherof pushed or of pulled nature. Whether the application is running or not can be pulled fromthe equipment by running the windows command tasklist. The state in the equipment is thensampled and this has to be done frequently in order to give confident results. Another approachis to have an application running on the server that registers when the application is startedand closed or to implement a logging feature in the application itself. The last solution requiresthat the feature is implemented by the suppliers of the tools. Only letting the logged in usersbe the base measure is problematic. Tektronix K15 can be used even if no user is logged inand for both Tektronix K15 and Nethawk M15 a user can be logged in without running theapplication.

The proposed solution for Tektronix K15 and Nethawk M15 is to use the base measurethat show whether a special application is running or not. For the different equipment types,different applications classify the equipment as used. The solution should be generic for theWindows platform, hence it can be used for both Windows 2000 Server and Windows XPEmbedded.

Chapter 10

Discussion

10.1 Possibilities and potentials of equipment utilizationmeasurements

The potential for success of a Common Utilization Tool within Ericsson seems to be enormous.A lot of effort is currently being invested in different efficiency programs within the test envi-ronment in order to raise the output from the invested resources. A utilization tool will give agood idea as to how much the equipment is used and will therefore show some of the possibilitiesof these effectivity programs.

The equipment used for testing the developed software for the different networks is, further-more, very expensive and if one can determine at which rate they are used there will be a betterfoundation for investment decisions. If, for example, the optimal usage level is determined tobe 80% and this can be proved not to be the case, the question is whether an investment shouldbe made in more test equipment. There is of course a possibility that even if a low level ofusage is discovered, the equipment cannot be used for anything else, due to, for example, longconfiguration times and faulty equipment. If this is the case the utilization measures will in-dicate the need to invest in better and faster configuration methods or to further investigatewhether some other part of the test environment can be improved.

10.2 The value of a Common Utilization ToolThe situation today, where new utilization tools are developed for each type of equipment thatis to be measured on, is not a sustainable solution in the long run. Development resources arewasted and if the development resources could instead be combined, the costs for developingthese tools would be drastically reduced. These various tools also have their own definition ofusage and this therefore makes it hard to compare the data that is collected from the tools.This definitely makes it harder to follow up the impact on the utilization part of a test efficiencyprogram on a higher level, e.g. the Design Unit Radio level. If one is able to create a completepicture on this level, one would probably be able to detect the departments that are using theirtest equipment more effectively than others. A big gain would then be to study these to seewhat it is that makes the difference and use this to help others.

The purposed solution, of constructing a common tool with separated modules, will of coursemake the system a bit more expensive than just to implement a generic utilization tool that

85

86 Discussion

could be used for all types of equipment, i.e. ERUMS. But this will enable Ericsson to createa utilization tool that will hopefully work for all types of equipment regardless of platform andtype of test they are used for. A tool that is used for this purpose has to give as a correctpicture of realty as possible or questions and excuses will be made.

The trustworthiness of this kind of tool depends to a great extent, on achieving an un-derstanding of the users of the system. To achieve this understanding the users have to beinvolved in the process. Then even if the tool is not perfect the users will know what the tool’sinformation is actually presenting.

10.3 Weakness of the BSC Utilization Module

The collected datas show a high usage of the equipment. The suggested classification rule givesan equipment utilization of 84.5 percent. It can be interpreted in several ways. It is possiblethat the utilization value is overestimated, though the verification and validation do not indicatethat it is the case. Another explanation is that the collected data is not representative of thepopulations of BSCs since it was only possible to collect data from nine BCSs.

Another problem that has been found is the ”special cases”. One out of the nine BSCs wasused as a TRC (see Appendix B.2 for some time, which resulted in the outcome that none ofthe base measures indicated activity. This can be seen as a ”special case”, which either can bedealt with by using additional base measures or it can be considered a rare case and it can beignored. When the BSC utilization module is set to collect data from all BSCs it is likely thatfurther ”special cases” will be found. It is certain that not all test cases will be able to be foundand there is a trade-off between having a model that has few base measures and is simple andhaving a model that captures as many cases as possible.

To determine how the classification should be carried out is difficult. Other methods arepossible which could increase the accuracy in the classification. To construct a more sophisti-cated classification a large number of correct classified records are needed, called training data.Training data is time consuming to collect but one solution is to include functionality in theweb-GUI where the testers can manually put in information about what activity is carried outin the equipment. In practice it is likely that testers use this function when the tool is notclassifying the equipment as used when the tester consider it begin used. If that is the casethe training data will no be representative. If such a function in the web-GUI would exist adynamic classification is possible. The tool would then change the limits or the rules for theclassification over time. One technique to use for this is Bayesian Classifiers, which is success-fully used to classify SPAM. However that is a black box black box technique, which is difficultto understand. The results from the previous work at Ericsson, on similar tools, are that a toolwith a lot of base measures makes it hard to decide the classification rules. A simple model hasthe advantage of being easy to understand and people will therefore be more confident aboutthe result.

It is possible for testers to leave the traffic activated on purpose or to have scripts that typecommands to increase the Equipment Utilization. It could be complicated to consider suchbehavior in the utilization module. A better method is to complement the indicator EquipmentUtilization with Test Performance and Quality Measurements. However the test performanceand quality measurements used today, e. g. Fault-slip-through, is on an aggregated level for awhole project and not for a specific equipment.

10.4 Future work with the BSC Utilization Module 87

10.4 Future work with the BSC Utilization Module

The future work with the BSC Utilization module should be focused on collecting more datafrom many more BSCs. The data have to be analyzed and additional ”special cases” wouldprobably be found. Also the validation has to be further extended. This could be done eitherby discussions with the tester of the equipment after data has been collected or by letting thetester write down the test activities while the data is being collected. A more detailed validationcould improve the classification rules. The improvement of the classification rule in 8.4.1 is tocalibrate a threshold for the number of typed MML commands. The other base measures,especially when STS-counters are not active, have to be included in the classification rule aswell.

The current BSC Utilization Module has problems to classify the state of a BSC wherefunction test is carried out. Since the function test is carried out with traffic that is low andSTS-counters are inactive another approach is needed. One solution is to activate STS-countersin all BSCs that are being measured. This can easily be achieved by a MML command thatthe BSC utilization module can execute. Having some of the STS-counters active should notinterfere with test operations.

A smarter parsing of the audit log then just counting the number of commands is also agood base measure. Testers use specific commands when carrying out function tests. Howeverthe audit log that can be parsed with the BSC Utilization Module is for commands typed tothe CP of the BSC. There are Regional processors which testers type commands to, which arenot stored in audit log. How often testers only type commands to the Regional processors hasnot been thoroughly investigated. The number of BSC used for function test is not so high ,since most of the function testing is carried out in simulated and emulated test environments.

During the implementation a number of testers suggested other features for the tool, featuresthat did not directly contribute to the purpose of the tool but could easily be implemented. Itcan, for example, be to see in the web-GUI which tester that is logged in to BSC and whatconfigurations the BSC have. These Spin-off features can be implemented in the future.

10.5 Future work with the Common Utilization Tool

Since we have only implemented a part of the common utilization tool, a part of the presentationlayer, the next step in development should be to construct a database environment. Thisenvironment will have to be able to support a heavy load of traffic if it is to be implementedthroughout all Ericsson sites. The database tables presented in Chapter 7 will be needed tosupport the basic functions needed to present the information.

The proposed presentation layer will have to be adjusted so that user is able to choose whichequipment that should be presented, by resource, group (stakeholder, project etc.) or a moreadvanced choice like a dynamic STP grouping. This last choice will probably require that moreinformation is stored in the database, in order to be able to support this, but this could bepolled to get this type of result, since much of this information is stored in the BAMS database.

The common configuration layer could at first be implemented at a site level to reduce theamount of equipment that should be able to be configured in the tool. This will also reduce thelevel of complexity since the communication will only be required to be intra site rather thaninter site.

88 Discussion

10.6 Future work on the utilization uodules for other equip-ment

When implementing utilization modules for other equipment the experience from the work inthis Master Thesis can be useful. To be precise, the pre-study and implementation of BSCutilization module, described in Chapter 8, gave the following method for utilization modules:

1. Talk to the experts of the equipment to get information about which possible base mea-sures there are.

2. Talk to the users (often tester) that use the equipment for knowledge about how theequipment is used.

3. Implement at data collecting application using THC or reuse existing scripts and tools.

4. Collect data over a sufficiently long time period.

5. Based from the collected data discuss with the users of the equipment how to build upthe classification rules.

The first step is very important and was a key factor for the implementation of BSC uti-lization module. Since the experts of the BSC were available it was possible to identify thebase measures. Step number two is essential since the experts of the equipment do not haveto be the same people as those that carry out the tests. One type of equipment can often beused in many different ways for different reasons and the main users of the equipment shouldbe interviewed. It is more time efficient to reuse previous work and therefore the THC shouldbe used for collecting data.

The process of implementing the utilization modules is mainly time consuming when itcomes to validation of the model and discussions and interviews with testers and experts. Agood idea is to initiate the work with a workshop where the topic is to discuss possible basemeasures. Then a reference group can be put together, with people representing the main usersof the equipment. The reference group is important for steps three and five on the list above.

10.7 Future work in the test environmentDuring the work with this thesis we found some areas in the test environment that couldbe changed to make it easier to conduct and assure the accuracy utilization measurements.When Ericsson states the requirements for a tool that is bought from a supplier the possibilityof measuring utilization should be added. This will avoid the need of introducing complexand unnecessary ways of measuring utilization, e.g. in the case of the UE simulator 2 wherethe proposed solution required measuring and filtering high loads of data through externalequipment. If this is introduced early on in the negotiations with supplier the feature willprobably introduce a lower cost and the benefits of measuring the utilization will top this cost.

To counter the problem that occurs when a tester leaves a script or a traffic generator runningeven after the test is fulfilled is something that we don’t have a good solution to. One way ofsolving this problem is to increase the usage of an automated test environment, e.g. THC. Byscripting test case the generated traffic can automatically be taken down at the end test andthe "forgotten" traffic would not exist and therefore not detected as used.

Chapter 11

Conclusion

The objectives of the Master’s Thesis was to define utilization for test equipment, constructa general model for measuring utilization of test equipment and implement at prototype formeasuring the utilization of the BSC in GSM. The definition of utilization was straightforward,by classifying equipment into a discrete state per time period. The definition has the advantagethat it simple and that it is general for different equipment types. The drawback is that itcannot capture the performance and quality of the tests carried out on the equipment. Thegeneral definition was required for the general model for utilization measurements presented inthe thesis. The general model makes it possible to have a common presentation of the utilizationfor different equipment and have equal equipment efficiency indicators. The collecting of dataand classification of equipment is however not considered to be general and that is why theconcept of utilization modules is presented in the chapter Common Utilization Tool where thegeneral model is realized.

One utilization module was implemented for the BSC. The implementation shows that itis possible to measure the utilization of such equipment. It also shows that it is a winningmethod to reuse current solutions for this purpose. In this case it was THC which was used formaking the measurements which are primarily used for test automation. Since THC already hasconnections to the test equipment it can continue to be used for this purpose when utilizationmeasurements of additional test equipment are to be carried out. The BSC utilization modulehave collected over 5000 records from nine BSCs and a suggestion of a way to classify the BSCfor each record is presented. Using that classification rule, the BSCs that had their STS-countersactive were used 84.5 percent of the time.

The Master´s Thesis gives great possibilities for the future of measuring the utilizationof test equipment. The BSC utilization module can be scaled up to be used for all BSCs inthe Ericsson test environment and it is possible to implement utilization modules for otherequipment using the methodology and concepts presented in this thesis.

89

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Appendix A

Test Harness Core (THC)

THC is a internal "framework for a unified test tool environment within Ericsson". The frame-work is designed by Ericsson together with the external partner Cybercom Group. Cybercom isresponsible for implementing new requirements, carrying out maintenance and providing sup-port on THC. The work with THC is based on previous experience from developing the firstgeneration of test tool framework the Test Tool Middle Ware (TTMW) back in 2001.

THC could and is recommended to be used for all Ericssons R&D organizations. It ismostly used within System, Network and end-to-end verification of Telecom applications. Themain customers of THC is LTE RAN IoV (Kista and Linköping), CNIV in Aachen, GRAN(GSM Radio Access Network) in Linköping and MGW in Jorvas, all using Java as test casedevelopment language.

The main motivation for using a automated test environment is that the test plants oftenconsist of many different test tools, all with their own interface and platform. Therefore thetest code developer has to learn each tool’s Application Programmers Interface. When usingmore than one of these tools, and the tools are located at different places (and platforms) thesituation gets even more complex. One other major driving force is that the test channels areoften booked for a long period of time but not used more than a fraction of this time due toextensive reconfiguration of test scenarios. Also changing test environment from a simulatedenvironment to real equipment a tester often has to design a new test script. But since the twocases verify the same feature, using different tools, it is more efficient to use a standardized APIwhich enables reuse of the first test script.

The use of THC has proved to be an efficient test case design, execution, test tool devel-opment, maintenance, support building tool that therefore reduces the total cost of executingtest cases [10].

The basic idea behind the framework is to provide an easy conversion of test case specifica-tions that a tester creates to verify a new feature to a executive code that carries out the actualmeasurements.

A.1 Definitions in THCTHC provides a set of definitions that are used in the thesis [1]:

Feature Resource is an object providing functionality in a physical or simulated environment,for example Mobile SMS, MML Session etc.

93

94 Test Harness Core (THC)

Physical Resource is a hardware entity, for example a SUN workstation, an AXE node etc.

Resource is in the scope of THC, either a Feature Resource or a Physical Resource.

Test Campaign is a collection of one or more Test Cases to be executed together.

Test Case is a collection of one or more Test Codes and a Test Scheduler that decides how theTest Code(s) are executed. A Test Case always has a specified purpose and is thereforenot suitable for reuse. A Test Case shall be freestanding and there shall be no dependencyon other Test Cases.

Test Code is the actual code written in the notation understood by the test executor toperform some test task. JTEX understands Test Codes written in Java. Many testingbehaviours within a test case are similar, e.g. a mobile to mobile call. These generaltesting behaviours should be implemented in reusable test codes. A test code could in itsturn reuse other test codes in order to make a more specific but still reusable test code.

Test Session is the time during one or several resources is allocated by a test case or testcampaign.

A.2 System overview and conceptTHC is designed to be a distributed test automation system and is based on Common ObjectRequest Broker Architecture technology. The use of CORBA gives the possibility to develop aplatform and programming language independent framework with a specified Interface Defini-tion Language (IDL). The system consists of four subsystems, illustrated in Figure A.1.

• Test execution system

• Test Tool Middle Ware Subsystem (Middle Ware and Resource Manager)

• Tool Adaptation Subsystem (Resource Factories)

• Logging Subsystem

• External systems

– A Test management system (i.e such as MARS, a TMS system used at Ericsson).The TMS could be used to configure the resources involved in the test campaign.

– System under test (the actual node that test case covers)

A.2.1 Resource Factory (RF)THC uses different Resource Factorys to control the resources involved in the test campaign.The RF is collection of Test Tool Resources that actually communicates with the physicalresources (system under test). One example of RFs is the File Transfer factory that can com-municate through ftp, sftp and SSH and another important factory is the OM AXE (MML)factory that can communicate with APGs or the old Input/Output Groups. The RF are in-cluded in the TAS subsystem.

A.2 System overview and concept 95

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Execution GUI

Examples:• Load Generators• Protcol Analyzers• Link Breaks• Terminals• Unix/Linux/Windows ws

Eclipse IDE for test casedevelopment

Factories

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Figure A.1. Test Harness Core (THC) system components (used with permission by Jonas Madsen,Ericsson AB)

A.2.2 Test Execution System (TES)The TES is the subsystem that is in charge of executing the actual test campaign and test caseswithin the THC environment. The test campaign is started directly by the TES but the actualtest cases are initiated by TES but executed by several Test Executors. The TES provides thefollowing features.

• Test Campaign Executors Responsible for the test campaigns and reports to the progressto the TCE client. Also communicates with the TEXs and the Resource Manager

• Test Campaign Executors Client, Command-line interface or Graphical User Interface(only available at Site Linköping and Kista), screenshot of GUI in Figure A.3. The CLI isused to login to the THC environment and give instructions on which test campaign thatis supposed to run. The feedback from the TCE is directed to the standard output (oftenthe terminal window in the GUI where the test case is executed). The GUI is a graphical

96 Test Harness Core (THC)

interface to the CLI but also helps the user with configuring the required configurationon, for example, the resources needed to execute the test campaign.

• Test Executors, (e.g. Java Test Executor). The TEX carries out the test cases that havebeen designed in java test case code. One main opportunity here is that the actual codecan easily be reused if a new test case uses similar components. The TEX supports twotypes of schedulers, parallel or sequential execution of the test case.

• A MML Java Parsing Support. THC provides a very convenient way of working withMML printout by converting them to Tables or using regular expression matching.

A.2.3 Test Tool Middle Ware Subsystem (TTMW)The TTMW consist of the RM and the Core Services. The Core Services are, as mention earlier,based on CORBA Technology and are responsible for the communication between the differentblocks and provide the notification service. The RM maintains a repository for all resources inTHC with their associated configuration data. All resources are identified with a unique nameand can be prereserved for different test sessions, available and not available.

A.2.4 Log ServiceThe logging subsystem provides a database where the THC logs can be placed. The currentversion of THC uses a Notification CORBA Service connected to MySql database server wherethe different nodes in THC, TEX, TCE, RM etc. The log service also provides a log viewerwebservice which gives the tester the possibility of viewing the progress and log of the testcampaign. The log viewer consists of two views a Test Session View, see Figure A.4, whichgives a summary of all test cases and a Log Record View, see Figure A.2.4, which gives detailedinformation on a test case.

A.2 System overview and concept 97

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98 Test Harness Core (THC)

Figure A.3. The ATE GUI

A.2 System overview and concept 99

Figure A.4. Log Session View in THC

Figure A.5. Log Record View in THC

Appendix B

Ericssons Base Station Controller(BSC)

B.1 Base Station System (BSS)The BSC is as mentioned in Chapter 3, a part of the BSS which is responsible for all radio-related operations in the network such as; communication with MS, handovers, management ofradio resources and cell configuration. Ericsson’s BSS consist of:

• Base Station Controller. The BSC is the main switching part of BSS and controls theBase Station Transceiver Station and MS.

• Transcoder and Rate adapter Controller. Responsible for the rate adaptations betweenthe BSS and the NSS. The MS 33.8 kbit/s signal is to be adapted to the rate that is usedby MSC, 64 kbit/s.

• BTS. Interface between the MS and the rest of the GSM-network.

The BSC is one of Ericsson’s most powerful and flexible systems and commonly controlsabout 10 to 100 BTSs. The main functions are to control the MS, carry out measurements onradio conditions, and to support the handover functions needed.

B.1.1 TRC

As mentioned before the TRC main task is to perform the rate adaptations that is need for aMS to communicate with the NSS. The 33.6 kbit/s signal that is sent from the MS is removedfrom the overhead that is introduced to support a reliable transfer over the air interface, Um,to a 16 kbit/s. This 16 kbit/s consists of 13 kbit/s data and 3 kbit/s of signaling traffic.

The 16 kbit/s signal is the sent to the TRC where the signal is adapted to a 64 kbit/s PCMsignal. The reason why the signal between the MS and NSS is first converted to a 16 kbit/s isto reduce the number of links form the BSC to TRC by a quarter.

The PCM signal that is used in most PSTN networks and ISDN to the GSM codec’s does alsohas to be converted. This function is also a task performed by TRC and is called transcoding.

100

B.2 BSC Products 101

B.2 BSC ProductsEricsson’s BSC family consists of two main parts a combined BSC and TRC and a standaloneremote BSC (with a separate TRC or a combined BSC/TRC). The TRCs can be allocateddepending on load, polled, as Full Rate, Half rate, Adaptive Multi Rate (AMR) Full Rate andAMR Half Rate.

The combined BSC/TRC is the most common configuration of the BSC and is able tosupport up to 1,020 transceivers (Transcoder and Rate Adaptation Unit) and 15 remote BSC[3]. The remote BSC is ideal to use in locations with low traffic demand and can thereforeuse a separated TRC. These remote BSC supports 500 Transcoder and Rate Adaptation Units.To reduce the capacity needed the stand-alone TRC is located close to the MSC supports 16remote BSC [3]. The different BSC configurations are illustrated in Figure B.1.

MS

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Figure B.1. The different BSC configurations

The BSC is based on the AXE platform first developed for the PSTN. The newer AXE 810platform which is also used in other nodes like MSC, VLR, HLR. The AXE used in BSC withthe following components:

• APT. The APT is the switching part of the AXE. Includes the non-blocking switch and theEricsson Generic Magazine. The Generic Magazine gives the possibility to mix differentfunctions from different units in the same magazine instead of having to use separateones. The magazine supports 22 different devices which reduces both footprint and powerconsumption of the BSC.

• APZ. The APZ is the control part. Runs the applications that controls the switches.

102 Ericssons Base Station Controller (BSC)

• Adjunct Processor Group. The APG is the I/O system connected to the BSC.

The AXE platform is designed as a modular concept which provides a platform that caneasily adapt to changes and new features. This leads to an open architecture with reduced timeto market. The AXE is a multi functionality platform which means that the same AXE systemcan be used for many applications from a small exchange to a large mobile node. The softwarethat runs on the platform are programmed and deployed independently, and with standardizedinterfaces to increase the level of software security [2].

B.3 BSC Hardware and SubsystemsThe BSC includes several AXE system components such as:

• Common Channel Signaling Subsystem (CCS). Consists of both hardware and softwarefor signaling and routing etc. using the SS7 signaling standard.

• Group Switching Subsystem (GSS). Consists of Group Switch and is responsible for con-nection setup and teardown. The group switch uses a Time-Space-Time architecture withhigh speeds.

• Digital Link, DL. Is an interface between the Group Switch and the configured devices.

• Central Processor

• Regional Processors

• Adjunct Processor Group

The BSC is shown in Figure B.2. The Figure consists of 1. Cabinet, 2. Ericsson GenericMagazine (GEM) and 3. The circuit boards. Examples of the circuit boards are switch-,multiplexer-, transcoder and echo canceller boards.

B.4 APZ Control SystemThe control system in AXE-platform, the APZ, uses two level architecture with both a centraland distributed control. The system uses a powerful central processor and a number of regionalprocessors that can easily be connected.

B.4.1 Central ProcessorThe CP is duplicated to offer a higher level of security to the system and to reduce the totaldowntime of the switch. The AXE-platform automatically detects errors during execution byprocessing data in both processors and may, if needed, swap operations between the two sideswithout impact on the traffic. If an error is detected each side performs the test programexecuted by the Maintenance Unit (MU) on both processors. The side that is detected to bedefect is shutdown or rebooted to avoid system failure.

The CP is mostly concerned with CS traffic. In the CP the PLEX code language is usedwhich is an Ericsson developed language. During the periods of lower load the CP will conductmaintenance activities and therefore there some load can exist on the CP even during idleperiods.

B.4 APZ Control System 103

1.

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3.Figure B.2. The BSC components

B.4.2 Regional Processor

The Regional processors are used for repetitive and routine operations and for process intensivejobs. In the RPs C is used as programming language and is often used for PS traffic than theCP. Activities in a RP can be executed independently of the CP and therefore the total systemload is distributed between the system entities.

B.4.3 Adjunct Processor Group

Adjunct Processor (or Adjunct Processor Group) is used for maintenance, management andlogging. It is an evolved AXE I/O system with a focus on low cost, board size and is integratedin the Ericsson General Magazine. The APG provides a an interface between the AXE node andthe OMC so that the node can be configured, traced for errors and record traffic information.

APG includes a terminal communications interface to the CP that could be used for exe-cuting commands and to produce printouts. The CP file system is also managed by APG inwhich files used by the CP are stored together with charging- and other statistical data.

The APG is based on a Microsoft Windows platform and is, like the CP, duplicated forincreased security. The charging data, Formating and Output Subsystem, is forwarded toexternal billing nodes. The statistics from the CP are stored as STS files which are blocks ofrelated counters, i.e. the BSC block which records the number of connected calls.

The charging data is collected by the CP and sent to the APG where it is stored andtransferred to the billing system which is responsible for the AXE node.

104 Ericssons Base Station Controller (BSC)

B.4.3.1 STS

STS gives support for the collection, storage and presentation of the AXE-nodes statisticaldata. STS consists of counter groups (blocks) that the supports functions like Authentication,Local Number Portability, Traffic on Routes, Size Alteration Events, Multi-Exchange Paging,and Subscriber Activities [11]. The information from the CP collected by the STS subsystemwithin the APG is in a raw format so that it can be stored in the filesystem.

The STS system is constructed to produce output in the 3GGP ASN.1 but can also produceterminal output and comma separated files. STS supports approx. 2.100.000 counters recordedat a 15 minute interval (by default) [11]. All these counters can also be configured to be collecteddirectly from the BSC to the OSS through the Record Transfer (RTR) and Generic OutputHandler (GOH) where the data can permanently stored in OSS databases.

B.5 OM interfaces

The BSC provides many ways of connecting to the internal components to conduct maintenance.An operator most often only uses the APG to connect to the BSC and then through the OSS.The developers and tester of the BSC software often use the APG for manual control of theCP through an application called WinFIOL but they also connects directly to the Regionalprocessors for faster access. See Figure B.3 for more details.

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Figure B.3. Possible connections to the BSC OM interfaces

B.6 Man-Machine Language (MML) 105

B.6 Man-Machine Language (MML)MML is the language used to communicate with OM interface om AXE based nodes. Thelanguage is used both to configure (c), to create printouts (p) from the node and inititiate (i)commands, e.g. restart. MML is written according to ITU-T recommendations to follow astandardized structure.

B.6.1 Command structureThe MML-language uses a general structure for the commands as:

COMMAND CODE:PARAMETER NAME=PARAMETER VALUE;

• The command code is the name of the command and a print command often ends withthe letter p to indicate that the command only produces a printout and no change on thenode. The code often contains five characters.

• The parameter name is used to how and where the command should be carried out.More than one parameter can be used by separating them with a comma.

• The parameter value specifies the value of the parameter name requested.

Acronyms and glossaries

1G first generation mobile systems, examples NMTand AMPS

2G Second generation mobile systems, exampleGSM

3GGP 3rd Generation Partnership Project. 3GGPscope is to produce technical specifications thatcould be used for 3G mobile systems and main-tenance and development for the GSM system

AGCH Access Grant ChannelAMPS Advanced Mobile Phone ServiceAPG Adjunct Processor GroupAPI Application Programmers InterfaceAPZ The controll part of the AXE platformAuC Authentication CenterAXE Ericsson developed telephone switching system

BAMS BETE Asset Management SystemBCCH Broadcast Control ChannelBETE BUGS Ericsson Test EnvironmentBSC Base Station Controller. Is one of the networks’

most node . Controlls the MSBSIC Base-Station Identity CodeBSS Base Station SubsystemBSSMAP Base Transceiver Station Managment Applica-

tion PartBTS Base Station Transceiver StationBTSM Base Transceiver Station Management

CBCH Cell broadcast channelCCCH Common Control ChannelsCCH Control ChannelCell An area covered by a BST

107

108 Acronyms and glossaries

CLI Command-line interfaceCM Connection ManagementCN Core NetworkCORBACP Central ProcessorCS Circut swiched data. Data is sent in a connec-

tion oriented setup with reserved capacity. Of-ten used for speech

DCCH Dedicated Control Channelsdns-name Domain Name System is i.e. used to identify

windows computers by their hostnameDURA Design Unit Radio which is the design unit for

mobile radio

ECUT Ericsson Common Utilization ToolEDGE Enhanced Data Rates for GSM EvolutioneForge eForge is Ericssons open source community for

all types of code. eForge could be compared tothe SourceForge project but also permits inter-nal Ericsson users. eForge is courently migarat-ing to the new TeamForge system

EIR Equipment Identity RegisterEMS Enhanced Messaging ServiceERUMS Ericsson Real Utilization Measurement SolutionETSI European Telecommunication Standards Insti-

tute

FACCH Fast associated control control channelFCCH Frequency control channelFDD Frequency division duplex. Scheme that uses

seperate frequency bands to support duplexchannels, communications in both directions

FDMA Frequency division multiple access. Scheme thatallows multiple users to communicate by split-ting the available amount of frequencies intosmaller parts, in the simplest form; one part foreach user

FEC Forward Error CorrectionFOS Formating and Output Subsystem

GGSN Gateway GPRS Support NodeGMSK Gaussian Minimum Shift Keying

Acronyms and glossaries 109

GPRS General Packet Radio ServiceGSM Global System for Mobile Communications:

originally from Groupe Spécial MobileGUI Graphical User Interface

HARQ Hybrid Automatic Repeat reQuestHLR Home-Location-Register

IMEI International mobile subscriber identityIMSI International Mobile Subscriber IdentityIOG Input/Output GroupISDN Integrated Services Digital NetworkITU International Telecommunications UnionIWF Internetworking Functions

KPI Key Performance IndicatorsKPIWeb KPIWeb is a tool implemented to automate

DURA I&V (Integration and Verification) KPImeasurements

LA Location AreaLAI Location area identityLAPD Link Access Protocol for the ISDN D-channelLAU Location Area Update consist of many CellsLSMI Local Mobile Subscriber IdentityLTE Long Term Evolution. Proposed to be the 4G

cellular network system. Uses a all IP core net-work

MD Meditation deviceME Mobile EquipmentMM Mobility ManagmentMML Man-Machine-LanguageMN Management NetworkMS Mobile StationMSC Gateway Mobile Switching StationMSC Mobile Switching StationMSISDN Mobile subscriber ISDN numberMSRN Mobile Subscriber Roaming Number

NCH Notification channelNE Network ElementNethawk A protocol analyzer used in the test operations

at Ericsson Test Enviorment

110 Acronyms and glossaries

NMT Nordic Mobile Telephone. 1-G systemNSS Network and Switching Subsystem

OEE Overall Equipment EfficiencyOM Operations and MaintenanceOMC Operations and Maintenance CenterOML Operations and Maintenance LinkOS Operations SystemOSI Open Systems InterconnectOSS Operations (Support) Subsystem

PCH Paging channelPCM Pulse Code Modulation. Often 64 kbpsPIN Personal Identification NumberPLM Product Line MaintenancePLMN Public Land Mobile NetworkPS Packet switched data. Data is sent without to

first setup a connection. No capacity is reservedbut therefore not wasted during idle periods

PSTN Public Switched Telephone NetworkPUK Personal Unblocking Code

QA Q-Adapter

RA Routing AreaRACH Random Access ChannelRAN Radio Access NetworkRBS Radio Base StationRF Resource Factory. RF is controls a resource in

THCRM Resource ManagerRNC Radio Network ControllerRP Regional ProcessorRR Radio Resource ManagmentRSS Radio Subsystem

SACCH Slow associated control control channelSCH Synchroization channelSDCCH Standalone dedicated control channelSDMA Space division multiple access. SDMA separates

the channels into physical chanels spaced apartfrom each other

Acronyms and glossaries 111

SGSN Serving GPRS Support NodeSIM Subscriber Identity ModuleSMS Short Message ServiceSMSC Short Message Service CenterSQL Structured Query Language. SQL is standard-

alized language retrieve and store informationin a relational database management system

SS7 Signaling System 7. SS7 is a set of telephonysignaling protcols used to tear upp and downcalls, number translations, billing, sms etc.

STP System Test PlantSTS Statistics and Traffic Measurement

TAS Tool Adaptation SubsystemTCE Test Campaign ExecutorsTCH Traffic ChannelTDM Time-division multiplexing. Multiplexing

scheme where the nodes are taking turns oftransmitting to the media and therefore slicingthe total available time into slots.

TDMA Time division multiple access. Scheme that al-lows multiple users to communicate by splittingthe available time for transmission into smallerparts, in the simplest form; one part for eachuser

TE Terminal EquipmentTektronix protocol analyzer used in the test operations at

Ericsson Test EnviormentTES Test execution systemTEX Test ExecutorsTHC Test Harness Core. A framework for a auto-

mated test environmentTMN Telecommunications Management NetworkTMS Test management systemTMSI Temporary mobile subscriber identityTRAU Transcoder and Rate Adaptation UnitTRC Transcoder and Rate adapter ControllerTSS Telephony Softswitch Solution is a solution for

Telephony with emulation of subscriber services.TTMW Test Tool Middle Ware Subsystem

UE User Equipment

112 Acronyms and glossaries

VLR Visitor-Location-Register

WCDMA Wideband Code Division Multiple Access. 3-Gsystem

WS Workstation