Self-x Whitepaper Final v1.0

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E3 White Paper, Version 1.0, 2008-12-22 1/24

A White Paper by

the FP7 project

End-to-End Efficiency (E3)

Self-x in Radio Access Networks

Editors: Eckard Bogenfeld , Ingo GaspardDeutsche Telekom AG

Document Status: Final Version

Version: 1.0

Total Number of Pages: 24Date: 22 December 2008

Abstract: This white paper deals with the aspects of a self-organizationnetwork (SON) which is considered along its different manifestations beaddressed as self-x, e.g. self-configuration or self-optimization. The SONfunctionality includes all possible technical functions that a networkmanages in an autonomous way. SON follows the paradigm change wherenow the focus is fixed on the following two aspects, right from the start:excellent network performance and operational efficiency.

Keywords: Self-organizing Networks, SON, Self-x, Self-optimization, RadioAccess Networks, RAN




Contributors

Alcatel-Lucent Deutschland AG Harald Eckhardt, Edgar Kuehn

Deutsche Telekom AG Eckard Bogenfeld, Ingo Gaspard, Manfred RosenbergerTelecom Italia S.p.A. Enrico Buracchini, Paolo Goria, Alessandro Trogolo

Telefónica I. y D. S.A. Unipersonal Raquel García, Luis Miguel del Apio, Beatriz Solana

Beijing University of Post andTelecommunications

Dian Fan, Zhiyong Feng, Vanbien Le

Fraunhofer-Gesellschaft e.V. Stefan-Liviu Taranu

National and KapodistrianUniversity of Athens

Nancy Alonistioti, Apostolis Kousaridas, Eleni Patouni,Panagiotis Spapis

University of Piraeus ResearchCenter

Panagiotis Demestichas, George Dimitrakopoulos,Yiouli Kritikou, Dionysis Petromanolakis,Aggelos Saatsakis, Vera Stavroulaki, Kostas Tsagkaris

Universitat Politecnica deCatalunya Ramon Agustí, Francisco Bernardo, Jordi Pérez-Romero,Oriol Sallent

Table of Contents

Executive Summary............................................................................................3 Acronyms ........................................................................................................... 4

5567

7789

1011

111112131414

151516171818192020212122

23

1. Introduction ................................................................................................... 1.1 SON prioritization from an operator’s point of view.............................................. 1.2 Different self-x functionalities........................................................................... 2. Research, Standardization and I ndustry Fora activ ities ................................. 2.1 Self-x activities in the CELTIC project Gandalf .................................................... 2.2 Self-x in the FP7 project SOCRATES .................................................................. 2.3 IEEE 802.16 .................................................................................................. 2.4 NGMN........................................................................................................... 2.5 3GPP .......................................................................................................... 3. E3 approach .................................................................................................. 3.1 Different aspects of self-x functionalities ..........................................................

3.1.1 Self-optimization in heterogeneous wireless networks ............................... 3.1.2 DSNPM – A SON functionality ................................................................ 3.1.3 Self-management of spectrum in wireless networks .................................. 3.1.4 Self-configuring protocols .....................................................................

3.1.4.1 Autonomous DM for RAT selection and protocol configuration ............... 3.1.4.2 Dynamic configuration of RAT protocol components ............................ 3.1.4.3 Autonomic RAT selection algorithms .................................................

3.1.5 Self-organization of cognitive network elements ....................................... 3.1.6 Self-optimization of cognitive devices .....................................................

3.2 Implementation issues at self-x use-case examples ........................................... 3.2.1 Architecture for management of reconfigurable BS ................................... 3.2.2 Self-healing based on a use case in a real network ................................... 3.2.3 Add cell self-configuration use case ........................................................

3.2.3.1 Motivation and realization in highly dynamic E3 environments............... 3.2.3.2 Principles for add cell self-configuration.............................................

3.3 Ideas to the assessment of self-x systems ....................................................... 4. Conc lusions & Outlook ................................................................................. References .......................................................................................................




Executive Summary

This white paper deals with the aspects of a self-organization network (SON) which is

considered along its different manifestations be addressed as self-x, e.g. self-configuration or self-optimization. The SON functionality includes all possible technicalfunctions that a network manages in an autonomous way. SON follows the paradigmchange where now the focus is fixed on the following two aspects, right from the start:excellent network performance and operational efficiency. This new mindset is a naturalconsequence because complexity and heterogeneity of future radio access networks willdramatically increase, and simultaneously also the operational tasks such as networkplanning, deployment, OAM functionalities, network optimization and so on.

Chapter 1 introduces the basic self-x paradigm. Self-x enables the automation of operational tasks, and thus it minimizes human intervention. Hence, the operationalexpenditure (OPEX) is reduced. Generally self-x functionalities are based on a loop of gathering input data, processing these data and deriving optimized parameterization.

Furthermore, self-x effectuates the improvement of the usability of future wirelessaccess solutions (“plug&play”), and it accelerates the introduction and deployment of new wireless services. In addition, self-organizing approaches may contribute to furtherincreasing spectral efficiency, since they can be used to allocate capacity where it isneeded.

Chapter 2 gives an overview about the self-x activities in research, standardization andindustry fora. The current framework of a SON is governed by the activities deployedunder several research initiatives which try to develop the appropriate techniques for theautomation of management and optimization tasks in the radio access network. Thepresence of the self-x mechanism becomes especially important for RAN Long TermEvolution. Thus, SON is a topic at 3GPP and at the NGMN consortium. Furthermore, it isalso supported by the IEEE 802.16m task group.

Chapter 3 represents the current status of the work on self-x in the E3 (End-to-EndEfficiency) project which is supported by the European Union under the 7th FrameworkProgram. The project is evolving current and future heterogeneous wireless systeminfrastructures into an integrated, scalable, efficiently managed Beyond 3G (B3G)cognitive system framework. E3 brings together European key players in the domain of cognitive radios and networks, self-organization and end-to-end reconfigurable systems.The self-x framework is analyzed as well as the problem statement from severalperspectives in terms of dynamic self-organized heterogeneous wireless networksegments, spectrum management, self-configuring protocol and self-optimized networkdevices. Additionally, it is shown exemplarily how the E3 concepts can be put intopractice. For that purpose, three use cases have been selected for addressing a broadspectrum of network infrastructure functionality enabling self-organization issues.




Acronyms

Acronym Meaning

3GPP 3 Generation Partnership Project

7FP 7th Framework Program

AGW Access Gateway

ARRM Advanced RRM

BS Base Station

DM Decision Making

DSA Dynamic Spectrum Assignment

DSM Dynamic Spectrum Management

DSNPM Dynamic Self-organized Network Planning and Management

CAPEX Capital ExpenditureCID Cell Identification

E3 End-to-End Efficiency

eNB evolved Node B

EPC Evolved Packet Core

E-UTRAN Evolved–UTRAN

FBS Flexible Base Station

HO Handover

IEEE Institute of Electrical and Electronics Engineers

IMT-Advanced International Mobile Telecommunications – Advanced

JRRM Joint RRM

KPI Key Performance Indicator

LTE Long Term Evolution

MAC Medium Access Control

MME Mobile Management Entity

NGMN Next Generation Mobile Networks

NOFM Network Optimization Functional Module

OAM (or O&M) Operation and Maintenance

OMC Operation and Maintenance Centre

OPEX Operational Expenditure

QoS Quality of Service

RAN Radio Access NetworkRAT Radio Access Technology

RRCM Radio Resource Control & Management

RRM Radio Resource Management

SA System Aspects

SGW Serving Gateway

SON Self-organizing Network

UMTS Universal Mobile Telecommunications System

UTRAN UMTS Terrestrial Access Network

WiMAX Worldwide Interoperability for Microwave Access



1. IntroductionThe complexity and heterogeneity of future radio access networks will dramatically

increase and simultaneously also the operational tasks such as network planning,deployment, OAM functionalities, network optimization, etc.. The current mindset, asseen in mobile telecommunication market, is as follows: An operator first focused onsystem performance, and then one thought about operation of the network. But the keydriver for next generation mobile radio systems is the reduction of cost and complexity.Thus, a change of the described mindset is needed where now the focus is fixed on thefollowing aspects right from the start: excellent performance and operational efficiency.For this paradigm change, the self-organizing approach is a promising solution.

A Self-organizing Network (SON) is acommunication network which supportsself-x functionalities, e.g. self-configuration or self-optimization. Self-x

enables the automation of operationaltasks, and thus it minimizes humanintervention.

Network

Monitoring


Generally self-x functionalities are basedon a loop (self-x cycle) of gatheringinput data, processing these data andderiving optimized parameterization (seeFigure 1).

Furthermore, self-x effectuates theimprovement of the usability of futurewireless access solutions (“plug&play”),and it accelerates the introduction and

deployment of new wireless services.In addition, self-organizing approachesmay contribute to further increasing

spectral efficiency, since they can be used to allocate capacity where it is needed, incontrast to today’s networks, whose design is made to handle the maximum demandexpected at any place in the covered area. Finally, self-x approaches target also onimprovement of Quality of Service (QoS) perceived by the user. Besides the increase of spectral efficiency, this is also the optimization of interference and coverage in criticalreception conditions.

Hence, the main gains of self-x are expected first of all in operational expenditure(OPEX) reductions and secondly in network performance improvements.

1 .1 SON pr i o r i t i zat i on f r om an opera to r ’ s po in t o f v i ew From an operator’s point of view, the SON features that are considered higher priorityespecially during the initial stage of deployment of a new network are in the following:

• HW & SW installation, Network authentication: with significant OPEX implicationthrough minimized factory pre-configuration, number of site visits, manualconfiguration.

• Radio parameters setup: some self configuration functionalities as AutomaticNeighbor Relation (ANR), Neighbor Cell List (NCL), PhyCID, initial RRM thresholds,etc.. This is important to get system functional with minimized OPEX.

• Radio parameter optimization and energy saving: power optimization (interferencereduction, energy saving, TX power optimization, antenna tilt), HO optimization, load

balancing, coverage and capacity optimization, etc.. The optimization of networkperformance with minimum human intervention reduces CAPEX and OPEX.

Figure 1: Basic self-x cycle

Actual NetworkParameters

New Network

Parameters

ParameterConfiguration

Optimization

S e l f - x

SON

c y

c l

e

Network

MonitoringActual Network

Parameters

New Network

Parameters

ParameterConfiguration

Optimization

S e l f - x

SON

c y

c l

e



1 .2 Di f f eren t sel f - x f u nc t i ona l i t i es SON is considered along its different manifestations addressed as self-x; this issummarized in Figure 2. Several research activities in this field include initial support of primary self-capabilities, such as the self-configuration and -optimization on radionetworks dealing both with the user device and the infrastructure equipment side [3].

Self-organization

Self-planning

Self-optimization

Self-managing

Self-healing

Self-configuration

…

Self-x

Self-organization

Self-planning

Self-optimization

Self-managing

Self-healing

Self-configuration

…Self-planning

Self-optimization

Self-managing

Self-healing

Self-configuration

…

Self-x

Figure 2: Different manifestations of self-organization

• Self-configuration: Self-configuration process is defined as the process wherenewly deployed nodes are configured by automatic installation procedures to get thenecessary basic configuration for system operation. This process is performed in pre-operational state. The pre-operational state is defined as the state where the RFinterface is not commercially active [4]. After the first initial configuration, the nodeshall have sufficient connectivity towards the network to obtain possible additionalconfiguration parameters or software updates from the network to get into fulloperation.

• Self-planning: Self-planning could be considered as a particular situation of the self-configuration mechanisms. It comprises the processes were radio planning

parameters are assigned to a newly deployed network node. Parameters in scope of self-planning are (1) Neighbor cell relations; (2) Max TX power values of UE andeNodeB; (3) HO parameters, hysteresis, trigger levels, etc..

• Self-Optimization: Self-optimization is defined as the process where UE and basestation measurements and performance measurements are used to auto-tune thenetwork. The tuning actions could mean changing parameters, thresholds,neighborhood relationships, etc. The main benefits of self-optimization will be (1)Operational effort minimization; (2) Quality and performance increase; (3) Planningeffort and failure reduction. This process is accomplished in the operational state. Theoperational state is defined as the state where the RF interface is commerciallyactive.

• Self-managing: Self-managing is the automation of Operation and Maintenance

(OAM) tasks and workflows, i.e. shifting them from human operators to the mobilenetworks and their Network Elements. The burden of management would rest on amobile network itself while human operators would only have to provide high levelguidance to OAM. The network and its elements can automatically take actions basedon the available information and the knowledge about what is happening in theenvironment, while policies and objectives govern the network OAM system.

• Self-healing: Self-healing is a SON functionality which detects problems itself andsolves or mitigates these problems to avoid user impact and to significantly reducemaintenance costs. For each detected fault, appropriate alarms shall be generated bythe faulty network entity. So the trigger of Self-healing is alarm. The Self-healingfunctionality monitors the alarms, and when it finds alarm/s which could be solvedautomatically, it gathers more necessary correlated information (e.g. measurements,

testing results, etc) and does deep analysis, and then according to the analysisresult, it triggers appropriate recovery actions to solve the fault automatically.





2. Research, Standardization and Industry Foraactivities

The current framework of a SON is governed by the activities deployed under severalresearch initiatives which try to develop the appropriate techniques for the automation of management and optimization tasks in the radio access network. The presence of theself-x mechanism becomes especially important for RAN Long Term Evolution, since theincrease of the network performance and quality reacting to dynamic processes in thenetwork would be one of the main targets. Standardization efforts are demanded todefine the necessary measurements, procedures and open interfaces to support betteroperability under multi vendor environment. The work within standardization bodies iscomplement by industry fora initiatives that expect to provide a coherent view of whatthe operator community is going to require to the next generation technology.

2 .1 Sel f -x ac t i v i t i es in t he CELTI C pr o j ec t Ganda l f The aim of the CELTIC Gandalf Project [8] was to employ large-scale networkmonitoring, Advanced Radio Resource Management (ARRM), parameter optimization andconfiguration management techniques in order to achieve automation of networkmanagement tasks in a multi-system environment. These include techniques to collectand process network data on a large scale in order to produce key performanceindicators allowing identifying malfunctions and to dynamically propose and performhealing actions. To optimize Quality of Service delivery and overall systems performancein a multi-system environment, the project proposes new radio resource managementalgorithms together with methods for self-tuning.

The feasibility of the multi-system self-tuning concept and the viability of the ARRM andauto-tuning concept have been demonstrated through network simulations and hardwaredemonstrations (multi-system testbed).

The management functionalities, considered in the Gandalf project, have been:

• Advanced-(for distinct Radio Access Network) and Joint- (for inter-system) RRM:Auto-tuning of ARRM and JRRM parameters have been studied in the context of FuzzyInterference System (FIS). To improve FIS performance, it has been optimized usingReinforcement Learning techniques.

• Self-tuning for both on-line and off-line optimization: The optimization functionsderived optimal parameters to improve network performance.

• Troubleshooting: Automated diagnosis techniques using a reasoning mechanismbased on Bayesian Networks.

2 .2 Sel f -x in t he FP7 p r oj ect SOCRATES The SOCRATES (Self-Optimization and self-ConfiguRATion in wirelEss networkS) project[23] is supported by the European Union under the 7th Framework Program, and will runfrom 1 January 2008 until 31 December 2010.

The general objective of SOCRATES is to develop self-organization methods in order tooptimize network capacity, coverage and service quality while achieving significant OPEX(and possibly CAPEX) reductions. Although the developed solutions are likely to be morebroadly applicable (e.g. to WiMAX networks), the project primarily concentrates on3GPP’s LTE radio interface (E-UTRAN). In more detail the objectives are as follows:

• The development of novel concepts, methods and algorithms for the efficient andeffective self-optimization, self-configuration and self-healing of wireless accessnetworks, adapting the diverse radio (resource management) parameters to smooth

or abrupt variations in e.g. system, traffic, mobility and propagation conditions.Concrete examples of the radio parameters that will be addressed include: power



settings, antenna parameters, neighbor cell lists, handover parameters, schedulingparameters and admission control parameters.

• The specification of the required measurement information, its statistical accuracyand the methods of information retrieval including the needed protocol interfaces, in

support of the newly developed self-organization methods.• The validation and demonstration of the developed concepts and methods for self-

organization through extensive simulation experiments. In particular, simulations willbe performed in order to illustrate and assess the established capacity, coverage andquality enhancements, and estimating the attainable OPEX (/CAPEX) reductions.

• An evaluation of the implementation and operational impact of the developedconcepts and methods for self-organization, with respect to the operations,administration and maintenance architecture, terminals, scalability and the radionetwork planning and capacity management processes.

• Influence on 3GPP standardization and NGMN activities.

2 .3 I EEE 80 2.16 Since 2007 the new Task Group IEEE 802.16m (TGm) has been working on an advancedair interface to meet the requirements of next-generation mobile networks (4G). Thus, afew specific IMT-Advanced targets are mentioned, such as 100 Mbit/s for high mobilityand 1 Gbit/s for low mobility. Completion of the work is expected in November 2009.

TGm has been developing the following documents as part of the IEEE 802.16mstandardization process:

• System Requirements Document (SRD) [2]: Completed

• Evaluation Methodology Document (EMD) [52]: Completed

• System Description Document (SDD) [25]: Draft

At the end of 2008 they will start with:• 802.16m Amendment (detailed specification)

• 802.16 IMT-Advanced Proposal

One of the operational requirements by IEEE 802.16m is the support of self-organizingmechanisms [2].

Figure 3 shows the protocol structure of IEEE 802.16m. The self-organizing block islocated in the radio resource control and management (RRCM). Details are currently indiscussion for the SDD [25].

Network Layer

Physical Layer

ConvergenceSublayer

Data PlaneContol Plane

MAC

RRCM

MAC: Medium Access ControlRRCM: Radio Resource Control & Management

Self-organizationNetwork Layer

Physical Layer

ConvergenceSublayer

Data PlaneContol Plane

MAC

RRCM

MAC: Medium Access ControlRRCM: Radio Resource Control & Management

Self-organization

Figure 3: The IEEE 802.16m protocol structure [25]




2 .4 NGMN The NGMN Project is an initiative by a group of leading mobile operators to provide avision of technology evolution beyond 3G for the competitive delivery of broadbandwireless services to increase further end-customer benefits. Since 2006, this initiativeintends to complement and support the work within standardization bodies by providinga coherent view of what the operator community is going to require in the decadebeyond 2010.

Their mission is to provide a set of recommendations intended to enhance the ability of the mobile operators to offer cost-effective wireless broadband services to theircustomers. One of the functional features of mobile networks considered by NGMN askey is the SON concept. Among the strategic technical projects developed by theirworking groups there is one fully dedicated to support and facilitate the enabling frominitial deployment of highly effective automated self-optimizing functionality and self-organizing mechanisms such as self-configuration of all nodes [1].

The main achievements:

• Description of the operational use cases, where automatic or autonomous proceduresare expected to be introduced. The operator use cases are categorized into fourgroups [10], see. Figure 4.

• Definition of high level requirements for what type of performance counters andconfiguration parameters are needed. During the standardization of a new system(i.e LTE) it is necessary to specify the measurements, procedures and open interfacesto support self-organizing functions in a complex multi vendor environment.

• Comparison of SON architectures: Centralized versus distributed. Pros and cons areevaluated and proposed for discussion.

• Recommendation on SON requirements [11]: After SON use cases foreseen byoperators were outlined in [10], recommendations and guidelines on requirementsfor implementation of solutions to support SON use cases have been proposed. Thevendor criteria and their contributions have also been considered.

SON Use Cases

Planning Deployment Optimization Maintenance

NodeB Location

NodeB HardwareConfiguration

NodeB RadioParameter

Network Integration

NodeB TransportParameter

aGW / OMCParameter

Hardware Installation

NetworkAuthentication

Software installation

Transport ParameterSetup

Radio ParameterSetup

Testing

Radio ParameterOptimization

Transport ParameterOptimization

Hardware Extensionand/or Replacement

Software Upgrade

Network Monitoring

Failure Recovery

SON Use Cases

Planning Deployment Optimization Maintenance

NodeB Location



Network Integration


aGW / OMCParameter






Testing




Software Upgrade

Network Monitoring

Failure Recovery

NodeB Location



Network Integration


aGW / OMCParameter






Testing




Software Upgrade

Network Monitoring

Failure Recovery

Figure 4: Categories and sub-groups of SON related use cases [10]




2 .5 3GPP The 3GPP activity on 3G Evolution has being focused in setting the objectives,requirements and targets for LTE in an initial phase, and later on in providing a completedescription of all the technical aspects involved (multiple access techniques, architecture,services, etc). All these concepts are being collected and will lead the definition of thestandard 3GPP Release 8. In order to reduce the operating expenditure (OPEX)associated with the management of the necessary larger number of nodes from morethan one vendor the concept of the SON is introduced. In 3GPP Release 8 many of thesignaling interfaces between network elements are standardized (open) interfaces.Significant examples in the context of SON are the X2 interface between eNodeBs andthe S1 interface between eNodeB and the EPC (e.g. MME, SGW). On the other hand, for3GPP Release 8 it has been decided that SON algorithms themselves will not bestandardized. Several working groups propose contributions on this subject, that arediscussed and finally relevant conclusions are extracted.

In [4], the scope of self-configuring and self-optimizing functionality is defined. Bothprocesses are described as well as the functions handled by them (see also Figure 5).

One of the most important deliverables related to SON topic will be the TR 36.902 [12].The WG responsible for that is RAN WG3. This technical report deals with self-configuringand self-optimizing network use cases and solutions. The latest contributions that havebeen presented were during last RAN WG3 meeting, in August 2008. In this document itis expected that the use cases more relevant to SON would be described. For each usecases, the following sections will be completed: (1) Use case description; (2) Input data,definitions of measurements or performance data; (3) Output, influenced entities andparameters; (4) Impacted specifications, procedure interactions and interfaces.

The WG SA5 is also developing SON topics, more related to conceptual approach andrequirements. The deliverable TS 32.500 [7] is intended for the description of therequirements and architecture for the SON functions within the OAM system. In this

deliverable additional use cases are presented.

Basic SetupConfiguration of IP address

and detection of OAM

Authentication of eNB/NW

Association aGW

Downloading of eNB software(and operational parameters)

Neighbor list configuration

Coverage/capacity relatedparameter configuration

Neighbor list optimization

Coverage & capacity controlSelf-optimization

Self-configuration

Optimization /

Adaptation

Initial RadioConfiguration

O

p e r a t i o n a l s t a t e

P r e - o p e r a t i o n

a l s t a t e

Basic SetupConfiguration of IP address

and detection of OAM

Authentication of eNB/NW

Association aGW

Downloading of eNB software(and operational parameters)

Neighbor list configuration

Coverage/capacity relatedparameter configuration

Neighbor list optimization

Coverage & capacity controlSelf-optimization

Self-configuration

Optimization /

Adaptation

Initial RadioConfiguration

O

p e r a t i o n a l s t a t e

P r e - o p e r a t i o n

a l s t a t e

Figure 5: Ramifications of self-configuration/self-optimization functionality at an eNB





3. E3 approachThe E3 (End-to-End Efficiency) project [24] is supported by the European Union under

the 7th Framework Program. It has been started on 1 January 2008, and has a durationof 2 years. The project is evolving current and future heterogeneous wireless systeminfrastructures into an integrated, scalable, efficiently managed Beyond 3G (B3G)cognitive system framework. E3 brings together European key players in the domain of cognitive radios and networks, self-organization and end-to-end reconfigurable systems.

The key objective of the E3 project is to design, develop, prototype and showcasesolutions for optimized usage of existing and future radio access resources. In particular,more flexible use of frequency spectrum, terminals, base stations and networks isaddressed.

This chapter deals with the aspects of the current work on self-x in E3. Self-x is one topicin E3, but not the only one, within a B3G cognitive wireless system framework. Incontrast the FP7 project SOCRATES (see subchapter 2.2) has a narrower perspective.

SOCRATES considers only self-x functionalities and these primarily for the 3GPP LTEradio interface. Thus E3 and SOKRATES complement one another.

3 .1 Di f f eren t aspects o f sel f - x f un c t i ona l it i es This section presents the approach of E3 dealing with the aspects of self-x functionalities.The theoretical framework is analyzed as well as the problem statement from severalperspectives in terms of dynamic self-organized heterogeneous wireless networksegments, spectrum management, self-configuring protocol and self-optimized networkdevices.

3.1.1 Se l f -op t im iza t i on i n he te rogeneous w i re less ne tw orks Self-Organized Network (SON) is to support self-configuration and self-optimization of

network elements. In LTE system [26], the SON conceptions have been proposed toimplement the eNodeBs self-establishment and self-optimization aiming to reducenetwork operating expenditure (OPEX). In future wireless world, there are a largenumber of reconfigurable network elements in access networks resulting to the highcomplexity of network management. In order to adapt to the complex and dynamicnetwork environment and maintain network performances, self-optimization is necessaryto enable the automatic reconfiguration of network elements. To achieve the purpose, anetwork optimization functional module (NOFM) shown in Figure 6 is deployed inheterogeneous access network as a core optimization manager which is responsible of a)Monitoring and aggregating radio access networks information, b) Modeling andanalyzing network optimization problem, c) Planning and deciding optimization strategyon network reconfiguration, d) Directing and guaranteeing the network element

reconfiguration execution and e) Storing knowledge learned from the self-optimization.To catch up the dynamic change of wireless environment, NOFM keeps monitoring onnetwork environment. Consequently, the self-optimization is operated as a closed controlloop in NOFM. The input of the self-optimization loop is the dynamic environmentinformation, while the output is network element reconfiguration strategy. The inputinformation could be aggregated from the network elements’ measurement reporting,involving the serving base stations attributes and terminal distribution, QoS demand etc.Based on the information, NOFM makes appropriate reconfiguration decision whichmaximizes the network performances.

Since the principle of reconfiguration decision is to match the network capabilities anduser demands, the decision should involve both network parameter adjustment andterminal parameter adjustment. By executing the self-optimization during the wholeoperational process, it could overcome the heterogeneity and complexity of networkmanagement and optimize network performances.




Figure 6: Self-optimization in future wireless networks [26]

3.1.2 DSNPM – A SON fu nct ion al i t y Figure 7 provides an overall description of the management functionality of DynamicSelf-organized Network Planning and Management (DSNPM) for B3G wireless networksegments including LTE [26], [27], WiMAX [28], WiFI [29] etc. The input of themanagement functionality is described herein in detail.

• Context: This component reflects the status of the elements of the network segment,

and the status of their environment. Monitoring procedures provide, for each networkelement of the segment, and for a specific time period, the traffic requirements, themobility conditions, the configuration used, and the QoS levels offered. Discoveryprocedures provide information on the QoS that can be achieved by alternateconfigurations. Context information will be used from the system to provide thecurrent view of the service area.

• Profiles: This component provides information on the capabilities of the elements andterminals of the segment, as well as the behavior, preferences, requirements andconstraints of users and applications. This information is necessary during theoptimization procedure in order to decide the most appropriate configurationconsidering current context information.

• Policies: The optimized decisions of the management functionalities should not only

be feasible from technological perspective but also have to be aligned with NetworkOperators policies and strategies. Sample rules can specify allowed (or suggested)QoS levels per application, allocations of applications to RATs and assignments of configurations to transceivers.

The optimization procedure is responsible to produce a feasible network configurationafter all aforementioned information about context, profiles and policies are taken intoaccount. In general, the strategy should find the best configurations that maximize anobjective function, which takes into account the user satisfaction, resulting from theallocation of applications to QoS levels, the cost at which QoS levels are offered, and thecost of the reconfigurations [30], [31], [32].

Because of the fact that there are a large number of possible solutions that need to bechecked a great level of delay is introduced. However, if someone pays attention to themonitoring procedures of the management infrastructure, a useful result may come up.During a certain period of time, similar contexts are captured by the system and the



optimization procedures provide near the same reconfiguration decisions as given in thepast. This is the main idea where the cognitive features in the management process arebased. As it is depicted in Figure 7, DSNPM is enhanced with learning procedures. Infact, this will yield knowledge and experience, and therefore enable faster and more

reliable management decisions [33], [34], [35]. Though this learning procedure DSNPMis capable to achieve the following targets: a) remember encountered contexts andcorresponding solutions, b) remember contexts temporal info like topological aspectsand behavior in time, c) estimation of contexts transition probability, d) solutionssuitability estimation and e) given the contexts transition probabilities, DSNPM should becapable to predict contexts and act proactively in order to avoid certain problems in thenetwork.

Figure 7: DSNPM for B3G wireless network segments

3.1.3 Se lf -m anagem en t o f s pect r u m i n w i r e less ne t w o rk s Dynamic spectrum management techniques try to achieve an efficient utilization of thescarce and valuable spectral resources, trying to maximize spectrum reuse amongstusers while ensuring that mutual interference between them remains at acceptablelevels. This has been motivated by the fact that recent measurements have revealedthat, while some spectrum bands only exhibit a sporadic use, others are profusely used[14]. This fact claims for a new paradigm of spectrum access that overcomes currentregulatory and technological barriers and promotes the usage of the spectrumdynamically and opportunistically accounting for the different temporal and spatialspectrum demands. Then primary spectrum owners (e.g. operators) may concernthemselves to perform a Dynamic Spectrum Assignment (DSA) strategy to (i) maximizespectral efficiency, (ii) maintain the QoS of the users and (iii) release unused pieces of spectrum to create spectrum access opportunities and thus enhance the usage of thespectrum. This can be obtained by providing the network with the proper self-organization mechanisms to automatically react to the different temporal and spatialspectrum demands. In this way, operational costs can be reduced while adaptability androbustness is retained [16].

Figure 8 depicts the basic functionalities that should be implemented to provide thenetwork with capabilities of self-management of spectrum, following a classical cognition

cycle [15]. Network observation and analysis of the network status are required todetect the instants when current spectrum assignment is no longer valid and thenautomatically trigger the self-management spectrum strategies. Then, a new adequate




spectrum assignment to the different transmitters is decided. Finally, continuous learningwill improve the decision procedure through a suitable evaluation of the outcomeresulting from prior decision under similar conditions.

Key Performance Indicators (KPIs) are used to observe the status of the network for a

given assignment and to take the appropriate decisions. Some KPIs can be the spectralefficiency, the spatial spectrum usage, which measuring how frequencies are distributedin a geographical area and the degree of QoS fulfillment (see [18][19] for details). Basedon these KPIs, proper algorithms to decide the most suitable spectrum assignment givensome objective functions (e.g., maximize spectral efficiency, optimize QoS, etc.) shouldbe defined. Possible optimization methodologies can be genetic algorithms, simulatedannealing, swarm intelligence, heuristics, machine learning, etc. [21]. For instance, inthe case of multi-cell OFDMA, heuristic strategies [20] have shown good performancewhen compared with classical fixed assignment strategies. In turn, ReinforcementLearning (RL) techniques show appealing cognitive capabilities since they try to solve agiven problem from the continuous interaction with an environment that returns areward for each one of the RL actions [17].

K P I

Figure 8: Self-management of spectrum in a cellular wireless network

3.1.4 Se l f - con f i gu r ing p ro t oco ls One of the objectives of autonomic networking is the incorporation of adaptationcapabilities in the protocol stack of mobile devices. Inspired by the need to meetcognitive RAT connectivity requirements, remote management procedures and RATcomponent upgrade needs, the target of this work is to propose the introduction of appropriate self-x functionalities for the protocol reconfiguration realization [37],[38].

3.1.4.1 Autonomous DM for RAT selection and protocol configuration

The decision making (DM) functionality is responsible for specifying concretereconfiguration actions or specific constraints evaluating input stimuli from the

telecommunication environment. The proposed solution concerns the definition of anautonomous decision making loop, which extends the cognitive decision making loopproposed in [39]. Such loop takes into account all the available alternative actions anddecides the best of them, based on the degree of user satisfaction per alternative for aset of specified criteria. The aggregated degree of the user satisfaction is the weightedsum of the user satisfaction per performance metric/criterion related to the ongoingservice sessions and operating RAT. The proposed functionality has several advantages,also from the user perspective: a) the user can specify the importance of the criteriaaccording to his/her needs; therefore the user preferences affect the final decision of theterminal, b) the criteria are divided in classes; this way the weights of the lower prioritycriteria per alternative depend on whether each alternative satisfies the requirements of all the higher priority criteria or not and c) the users are divided in several classesaccording the services that they access the most in addition to their requirements. Anexample of the autonomic decision making loop is the following: we consider that themobile device is connected to one RAT and we assume the existence of an additional





RAT. The outcome of the autonomic decision loop specifies three alternative choicesincluding handover, reconfiguration and joint handover and reconfiguration targeting theoptimization of the following criteria: QoS, the duration of the action and the cost.

In addition, one key issue is the optimal distribution of decision operations between the

mobile device and the network. In this direction, we examine how the introduction of protocol self-configuration features in mobile devices affects the response time of thenetwork, considering the dynamic optimization of the overall system computationalresources [38]. We consider two classes of mobile devices: reconfigurable and semi-autonomous; their difference lies on the degree they support decision makingfunctionality. We investigate how the global bounds of the asymptotic network responsetime per class are affected by the number and frequency of reconfiguration decisionrequests. The results show that the global bounds of the asymptotic network responsetime for the semi-autonomous terminals are more strictly than for reconfigurableterminals. The outcomes of this analysis give us some first hints on how the increase of the autonomous level of mobile devices affects the network; specifically, the networktends to respond faster to devices with greater autonomous level [38]. The calculation of

the bounds is very important for the management of a high number of requests andmore specifically to dynamically check whether a resource reaches its full utilization andschedule proactive operations to avoid this. Such functionality can be implemented in themanagement entity of the network node, which can specify the necessary actions,including a) relocation of requests to other non-saturated network nodes and b)transmission of network policies to a group of mobile devices (i.e. idle-mode devices)indicating that reconfiguration requests should not be transmitted for a short period.

3.1.4.2 Dynamic configuration of RAT protocol components

In relation to the dynamic configuration of RAT protocol components, the following casesare considered for the derivation of the requirements [37]: a) the semantic-baseddynamic binding of protocol components - protocol stack bootstrap (such case concernsthe realization of the component binding with the use of context information – the later

is incorporated as a semantic layer of information within each protocol component).Depending on the degree of flexibility and autonomy that is considered in the system,different mechanisms for the binding realization may exist. For example, the binding canbe realized in a distributed manner by the RAT protocol components (high degree of autonomy) or in a centralized manner by management functionality. b) the dynamicconfiguration of RAT protocol components. The latter require for both intra-layermanagement functionality for a single RAT protocol layer as well as generic/cross-layermanagement functionality for the whole protocol stack. Such functionality should alsoinitiate and coordinate the dynamic configuration of protocols, achieved via eitherparameter tuning or adaptation of specific functionality. In addition, the incorporation of state management and component instantiation mechanisms are necessary to handlethe seamless transition from one protocol configuration to another without any loss of

protocol data or existing connections. The component-based approach is argued tofacilitate the optimum configuration of the protocol stack to a given environment, basedon cognitive orientation schemes. Moreover, the delay in realizing dynamic replacementof targeted components introduces minimum performance overhead to the system.

3.1.4.3 Autonomic RAT selection algorithms

Another self-x function that can be exploited in wireless networks is the capability of themobile terminals to take autonomous decisions regarding the RAT they are connected to.Traditionally, these functions have been mainly centralized because a central networknode may have a more complete picture of the radio access status. However, acentralized implementation has some drawbacks in terms of increased signaling load ortransfer delay. This prevents an efficient implementation of some functions such as

packet scheduling and explains why wireless cellular technology evolution (e.g. HSDPA)exhibits the trend towards implementing (J)RRM functions on the radio access networkedge nodes (e.g. base stations). Indeed, there is a trend towards decentralized and



autonomous (J)RRM functions in the mobile devices. This approach has claimed to beinefficient in the past because of the limited information available at the mobile deviceside. Nevertheless, this can be overcome if the network is able to provide someinformation or guidelines to the mobile device assisting its decisions. In this way, while a

mobile-assisted centralized decision making process requires the inputs from manymobile devices to a single node, the network-assisted decentralized decision makingprocess requires the input from a single node to the mobile devices, which can besignificantly more efficient from a signaling point of view. In that respect, several radioenablers can be found in the literature, such as IEEE P1900 [41], the spectruminformation channel [42], the Common Spectrum Coordination Channel (CSCC) [43] orthe Cognitive Pilot Channel (CPC) [44].

The required functionalities to execute the autonomous RAT selection at the mobileterminals have been identified in the framework of E3 project [45]. They are (i)acquisition and learning user information (to acquire information on user preferencesand behavior and device capabilities), (ii) acquisition and learning context information(so that mobile devices can get information about the network context, including

information about available RATs/operators in a given area and their status, etc.), (iii)acquisition and maintaining policy information (to derive and manage information relatedto policies, which include information about constraints to the RAT selection procedure),(iv) distributed decision making for RAT selection and protocol configuration (to decideand select the RAT that better fits its service requirements and cost constraints) and (v)awareness signaling (to provide the necessary support to exchange information betweendifferent network nodes).

In E3 project novel solutions for autonomous RAT selection are proposed. In particular,for services without stringent delay requirements (e.g. sending a number of emails,downloading an MP3 file, etc.), the mobile terminal can decide the most appropriateinstant to start data transmission based on the current context information and itsestimated future evolution. The main benefits from this approach come from the user

perspective, which can get the service under better conditions (e.g. cheaper price whileexperiencing a tolerable delay), and from the network perspective in terms of interference reduction [45].

3.1.5 Se l f -o rgan izat i on o f cogn i t i ve ne tw ork e lement s This self-organization of cognitive network elements (e.g. base stations) and mobiledevices case that have intrinsically embedded cognitive capabilities concerns their abilityto compose clusters, form collaborations and dynamically re-organize structures for aspecific goal that will optimize their local behavior and the global behavior of the networkthey participate. The introduced Self-Organization framework views topological changesas an optimization problem Figure 9, based on the following constraints that one/someelement or a network area faces: a) Energy Consumption, b) Delay and c) Throughput.

Figure 9: Self-organizing networks: Topological change as an optimization problem





The main self-organization stages are: a) the discovery of self-organization opportunitiesand the identification of available resources, b) the negotiation between the involvednetwork elements and user equipment to specify their participation and the rules of theircollaboration, c) the decision on the goal-driven optimal formation, satisfying global and

local performance metrics, and d) the control of the new formation after itsestablishment [40]. More specifically, the discovery of self-organization opportunitiesincludes the selection of the network elements or devices and thereinafter theidentification of the resources that are available to participate in the new organization.The negotiation phase specifies the rules, which govern the collaboration, based on thepolicy rules and the knowledge that is exchanged among the involved entities. If thenegotiation does not end successfully, the discovery phase is repeated, by extending thesearch area. Thereafter, the constituent elements decide in distributed way the exactorganization scheme that will be used in the new formation taking into account the re-organization agreement, and correspondingly update the policy rules and the knowledgemodels. The next step is the execution of the re-organization and the test the newstructure or their collaboration (self-test) to evaluate whether the required performancemetrics and goals are satisfied. Self-organization is a key feature of futurecommunication systems, which can be viewed as a capability that complements adaptivebehavior of communication systems and contributes towards their autonomy. Networkelements should capitalize the embedded cognition and using decentralized approachessolve traditional centralize optimization and configuration problems.

3.1.6 Se l f -op t im iza t i on o f cogn i t i ve dev i ces One of the most important features of evolving B3G/4G wireless systems is theavailability of multiple access technologies, which will allow users to enjoy wirelessservices at any time, at any place. Evidently in order to truly enhance the experience of all users, even technology agnostic ones, functionality is required, not only on thenetwork but also on user-device side, for providing the “always best connection” in atransparent manner, focusing more on requirements and preferences of individual users.In this direction, this sub-section focuses on self-optimization of cognitive devices.Cognitive devices should comprise capabilities for dynamically selecting and adaptingtheir behavior and operation, taking into account user preferences and requirements,user device environment characteristics, policies, and experience established throughmachine learning mechanisms (Figure 10). Self-optimization for cognitive devices shouldaugment devices with the following features:

• Acquiring and learning profile information [47], [48], i.e. data related to userbehavior, preferences, requirements and constraints as well as equipmentcapabilities.

• Acquiring and learning context information [49], [50]. This feature encompassesmechanisms for the device to perceive its current status and the conditions in its

present environment as well as estimating the capabilities of configurations based onmachine learning methods.

• Acquiring and managing information on policies of various relevant entities (networkoperator, etc.). A certain policy specifies a set of rules that the user device mustfollow.

• Selecting and deciding the optimal device configuration action(s) taking into accountcurrent context, profiles, policies and knowledge [51]. This functionality may betriggered either as a reaction to a situation currently encountered (such as a hot-spot) or in a proactive manner, by making use of experience obtained over time.Knowledge/experience related to the decisions made in certain problematic situationsis built over time so as be able to measure the effectiveness of a solution applied aswell as to speed up the decision process.




Figure 10: Overview of self-optimization of cognitive devices

3 .2 I m p lem enta t i on i ssues a t sel f - x use -case exam p les

Focus in previous chapters was put on E3 concepts for achieving a higher efficient usageof radio and network resources and for achieving a more efficient and highly automatednetwork management by self-organization and self-x functionality. Intention of thefollowing chapter is to show exemplarily how these E3 concepts can be put into practice.

For that purpose, three use cases have been selected for addressing a broad spectrum of network infrastructure functionality enabling self-organization, self-optimization andflexible adaptation to an E3 environment. The first one addresses the flexibility anddynamic adaptability achieved by introduction of flexible, reconfigurable base stationsw.r.t provided radio access technologies and usage of hardware and computationalresources. The second one focuses on cell self-configuration as a building block for self-organization in an E3 environment taking e.g. profit of dynamic spectrum management.Finally, the third one addresses self-healing as automated fault management and

network optimization feature.

3.2.1 Arch i t ectu re f o r m anagement o f recon f igu r ab le BS The availability of reconfigurable nodes in the networks (i.e. nodes whose hardware andprocessing resources can be reconfigured in order to be used with different RATs,frequencies, channels, etc.) will give the network operators the means for managing in aglobally efficient way the radio and processing resource pool, with the aim to adapt thenetwork itself to the dynamic variations of the traffic offered to the deployed RATs and tothe different portions of the area. Besides of that, possible OPEX and CAPEX reductioncould be obtained in network deployment. As a matter of fact this technology could alsohave an impact on the current planning processes.

As an example, it could be considered the deployment of GSM and UMTS systems in ageographical area with a network built with reconfigurable nodes. In this kind of network, the reconfigurable hardware is shared between GSM and UMTS functionalities.During the daily life of the network, it could be needed, for instance due to differenttraffic loads on the two RATs, to increase the percentage of processing resourcesdevoted to the over-loaded system while decreasing the resources given to the other(supposed under-loaded). In Figure 11, a reconfiguration example increasing UMTSresources is depicted.

Figure 11: Reconfiguration example



A possible system architecture to enable the aforesaid traffic handling mechanism isreported in Figure 12 [22]. This architecture is constituted by reconfigurable basestations, whose hardware and processing resources can be reconfigured in order to beused with different RATs, frequencies, channels, etc.. In particular, the architecture

foresees common radio control functionalities for the different access networks of eachRadio Access Technology (e.g. GSM system and UMTS system) and one or morereconfigurable base station BS1, …, BSk. Each base station (BS1, …, BSk) is a multi-RATbase station (e.g. GSM, UMTS and LTE), able to manage different systems at the sametime and to be reconfigured accordingly, and includes hardware/software reconfigurabletransceiver modules. Inside any multi-RAT reconfigurable base station, each supportedcell has its own reconfigurable hardware pool, shared among supported RATs. Inaddition, each reconfigurable base station BS1, …, BSk can be reconfigured in terms of percentage of processing resources devoted to each supported RAT and in terms of active radio resources (e.g. frequency carriers) for each supported RAT.

Figure 12: Reference network architecture [22]

The radio control functionalities include the RRM (Radio Resource Management) entitywhich aim is to manage the request and the assignment of a radio channel to the mobileterminals that are in the cells managed by the base station BS1, …, BSk. In the

reference architecture depicted above a new functionality called ReconfigurationManagement has been introduced, in order to run the reconfiguration algorithm.

3.2.2 Sel f -h ea l ing based on a use case in a rea l ne tw ork Self-optimization and self-healing functionality will monitor and analyze faultmanagement data, alarms, notifications, and self-test results and will automaticallytrigger corrective action on the affected network node(s) when necessary.

The current process presents a high component of manual intervention that could bereplaced using self-x techniques. The two major areas where the self-x concept could beapplied are: a) Self-diagnosis: Create a model to diagnose, learning from pastexperiences and b) Self-healing: Automatically start the corrective actions to solve theproblem.

Making use and analyzing data from the current optimization tools (alarm supervisionsystem, OAM system, network consistency checks), optimizers can decide if networkdegradation occurs, which is the most likely cause, and then perform the neededcorrections to solve the problem. The experience of optimizers in solving such problemsin the past, and the access to a database of historic solved problems is very useful toimprove the efficiency in finding solutions.

This whole optimization process could be enhanced in two steps

• Diagnosis model creation based on the experience of already solved problems, usinga database with faults and their symptoms (PM counters). Automatic troubleshootingaction can be done without human intervention.

• Self-test results from the periodic execution of consistency checks would help during

the self-diagnosis phase, to address better the healing process.Next, an illustrative example in a real 3G network is being described:




1. High drop call rate detected by the alarm supervision system. The automatic process

to find out the reason of this problem starts.

2. PM indicators and counters consultancy: inputs to the diagnosis tool.

3. Diagnosis tool result: Bad inter-system (3G-2G) Neighbors definition.

4. Verification of diagnosis with results from the consistency checks: a) Far awayneighbors check: This check detects neighbors not well defined because of distancereasons and b) Handover statistics check: Neighbors with handover fail rate high.

These two checks confirm the most likely cause given by the self-diagnosis tool.There is a 2G cell with a frequency defined as neighbor, but in fact, the cell ismeasuring another one much nearer, with the same frequency but not defined as 2Gneighbor.

5. Start healing actions. Based in a profile which would relate diagnosis results andcheck results, the system would start a procedure to solve the problem: a) Verifycloser cells to the analyzed one with the same frequency as the further neighbor, b)Define this cell as a new 2G neighbor and c) Send to the OSS a changes report to

update the neighborhood list.6. The system should supervise the effect of the change during a certain time period, in

order to check that the corrective action solves the problem.

7. This fault and their related counters would feedback the diagnosis model since itcould be another entry in the learning phase.

3.2.3 Add ce l l se l f -conf igu ra t ion u se case

3.2.3.1 Motivation and realization in highly dynamic E3 environments

Configuring a new cell is a basic management feature for each radio access system. Inlegacy systems, it is the result of a planning activity nowadays linked with significantmanual and tool supported configuration effort. In highly dynamic and self-managed,

cognitive environments the introduction of Flexible Base Stations (FBS) enables adynamic re-configuration and adaptation of the heterogeneous mobile system to radioenvironment for optimum efficiency and optimum provisioning of services w.r.t servicerequests and terminal capabilities. Example is an optimized (opportunistic) usage of available spectrum and assignment of available HW and computational resources. Keyfeature for such a dynamic adaptability is an automated, flexible and self-manageddeployment and self-configuration of cells (Add Cell use case), initiated by cognitivereconfiguration management decisions.

This requires new functionality for self-configuration and interfaces for interworkingbetween the cognitive decision functionality (interface 2) and the underlyingreconfigurable base station platform (interface 5) as shown in Figure 13.

Neighbour NodeBNodeB 3. Negotiations

and alignment with

neighbour cell

1. Resources and

capabilities check

Base Station Platform

CognitiveReconfiguration Management Decision Function

Base Station Platform

2. Decision to add a cell at this site

with certain properties and resources

6. Status feedback

4. Re-configured

parameters

5. Initial cell configurationparameters and state transition

to „Operational“

Add Cell Functionality

for Initial Configuration

Re- Configuration Functionality

of Operational Neighbour Cell

Figure 13: Interfaces of add cell functionality with other functional entities





Interface 2 in Figure 13 passes the dynamic cognitive decision to add a cell together withsome cell specific information. Such information comprises topological and cell specificparameters (e.g. radio access type, frequency band, power limitations and cell coveragearea) as well as the resources to be claimed for the new cell.

Interface 5 in Figure 13 needs an increased flexibility for managing the currentlyallocated platform resources for the cells in case of add/remove/modify cell operations.

3.2.3.2 Principles for add cell self-configuration

The proposed mechanism [41] is a distributed functionality for providing and collectingconfiguration information from operational neighbor cells and/or some more centrallyorganized data base providing operator policies and preferences. The idea behind is touse already optimized parameters of other, similar and operational network nodes forself-configuration. To select appropriate data sources, the situation and properties of thenew cell are compared with the properties of other cells. If they are sufficiently similar,they serve as a reference configuration for the new cell. Each parameter to be configuredis classified by the process the parameter has to be retrieved from available referenceconfigurations and by the required algorithms for calculating the initial configurationvalue. E.g., some parameters can be determined locally after self-classification, othersrequire the inspection of the neighbors to adapt the new cell to the environment, and athird group needs negotiations with the neighbors that also change the settings in theneighbors. In conclusion, self-configuration functionality should be based on componentsfor similarity detection, parameter retrieval and algorithmic post processing.

3 .3 I deas t o t he assessm ent o f se lf - x sys tem s If we look at the description of self-x and cognitive systems it is clear that it hasconsequences for testing such system. In fact there are new challenges in testing suchsystems; we call this assessment and give a short overview on differences and theprocess itself.

Self-x systems are systems that must find solutions themselves, which timely discovercontext changes. By retrieving context and through collaboration they timely apply anew control required by new context. Cognitive systems besides the fact that they areconsidered to be self-x have the ability to learn to adapt. For this kind of systemsevaluation is needed. Evaluation of self-x and cognitive systems differs from previouslyknown techniques in that it evaluates system’s adaptation and knowledge:

• Conformance testing – evaluates the point correctness of system operation. Theresults depend on specification and implementation

• Performance measurement – evaluates dynamic characteristics of system operation.The results depend on specification, implementation and environment

• Assessment – evaluates process correctness of system operation that is the

adaptation of dynamic characteristics [46]. The results depend on specification,implementation, environment and knowledge

One question that arises is: do we know how to evaluate the properties of a self-managed system? The adopted approach is a black-box assessment (no prior knowledgeabout the implementation of self-x features). The solution starts from scenarios and usecases to understand the goals of a manufacturer or operator. Based on them, test casesare derived and ordered on difficulty using expert opinions, creating an AssessmentScale. The context depicted from each test case, either real or artificial, is then appliedto the algorithm.

The evaluation of the self-x algorithms is made by analyzing the requested context andthe changes made to the context. The outcome of the assessment is either an absoluteresult: positioning of the algorithm on the Assessment Scale (“Algorithm A reaches 80%

on Assessment Suite X”); or a relative result: the comparison between 2 or morealgorithms on a common scale (Algorithm A is better then Algorithm B); or both.






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