Work Package 5 aims at combining the cognitive network framework from WP3 and the specificmanagement functions from WP4 into an integrated management system. A simulation environmentmust be designed and implemented to support a validation of the integrated management system.Validation will encompass the service scenarios and models of uncertainty defined in WP2, thecognitive network management functions developed in WP3, and the knowledge based reasoningmanagement algorithms implemented in WP4. To verify the results of the project, a set of relevanttest cases and experiments will be defined and executed either on simulated environments or realtestbeds.
Citation preview
COMMUNE Celtic Project: Deliverable D.5.1: issued on (M10) 31.08.2012
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CELTIC initiative
“Telecommunication Solutions”
Project acronym:COMMUNE
Proposal/Contract no.: CP08004
Testbeds
Workpackage Contributing to the Project Document: WP5
Deliverable Type and Security: Report
Editors: Jose Monserrat, Oscar Carrasco, Jordi Puig Bou
Abstract: This deliverable describes the specification of the COMMUNE Cognitive Network Simulator
and the requirements that the selected testbeds must fulfil
Keywords: Cognitive Network Simulator,
Tesbeds, Long Term Evolution, Machine
To Machine
Networks, Fibber To The Home
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Project Number CP08004
Document Number COMMUNE/WP5/D.5.1
Document Title
Specification of the Cognitive Network Simulator and Testbeds
Workpackage WP5
Authors
Kurjenniemi, Jose F. Monserrat, Jordi Puig, ukasz Rajewski, Joan
Meseguer Llopis, Chunrong Zhang, Sergio Perales
Reviewers
Version 1.0
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Executive Summary
Work Package 5 aims at
combining the cognitive network
framework from WP3 and the
specific
management functions from WP4 into an integrated management system. A simulation environment
must be designed and implemented to support a validation of the integrated management system.
Validation will encompass the service
scenarios and models of
uncertainty defined in WP2, the
cognitive network management
functions developed in WP3, and
the knowledge based reasoning
management algorithms implemented
in WP4. To verify the results of the project, a set of relevant
test cases and experiments will be defined and executed either on simulated environments or real
testbeds.
This deliverable describes the specification of the COMMUNE Cognitive Network Simulator and the
requirements the selected testbeds must
fulfil. With this aim, this deliverable
first summarizes the
characteristics of the available
simulation tools and testbeds and
then lists the requirements
collected from all partners in the consortium.
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Table of contents
1
INTRODUCTION ....................................................................................................................... 6
2
3GPP Long Term Evolution scenario Simulation Tools .............................................................. 7
2.1
SistelNetworks SN4G LTEA HetNet Simulator ......................................................................... 7
2.1.1
Building Blocks of the simulator ................................................................................................ 8
2.1.2
Radio Access Technologies ........................................................................................................ 9
2.1.3
Main simulator features .......................................................................................................... 13
2.1.4 Simulation
process .................................................................................................................. 19
2.1.6
Other Outputs from SN4G....................................................................................................... 25
3.1
LTE Home eNodeB Testbed ....................................................................................................39
3.2
Multimedia Session Testbed ..................................................................................................43
3.2.2
AIT SVDN P2P Testbed ............................................................................................................ 44
4
Machine to machine scenario Testbed ................................................................................... 46
4.1
Ericsson M2M Testbed ...........................................................................................................46
5.1
Telnet FTTH GPON Testbed ....................................................................................................47
5.1.1
Optical Line Terminator (OLT) ................................................................................................. 47
5.1.2
Optical Network Terminator (ONT) ......................................................................................... 49
5.1.4
GPON Tester ............................................................................................................................ 51
6
Changes to the Available Tools for the Evaluation of COMMUNE ........................................... 52
6.1
Network Management Automation .......................................................................................52
6.1.1
Scenario 1 –: Self healing in LTE Network ............................................................................... 52
6.1.2
Scenario 2 –: SON Coordination in LTE Network ..................................................................... 53
6.1.3
Scenario 3 –: Self optimization in LTE Network ...................................................................... 53
6.1.4
Scenario 4 –: Self management in FTTH Network .................................................................. 54
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6.2
Mobility ..................................................................................................................................54
6.3
Resource and QoS Mangement ..............................................................................................54
6.3.1
Scenario 7 –: Multimedia Application
Management .............................................................. 54
6.3.2
Scenario 8 –: Energy Efficient RAN Management in LTE
Networks ........................................ 54
6.4
Internet of Things and Machine to Machine ..........................................................................56
6.4.1
Scenario 9 –: Cognitive Management of IoT and M2M Networks .......................................... 56
6.4.2
Scenario 10 –: Cognitive Management of IoT and M2M Services and Applications
............... 56
7
CONCLUSIONS ....................................................................................................................... 57
8
REFERENCES .......................................................................................................................... 58
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1 INTRODUCTION
1.1 Scope of the Deliverable
The future leads towards an ambient where wireless communications will exist in every scenario of
life to provide the end user the “flexibility and choice”, to enhance the quality of life of the individual.
The vision is beyond 3G
communications, which is tending
towards a diverse wireless networking
world, where scenarios define that
the user will be able to
attain any service, at any time
on
effectively any network that
is optimised
for the application at hand. Thus creating a
future global
infrastructure, where several systems
can coexist to support transparent
endtoend
communications, in an efficient costeffective manner, raises significant research challenges in terms
of efficient use of scarce spectral resources, network deployment and virtual connectivity between
systems. Only with the development
of intelligent mechanism running
in the different network
entities it will be possible to
turn this vision into reality.
Moreover, a complete experimental
infrastructure, comprising simulation tools
and testbeds, is required to
test the next generation
cognitive protocols/algorithms in a heterogeneous networking environment.
The aim of COMMUNE project is to build an innovative solution for cognitive network management
under uncertainty. COMMUNE will seek to mitigate the practical effects of uncertainty by exploring
the latest advances in knowledge
based reasoning and other relevant
cognitive methods. This
approach is chosen due to the
intuitive applicability of these
models and their computational
efficiency. The developed COMMUNE
Management System shall be thoroughly
tested through a
combination of network trials (a proper mix of current wireless and wireline
Internet technologies)
and simulation campaigns. Special
attention will be paid to
access networks, focusing on
two
relevant scenarios: LTE and FTTH.
In the scope of this
deliverable, we target a detailed
description of the available
experimental
infrastructures, both simulation tools
and testbeds, which will emulate
the different radio access
technologies and the cognitive
operation of the network. In
addition to that, this
deliverable
summarizes the requirements of
different partners in terms of
experimentation, which allows
identifying the required updates in the experimental infrastructures.
1.2
Structure of the document
The deliverable is divided into three main parts. Firstly, Section 2 describes the LTE simulation tools.
Secondly, Section 3 and 4
focus on
testbeds. Finally, Section 5 collects all changes
required in the
available tools for the evaluation of COMMUNE cognitive mechanisms.
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2
3GPP Long Term Evolution scenario Simulation Tools
This chapter contains the description
of the available simulation
tools for the 3GPP Long
Term
Evolution scenario.
2.1
SistelNetworks SN4G LTEA HetNet Simulator
The SistelNetworks 4G (SN4G) HetNet
simulator is one of the
tools used by COMMUNE project
to
emulate the wireless network defined at the project. The scenarios, detailed at D2.1
[1] Section 4,
will provide the required inputs to configure the simulation. Section 2.1.1 gives us a brief description
of the simulator.
The scenario inputs should be
mapped to SN4G input variables.
At this point, it is important
to
identify the critical inputs: users’
technology and femtocellular capabilities,
type of service –VoIP,
www, etc– and
the quality of service (QoS)
requirements. Moreover, SN4G needs some additional
variables defined at this section.
Each scenario entails different
situations and, hence, the type
of traffic should be different.
The
simulator has to identify the
most appropriate traffic models
(2.1.3.4) for each service at
each
situation.
All the scenarios can be
modelled with the same variables,
for this reason, they should
be
configurable inputs to the SN4G simulator. These inputs are introduced in the simulator using an init
file. The init file has
to be modified by
the user each time a new
scenario is tested. The variables
included in the
init file are detailed at point 2.1.4.1. Currently, SistelNetworks
is developing a web
based GUI
that will simplify the settings of a new simulation experiment. This GUI will be adapted
according to the project needs.
Figure 1 shows several inputs and the outputs that SN4G that are planned to be used for COMMUNE
project.
SN4G is a novel, ambitious and
scalable radio simulation platform
for heterogeneous wireless
systems initially developed by the
Universidad Politécnica de Valencia
in collaboration with
SistelNetworks. The platform currently
integrates five advanced system
level simulators, emulating
the GPRS (General Packet Radio
Service), EDGE (Enhanced Datarates
for GSM/Global Evolution),
HSDPA (High Speed Downlink Packet
Access), WLAN and LTE (Long
Term Evolution) Radio Access
Technologies (RATs). SN4G
is a unique dynamic
simulation platform that emulates all
five RATs in
parallel and at the packet level, which enables an accurate evaluation of many output variables that
could be defined, including the
final user perceived QoS through
the implementation of advanced
RRM mechanisms. The radio
interface specifications of these five
technologies have been faithfully
implemented in the SN4G simulation platform, which works with a high time resolution (in the order
of milliseconds). This modelling approach validates the capability of the SN4G simulation platform to
dynamically and precisely evaluate the
performance of RRM techniques,
so important for the
evaluation of the actual
behaviour of technologies. The
platform has been developed following
a
modular and scalable design, which guarantees an easy adaptation of the platform configuration to
specific requirements, and allows the rapid integration of new functionalities.
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Number of
highpriority
2.1.1
Building Blocks of the simulator
The main building blocks of the simulator can be seen in figure 2, where every box shown represents
a software module, that models or implements one precise feature of SN4G.
Link Control
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For its special interest, a
logical structure of the LTE
simulation platform is shown in
Figure 2.
Interactions among functional entities
are shown as connections among
blocks in Figure 2. Main
features of each
functional entity have been
included within the block representing
this functional
entity. The components shown in
this figure and their interactions
are described in the following
subsections.
2.1.2 Radio Access Technologies
The main features of the radio interfaces emulated in SN4G are briefly summarized in this section:
GPRS
The GPRS radio interface is based on a combined FDMA/TDMA multiple access mechanism and a FDD
scheme. The GPRS standard can be modelled as a hierarchy of logical layers with specific functions.
Prior to transmission, data packets
are segmented into smaller data
blocks across the different
layers, with the final logical unit being the Radio Link Control block (RLC) which has duration of 20ms.
The resulting RLC data blocks
are then coded and blockinterleaved
over four normal bursts in
consecutive TDMA frames. Although GPRS
is based on a single modulation scheme,
it defines four
different coding schemes (CS) (see Table 1) that have all been emulated within SN4G.
A GPRS TDMA frame
is equal to 4.615 ms and
is divided
into eight 0.577 ms timeslots. Such time
slots impose the SN4G time
resolution for the GPRS radio
interface. GPRS defines a
temporal
hierarchy with higher order
structures such as super and
hyperframes that have not been
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GPRS TRANSMISSION MODES
MODE MODULATION CODE RATE
BITS PER RADIO BLOCK
BIT RATE (KBPS)
CS1 GMSK 1/2 181 9.05
CS2 GMSK Aprox 2/3 268
13.4
CS3 GMSK Aprox 3/4 312
15.6
CS4 GMSK 1 428 21.4
Table 1 GPRS transmission modes
EDGE
The EDGE radio interface is
based on the same multiple
access scheme as GPRS, but
considers
different transmission modes (Modulation and Coding Schemes, MCS), all implemented in the SN4G
platform following the description in
Table 2. The main difference to
GPRS is the introduction
of
8PSK, a multilevel modulation that theoretically increases EDGE data rates by a factor of three.
The EDGE transmission modes are
divided into three different
families, namely A, B and C.
Each
family has a different basic payload unit of 37 (and 34), 28 and 22 octets respectively. Different code
rates within a
family are achieved by
transmitting a different number of payload units within one
radio block. For families A and B, 1, 2 or 4 payload units can be transmitted per radio block, while for
family C, only 1 or 2 payload units can be transmitted. These families are designed to allow a radio
block to be retransmitted with a transmission mode, within the same family, different from that used
in the original transmission; this option is not possible in the current GPRS standard. A block received
in error can be resegmented and retransmitted using a more robust transmission mode within the
same transmission family.
The GPRS and EDGE
transmission procedures are very
similar, although some differences
for high
order modes are appreciated. When
4 payload units are transmitted
(MCS7, MCS8 and MCS9),
these are split into two separate blocks. These blocks are in turn interleaved over only two bursts, for
MCS8 and MCS9, and over
four bursts for MCS7. All
the other MCSs can only
transmit a single
block that is interleaved over four bursts. When switching to MCS3 or MCS6 from MCS8, three or
six padding octets are, respectively, added to fill a radio block. Identically to GPRS, the transmission
of a whole EDGE radio block requires 20 ms.
EDGE transmission modes
BITS PER RADIO
MCS3 GMSK 0.85 A pad.
1 x 272 13.6
A 1 x 296 14.8
MCS4 GMSK 1.00 C 2 x 176
17.6
MCS5 8PSK 0.37 B 2 x 224
22.4
MCS6 8PSK 0.49 A pad.
2 x 272 27.2
A 2 x 296 29.6
MCS7 8PSK 0.76 B 4 x 224
44.8
MCS8 8PSK 0.92 A pad.
4 x 272 54.4
MCS9 8PSK 1.00 A 4 x 296
59.2
Table 2 EDGE transmission modes
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HSDPA
HSDPA is based on a CDMA multiple access scheme and considers both a FDD and TDD component,
although SN4G only emulates
the FDD one.
The FDD mode operates at a
chip rate of 3.84 Mcps,
which results in an approximated
bandwidth of 5 MHz. In the
time domain, a Transmission
Time
Interval (TTI) of 2 ms is defined. A TTI is further divided into three 667 pts slots. In the code domain,
channelization codes at a fixed spreading factor of 16 are used. Multicode transmission to a single
user during a TTI is also allowed.
Current version of HSDPA
implemented in SN4G (Release 6)
achieves high data rates of
up to 14
Mbps by means of adaptive modulation and coding
(AMC), fast scheduling mechanisms
(each TTI)
and a powerful Hybrid ARQ
mechanism. AMC or Link adaptation
(LA) is a process of
paramount
importance to optimize system functioning. Its operation
is based on user equipment reporting the
channel state either cyclically or
in a triggeredbased manner by
means of the Channel
Quality
Indicator (CQI). The definition and processing of the CQIs is explained in detail in [2]. Numerically, CQI
varies from 1 to 30, increasing
its value when the channel
quality augments. To model the
radio
channel quality, the simulator
considers several lookup tables
(LUT) matching the SINR (Signal
to
Interference Ratio) as a function of the BLER (Block Error Rate); in particular, one for each CQI such
that the maximum CQI can be calculated considering a specific QoS. These LUT also include the effect
of the HARQ retransmission with chase combining.
The available number of codes
has also been carefully taken
into account and, in the same
way,
power consumption of all the
control channels has been considered
to determine the available
power per user. Assuming code
multiplexing of n users per
TTI, n HSSCCH channelization
codes
should be allocated, whereas the
available power is equally divided
among the n users. The
maximum number of HSSSCH codes has been set to 4.
WLAN
Current WLAN standards do not
contemplate the same level of
radio resource management
functionality than mobile systems.
However, extensions that support a
more advanced RRM
framework have been developed in
standardization bodies. In this
context, SN4G implements the
802.11e specification, which provides
more advanced MAC mechanisms to
support QoS. This
standard specifies two access mechanisms, the Enhanced Distributed Channel Access (EDCA) and the
HCF controlled channel access
(HCCA). According to the literature,
the optimum system operation
corresponds to the case in which both access mechanisms work together, and this is the philosophy
followed in SN4G.
At the physical layer, both WLANs 802.11 b/g versions have been implemented. Table 3 summarizes
the
list of properties for both specifications.
In SN4G, user equipments are simply characterized by
the receiver sensitivity (S) and the transmission power which has been set to 100 mW (20 dBm).
LTE
The Long Term Evolution (LTE)
radio interface
is based on OFDMA multiple access mechanism and
can be deployed in FDD or TDD. However, our implementation is restricted to FDD mode.
User data are transmitted
from the eNodeB to the User Equipments
(UEs) using a shared channel.
Fast scheduling is performed in
the eNodeB in order
to decide how to distribute
shared downlink
resources (time,
frequency and power) among
the UEs. For each one of
the receivers, one or two
(with MIMO) transport blocks (TBs)
can be transmitted in each 1ms
transmit time interval (TTI),
which is the minimum required
simulation resolution. These TBs can
be transmitted according to
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WLAN PHY MAIN CHARACTERISTICS
(MBPS)
OFDM
Table 3 WLAN PHY main characteristics
The LTE simulator (Release 8) presents some specific features:
New link to system model
based on the effective SINR
calculation. An effective SINR
is
calculated based on a sampling of the SINR
in frequency domain using previously calibrated
functions. Then, this effective SINR value is mapped to a BLER value using LUTs obtained for
AWGN channels. All the required information has been simulated.
As LTE is based on OFDM, the scheduling is in frequency, space and time.
CQI (Channel Quality Indicator)
presents a different definition in
LTE. Besides, other
indicators, namely PMI (Precoding Matrix Indicator) and RI (Rank Indicator), are present and
useful for MIMO operation.
One or two TBs can be transmitted in each TTI, so up to two HARQ processes can be active at
the same time.
The next table (Table 4) shows the possible CQI
indexes, and the corresponding modulations, code
rates and efficiencies. In LTE, there are more possible transmission modes but in order to reduce the
number of cases, only the
modes proposed in [3] are
considered in the simulator. After
the
measurement and evaluation of the radio channel conditions, the UE (User Equipment) selects a CQI
index. This selection entails that the UE is able to receive information with the modulation, code rate
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CQI TABLE
0 out of range
1 QPSK 78 0.1523
2 QPSK 120 0.2344
3 QPSK 193 0.3770
4 QPSK 308 0.6016
5 QPSK 449 0.8770
6 QPSK 602 1.1758
7 16QAM 378 1.4766
8 16QAM 490 1.9141
9 16QAM 616 2.4063
10 64QAM 466 2.7305
11 64QAM 567 3.3223
12 64QAM 666 3.9023
13 64QAM 772 4.5234
14 64QAM 873 5.1152
15 64QAM 948 5.5547
Table 4 CQI table
2.1.3 Main simulator features
Figure 4 shows the scenario modelled by the SN4G platform which includes the GPRS/ EDGE, HSDPA,
WLAN and LTE radio interfaces. As shown in the figure, the SN4G platform does not only model the
radio interface of the four technologies but also implement various RAT specific RRM features and a
centralized CRRM
(Common RRM) entity. This entity directly collects specific RAT
information (e.g.
load, channel quality conditions, etc) and interacts with the RRM entities implemented at each RAT.
The main components of SN4G simulator, their features, interactions and data flow are described in
the following subsections.
2.1.3.1 Cellular environment
The Cellular Environment entity is a system module storing the location of each base station and the
interfering relations among them;
this information is needed to
estimate the experienced
interference levels. The cellular
layout can be modified offline
at any time to change the
system
configuration under study. In order
to avoid border effects, a
wraparound technique has been
applied. Moreover, sectorized cell sites can be modelled.
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Figure 4 SN4G heterogeneous scenario
For the COMMUNE project, in order to simulate HetNets would be possible to define network layers,
specifying the number
of micro/pico/femto base stations
per macrocell, allowing realistic
analysis
that includes different types of radio channels, depending on the network layer.
2.1.3.2 Radio Link
This module models the radio
propagation conditions between transmitter
and receiver and is a
generic entity employed by any wireless link established. It characterizes the three radio propagation
effects, namely path loss, shadowing and fast fading.
Depending on the technology, the radio
link modelling is different.
In the case of LTE,
the channel
model is the one proposed by the ITU in the M.2135 document [4].
For GSM and UMTS the path loss (in dB) reported in [5] has been considered:
80log21log18log)101640( 101010
f hd h L bb p
Where d is the distance in km between the base station transceiver and the mobile terminal, f is the
carrier frequency
in MHz and Δhb
is the base station antenna height, measured
in meters from the
average roof top
level. Typical values
for these parameters are Δhb = 15m, f =2000 MHz
for HSDPA
and f =1800 MHz for GPRS and EDGE.
For WLAN, the following path loss model has been implemented [6]:
10( ) 145 35log p L dB d
The shadowing loss has been modelled as a random process with a normal distribution of mean 0 dB
and standard deviation of 6 dB
for urban and suburban environment. The shadowing
is a spatially
correlated process so that the
shadowing loss experienced by a
mobile at a given position
is
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Fast fading modelling is also
important when considering technologies,
such as EDGE and HSDPA,
which base their radio operation
on link adaptation techniques [8].
In these RATs, transmission
conditions are modified depending on the current channel characteristics. For the sake of simplicity,
in SN4G only a simple block
fading model is considered, i.e.
the fast fading stays
constant over a
coherence time interval and each sample is statistically independent. Hence, in addition to the path
loss and shadowing value of
each radio block, in SN4G a
third multiplicative factor is
considered
when determining the received carrier:
the fast fading coefficient. This
factor
is of unit mean and
follows the probability density function:
1( ) ( ) ( 1)!
M
Here, M denotes the number
of resolvable independent multipaths
at the receiver. In GPRS
and
EDGE M is set to 1 and in HSDPA M=3.
2.1.3.3 Base Station
The Base Station entity
is responsible
for the Medium Access Control (MAC) and RRM
functions. It
also controls the channel pool where the status of all channels per RAT
is maintained. In the Base
Station is also located the session generation process. Once a new mobile is active in the system, the
CRRM entity chooses its initial RAT depending on a specific policy. When a mobile station requests a
channel from a given RAT, the channel pool of the serving base station is examined to search for an
available channel. If a free channel is available on the requested RAT, the mobile station is assigned a
randomly chosen channel or based on some quality metrics [9]. If a free channel
is not available on
the requested RAT, the mobile station is assigned a channel from a different RAT, depending on the
CRRM scheme under consideration, or
it is placed in a
queue until a
transmitting mobile ends its
transmission and releases its channel. For users in GPRS and EDGE queues, a FirstCome FirstServed
(FCFS) scheduling policy
is applied so that channel requests are satisfied
in the same order as they
appear. Users
in the HSDPA/LTE queue can be served either
in a round robin fashion, according to
the Max C/I criterion, which
selects at any moment the user
with better transmission quality,
or
following the proportional fair algorithm. In WLAN, realtime traffic is delivered through HCCA with a
FCFS policy, whereas best effort
users mutually contend to get
the channel control being
served
using the EDCA protocol.
Apart from the scheduling, other implemented RRM functionalities include Link Adaptation for GPRS,
EDGE, HSPDA, WLAN and LTE, multichannel operation for GPRS and EDGE, multicode allocation
in
HSDPA, multiResource Block allocation in LTE and call admission control in all technologies.
Link adaptation (LA), also referred to as Adaptive Modulation and Coding (AMC) in HSDPA and LTE, is
an adaptive RRM technique that periodically estimates the channel quality conditions and selects the
optimum transmission mode based on
a predefined selection criterion. For
web browsing, the
transmission mode that maximizes the throughput is selected. For H.263 video service, in GPRS and
EDGE the algorithm proposed
in [10] has been used since
it outperforms the former
in several key
aspects affecting realtime operation. In the case of multichannel transmissions, the channel quality
conditions are estimated over all channels simultaneously assigned to a single user and their average
value is used to estimate the
optimum transmission mode according
to the established selection
criterion. In HSDPA and LTE, the mobile directly reports its channel conditions to the base station by
means of the CQI. With this information, the base station knows the maximum number of codes or
resource blocks
to be allocated as well as
the modulation and coding
scheme. The final allocation
shall always be as higher as possible but not exceeding the transmission mode reported by the user.
Automatic Rate Fallback (ARF) is the implemented LA algorithm for WLAN; ARF and other algorithms
with similar operating
concepts have been widely implemented
in many WLAN products although
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measuring the numbers of consecutively successful and failed transmissions. The sender adjusts
its
modulation mode and data rate in accordance with these measurements.
Multichannel, multicode and multiresource block operation
is considered
in the SN4G simulation
platform for the GPRSEDGE, HSDPA and LTE radio interfaces, respectively. The number of channels,
codes or resource blocks that a base station can simultaneously allocate to a single user depends on
factors such as the capability of the terminal, the system load, the availability of radio resource, the
requested service type and the considered multichannel allocation policy.
In the current implementation, CRRM
techniques can be also included.
These CRRM mechanisms
base their RAT selection on
utility functions and operating
parameters, such as the RAT
load,
required service and its QoS parameters, interference levels and effect on active transmissions, etc.
The RAT selection can be performed for each new session, periodically, or every time a new packet is
generated. It is important to highlight that RAT changes are done dynamically in the SN4G platform
and that the radio transmission
can be immediately resumed with
the newly selected RAT at
the
stage where the radio transmission
ended using the previous RAT.
The platform has also been
prepared to consider the case in which a user handles different application sessions through various
RATs.
2.1.3.4 User traffic behavior
User traffic demands are usually described at two
levels: sessionarrival process and traffic models.
Sessionarrival processes, also
referred as
traffic generation, are usually modelled as a birthdeath
process, which can be characterized by
the
following parameters: busy hour call attempts
(BHCA),
arrival distribution, mean session
duration, duration distribution, etc.
On the other hand traffic
models describe the source behaviour within a session. They vary depending on the type of service
and they can be described by parameters such as: average active/inactive times, time distributions,
data rate, packet
length distribution, etc.
In SN4G, the sessionarrival has been implemented at the
base station, while the traffic models are controlled by the mobile station for optimizing the code.
Three different services have been implemented in SN4G, namely web browsing and realtime H.263
video transmissions. Cellular subscribers are usually considered to have
independent behaviour one
from each other, which results
in exponentially distributed
intersession arrival times. For each one
of the implemented services a
specific intersession time is
defined, which allows controlling
the
traffic load.
The web browsing service follows the model described in [11]. It follows an ON/OFF pattern where a
web browsing session starts with the submission of a web page request by the user. The time interval
needed to transfer the requested
web page is referred to as
active period. When the transfer
is
completed, the user will take some time to read the
information before
initiating another request.
This time corresponds to the
inactive period. The implemented
model is based on the HTTP
1.0
standard where a different TCP connection is established for the transmission of each object in a web
page. In this case, the active ON time has been considered as the time needed for the transmission of
a single object of a web page, while the active OFF time represents the time elapsed between closing
a TCP connection and opening a
new one to transfer another
object of the same web
page. The
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PARAMETERS OF THE WEB BROWSING TRAFFIC MODEL
PARAMETER
MATHEMATICAL
DISTRIBUTION
a
( ) k
Table 5 Parameters of the web browsing traffic model
Realtime services have also been included in SN4G through the emulation of realtime VoIP [4] and
H.263 video transmissions following the model presented in [12]. This last model takes into account
the three different frame
types considered in the H.263
standard, namely
I, P and PB. The model
characterizes the size and duration of the video frames, the correlation between both parameters for
each video frame, and the transition probability between different video frame types. The modelling
is performed at two levels. The
first one establishes the frame
type to generate. Iframes are
periodically created, while a Markov
chain drives the transition
generation between P and PB
frames. Once the frame type is selected, the model determines the size and the duration of the video
frame to be transmitted. The reader is referred to [4] and [12] for a detailed analytical described of
the realtime VoIP and H.263 video traffic model, respectively.
2.1.3.5 Resource Model
The resource model entity is
implemented at the mobile station
and is basically responsible
for
controlling the radio transmission
parameters of a channel
currently assigned to a user
and for
estimating the experienced channel quality conditions,
in this case carrier to
interference ratio. For
WLAN systems only the received power
is calculated since it is the only value needed to obtain the
maximum bit rate to be allocated.
2.1.3.6 Link Management
The Link Management module is
responsible for handling the radio
transmission and emulating
channel errors.
Transmission Process
GPRS controls the radio transmission
of RLC blocks through an
Automatic Repeat reQuest (ARQ)
protocol, described in specification 3GPP TS 04.60, that is implemented in SN4G. This ARQ protocol is
based on the numbering of the blocks and a selective repeat principle with sliding transmitting and
receiving windows. The transmitting and receiving ARQ windows have a size of 64 RLC blocks. The
reporting period, which defines how
regularly the receiver sends
acknowledgment messages, has
been set to 16 blocks. No
block losses and errors on the
transmission of the
acknowledgement
messages have yet been considered
in SN4G. A similar ARQ
protocol has been implemented
for
EDGE, with varying window sizes according to the number of channels simultaneously assigned to a
single user; the window sizes range vary from 64 to 1024 radio blocks. For EDGE transmissions, a 32
radio blocks has been selected.
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In HSDPA and LTE, retransmission of erroneous transport blocks is performed by an Nchannel stop
andwait (SAW) ARQ protocol. In stopandwait schemes, the transmitter handles the transmission of
a single block until it has been successfully received. In SN4G, a maximum of 8 channels can be set up
simultaneously since this is the
value suggested in the standard.
Block size is determined by
the
reported CQI. As
for GPRS and EDGE, no transport block
losses or errors on the acknowledgement
messages have been emulated.
For WLAN, the SN4G platform also implements an ARQ protocol. In this case only one channel SAW is
employed. The transport block is
a fixedlength
IP packet of 1500 bytes although
it is possible to
perform fragmentation to improve channel utilization.
One of the key techniques
of LTE is the link
adaptation that allows the
transmitter to adapt the
transmission format, including the MCS, transmission rank and precoder among other parameters, to
the channel quality variations. As the simulator
implements a FDD system,
in which uplink (UL) and
DL are separated in frequency,
DL quality cannot be estimated
directly by the BS through
measurements of the UL channel.
Instead, the user must report
the BS which is
the channel state
that it is experiencing. Several
methods are allowed to perform
the channel state report.
First,
explicit channel estimates can be
reported to
the BS. For example, one channel estimated
can be
reported per subcarrier. To reduce
the overhead of such a
transmission only channel correlation
matrices could be transmitted. Besides these methods, other kind of nonexplicit
information could
be transmitted. In fact, this approach is common in LTE. Following
this approach, the user performs
SINR measurements and calculates the
most suitable format for
transmission. This information
includes which is the most suitable MCS, multiantenna scheme, and precoding matrices and rank in
case of spatial multiplexing.
This information is conveyed in
a set of reports known as
Channel
Quality Indicator (CQI), Precoder Matrix Indicator
(PMI) and Rank Indicator (RI). In this process,
it is
necessary to have an
interface with the physical
layer to know the channel state and also with the
link abstraction model to be able to translate the channel state into a transmission quality estimate.
An additional source of
information used to perform the
link adaptation is the HARQ feedback sent
by the user to the BS. For
instance,
if a negative acknowledgement
is received by the BS,
it means
that the transmission format used in a previous transmission was incorrect and adaptation is needed.
Link adaptation algorithms are not included in the LTE specifications. Therefore, each developer uses
a different solution to obtain the best system performance.
Channel Errors Emulation
The link level performance is represented
by a simplified model consisting of a set of LookUp Tables
(LUTs) [8] mapping the CIR
(Carrier to
Interference Ratio) to a given
link quality parameter such as
the BLER. Different LUTs need then to be
produced for different operating conditions, e.g., RAT and
transmission mode, mobile speed and propagation environments (typical urban or rural area). SN4G
employs these LUTs to decide whether a radio block is received in error once the CIR experienced by
the radio block has been computed.
When modelling WLAN it is unusual to make use of LUTs as in cellular systems. Rather, the concept
of sensibility is employed.
If a certain physical
transmission mode is given and
the mean received
power is over
its specific sensibility then the transmitted block will be properly received, otherwise
the block is dropped.
Regarding LTE, this technology is based on Orthogonal Frequency Domain Multiplexing (OFDM) that
is a multicarrier modulation. Then,
the outcomes of SINR
measurements performed over the
air
interface are vectors whose elements represent the SINR of each carrier. This fact has made linkto
system mapping developed for single
carrier systems to be useless
for LTE. Instead, a family
of
methods known as Effective SINR
Mapping (ESM) is commonly used.
These methods map an
instantaneous set of SINR
samples into a scalar value,
which is called effective SINR.
The general
formula used to obtain the effective SINR is:
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being the number of samples,
an information measure function and
its
inverse and and
two configurable parameters. Once the effective
value, , has
been calculated, a look up
table obtained through simulations
conducted in an AWGN scenario
is
used to translate the effective
value to, for
instance, a BLER value. With this aim, an AWGN
look up table must be obtained for each Modulation and Coding Scheme (MCS).
For each MCS, and must be
calibrated through link level
simulations to minimize the
error
between real BLER (obtained through simulation) and predicted BLER (obtained through the ESM). If
the model was perfect, the would
be the scalar value that in
an AWGN scenario
would produce the same BLER obtained in the multicarrier
scenario with the measured SINR vector.
The most common ESM model
in LTE evaluation is the Mutual
Information Effective SINR Mapping
(MIESM), which is implemented in
SN4G. MIESM uses as its
information measure the so
called
mutual information.
2.1.4 Simulation process
The configuration of the
simulated SN4G scenario is made
through a configuration file
called
‘IniFile.ini’. This file is read
at the beginning of the
simulation process and the specified
input
parameters are
loaded. In the course of the simulation process, some performance parameters are
written and saved
in several output text files. These
input and output parameters are described
in
next sections. Note that the ‘IniFile.ini’ can be defined automatically using the GUI of SN4G.
2.1.4.1 Configuration parameters
The input parameters that can be specified in the configuration file are quite huge. Most significant
parameters are the following:
Active RATs considered in the
simulation. In SN4G, it is
possible to select the RATs to
be
considered in the simulated scenario among all the implemented RATs in the platform: GPRS,
EDGE, HSDPA, WLAN and LTE.
Cell radius for each RAT. For each considered RAT, the cell radius must be configured.
Number of interfering cells.
This parameter establishes the number
of interfering cells
considered to calculate the interference signal level. Several examples are shown in Figure 5.
In a scenario considering four 120ºsectorised cells per cluster, 7 interfering cells mean that
the first and the second interference rings are considered to calculate the interference signal
level.
In a four omnidirectional cells per cluster pattern, the number of
interfering cells will
be 6 if the first
interference ring is only
considered. The same number of
interfering cells
must be established if an interference reuse factor of 1 is considered as in HSDPA systems or
LTE.
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Figure 5 Interference patterns for different cell clusters
User load. Number of users that are being simulated.
Number of frequency carriers per RAT.
Thermal noise (default:
121 dBm for GPRS/EDGE,
102 dBm for HSDPA,
90 dBm for WLAN
and bandwidthspecific for LTE).
Transmission power (default:
30 dBm for GPRS/EDGE, 43 dBm
for HSDPA and LTE and 20
dBm for WLAN).
channelization codes in the same cell due to multipath dispersion.
Number of cells. Total number of cells consider in the current simulation.
Session and traffic input
parameters. All the parameters needed
to configure the traffic
model employed at the platform
must be specified at the
configuration file. For a more
complete description of these session and traffic parameters, [13] can be consulted.
Number of static/pedestrian/vehicular users. Users can be simulated as static, pedestrian or
vehicular users with different average speeds.
Average pedestrian/vehicular speed
(default: 35km/h and 80 km/h
respectively). The
average speed established for each type of users can be specified.
LA/AMC updating period (default: 60ms for GPRS/EDGE, 2ms for HSDPA, 1ms for LTE).
Seed. The seed to initialize the random number generator.
Simulation time.
BLER and throughput at radio block level per traffic service.
BLER and throughput at radio block level per RAT.
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Normalized delay at application level per traffic service.
Waiting times per traffic service. The time a user has waited for receiving resources to carry
out its transmission is saved per traffic service.
User satisfaction:
percentage of satisfactory transmissions per traffic service:
o WWW transmission is considered
satisfactory if a web page
is transmitted in less
than 4 seconds.
Video frames transmissions are
considered satisfactory if they
are completely transmitted
before the next video frame is to be transmitted.
Percentage of unsent and aborted video frames for realtime video services.
Statistics about the
transmission modes utilization are also
saved per each RAT and
traffic
service. The statistics are:
o
Percentage of times each transmission mode has been used.
o
Percentage of times the employed transmission mode was optimally selected. When
a block is received, the
optimum transmission mode is
calculated based on the
current radio conditions. Then,
the optimum transmission mode is
compared with
the transmission mode used to
transmit the block. If both
values are equal, the
employed transmission mode is considered optimally selected.
o Percentage of times the
employed transmission mode was more
robust than
necessary. If the optimum transmission mode calculated at the receiver is less robust
than the used transmission mode.
o Percentage of times the
employed transmission mode was less
robust than
necessary. If the optimum
transmission mode calculated at the
receiver is more
robust than the used transmission mode.
o Transmission mode change rate.
Rate at which the optimum
transmission mode
selected by the LA mechanism has changed. This parameter provides an estimation
of the channel quality variability.
o Retransmission rate. This output
parameter accounts for the radio
blocks
erroneously received, and then, which must be retransmitted.
o
Percentage of times the employed transmission mode was not optimally selected per
each transmission mode.
RAT selection statistics. Percentage of times each RAT has been selected to carry out a user
transmission. These results are provided per each traffic service.
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2.1.5 Key Performance Indicators
2.1.5.1 General performance evaluation criteria
The following indicators should be evaluated for all types of services.
Cell spectral efficiency
Cell edge user spectral efficiency
As defined in [14].
Peak spectral efficiency calculation
The peak spectral efficiency is calculated as specified in [14].
Control plane latency calculation
The control plane latency is calculated as specified in [14].
User plane latency calculation
The user plane latency is calculated as specified in [14].
Intra
and interfrequency handover interruption time derivation
The intra
and interfrequency handover interruption time is calculated as specified in [14].
Average blocking rate
It should be determined for all the services that can be blocked. It
is calculated over the simulation
duration as the ratio of blocked&nb