1. 2005:238 CIV
M A S T E R ' S T H E S I S
Improved Power Control
for GSM/EDGE
Fredrik Hägglund
Luleå University of Technology
MSc Programmes in Engineering
Department of Computer Science and Electrical Engineering
Division of Signal Processing
2005:238 CIV - ISSN: 1402-1617 - ISRN: LTU-EX--05/238--SE
3. Abstract
In GSM (Global System for Mobile Communication), a tradeoff between
different goals is necessary to achieve the optimal system performance. Gen-
erally, high speech quality, high capacity and low power consumption are
major goals. Power control is one of several techniques used to reach these
goals. Power control regulates the signal strength with the aim to reduce
the overall interference. Since radio environment and mixture of different
user requirements may vary, there is an interest in making the setting of
the power control target value automatically and dynamically. The target
value should adapt to the environment and the situation. The method used
is to extend the current power control with an outer loop that could adjust
the quality parameter qdes. An attempt to use EMR data to adjust qdes is
shown to have effects that eliminate the essential principle of power backoff
to avoid the so called party effect, and is therefore not recommended.
However, a method using the distribution of the transmitted power in-
dicates more promising results. It is shown that there is high correlation
between the number of satisfied users and the number of users within the
regulating window, i.e. the number of users not limited by the maximum or
minimum power levels.
4. Acknowledgement
This Master’s Thesis is the final part of my Master of Science Degree in
Signal Processing at Luleå University of Technology. The work has been
carried out at Ericsson Research in Luleå during fall 2004.
First I would like to thank my supervisor Magnus Thurfjell at Ericsson
for great guidance and valuable support during the Thesis. I would also like
to thank my examiner James LeBlanc at Luleå University of Technology for
his work.
i
7. List of Figures
2.1 Simple schematic view over the GSM hierarchy. . . . . . . . . 5
2.2 A 4/12 frequency reuse pattern. The gray cells are using the
same frequency group and hence mobiles from these cells using
the same frequency could interfere with each other. . . . . . . 6
3.1 Transmission power p1 and p2 from base stations to mobile
stations. Each mobile experience a carrier signal power C
and interference I. . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Principle for down regulation. The values rxlev and rxqual
are the measured values before any exponential filtering. . . 12
4.1 Schematic view of the FHRUNE simulator. . . . . . . . . . . 15
4.2 Format of the frequency hopping matrix. . . . . . . . . . . . . 17
4.3 The steps in the mapping from C/I to FER. . . . . . . . . . 18
4.4 The lookup table for mapping mean and standard deviation
to FEP shown as a figure. . . . . . . . . . . . . . . . . . . . 19
5.1 The existing power control in GSM extended with the outer
loop. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.2 Block scheme for the existing power control algorithm ex-
tended with an outer loop based on EMR. . . . . . . . . . . 22
5.3 The outer loop when the control algorithm is based on FER. 23
5.4 Satisfied users at different traffic loads. . . . . . . . . . . . . 25
5.5 BER and CV of BER is plotted against each other. Every
dot gives the BER and CV of BER for each mobile at each
measurement time. An approximation of a level curve for
FER_target = 1% is also in the figure. . . . . . . . . . . . . 26
5.6 A plot over how CV_BEP changes around the average line
for one single mobile during a call. The call lasts for nearly 7
seconds and it is impossible to see a trend for the CV_BEP
values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
5.7 Histograms for two different occasions. In the left plot the
system has experienced a relatively low traffic load and in the
right a relatively high traffic load. . . . . . . . . . . . . . . . 30
iv
8. 5.8 Number of satisfied users for a specific condition for different
qdes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5.9 The usage of power control and the amount of satisfied users
plotted for different qdes. The traffic load is fixed and low. . 32
5.10 The plot shows the regulating fraction of the transmitted pow-
ers and the number of satisfied users for a high traffic load.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
5.11 The plot shows the regulating fraction of the transmitted pow-
ers and the number of satisfied users for a high traffic load.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
5.12 The difference between number of mobiles transmitting on
maximum effect and the number of mobiles transmitting on
minimum effect for some different traffic loads. . . . . . . . . 35
5.13 A block scheme of the outer loop based on the usage of power
control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5.14 The qdes value is adjusted to a proper value, depending on
the current environment in the cell. . . . . . . . . . . . . . . 37
5.15 The number of satisfied users with and without an outer loop
in the power control. The result with no power control is also
displayed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
v
9. Chapter 1
Introduction
This section will give a brief introduction to the subject handled in this work.
The purpose, method and the delimitations are also in this section. Finally,
an outline for the report is presented.
1.1 General
GSM (Global System for Mobile Communication) is a mobile system that is
globally used. In GSM, and in other traditional mobile systems a tradeoff be-
tween different goals is necessary to achieve the optimal system performance.
Generally, high speech quality, high capacity and low power consumption are
major goals in cellular radio communication systems. Power control is one
of several techniques used to achieve these goals. Power control regulates
the signal strength to reduce the overall interference.
One of the parameters that control the power level are a target value
controlling the desired quality. Instead of setting this target value for each
separate case, for example different radio environment or speech codecs, there
are some benefits in making the control of this target value automatic. How
the target value should be set may depend on many variables, such as the
interference from other cells, the distance between the mobile station and
the base station, the traffic load, the total amount of radiated power etc.
Therefore, there is an interest in making the setting of the target value
automatic and dynamic.
1.2 Purpose
The purpose of this work is to find out whether a method, based on the
additional information from for example the new standardized measurement
report, could improve the current power control by reducing the hard param-
eter predefinitions. This could be done by making it possible to set the target
value automatically. The method should improve, or at least preserve the
1
10. performance of the existing power control. The results of the work includes
recommendations on algorithm design, parameter tuning and comments of
performance in general.
1.3 Method
The method chosen to fulfill the purpose was to automatically adjust the
target value in an “outer loop”, using additional information from for ex-
ample the enhanced measurement report. Initially some analysis was done,
principally about how information concerning speech quality should be used,
but also what kind of information to use. Several different fundamental algo-
rithms have been developed and a simple evaluation determines whether the
algorithms have been excluded or if they were relevant for further analysis.
A model has been made for each of the relevant algorithms, which shows
how the additional information controls the desired target value. Adjusting
the signal strength to track this target value sets the desired speech quality.
Input values, which are decided from the outer loop, that adjusts the
signal strength have been tested and evaluated. This means that simulations
have been done to verify the choices of input values in different situations. It
should be easy to change the input values. The simulation tool that has been
used is an advanced existing simulator at Ericsson Research called FHRUNE.
1.4 Delimitations
The work was delimited to only look at an outer loop that controls the qual-
ity target value, which is an input to the existing power control algorithm.
This means that there are possibilities that other solutions could give better
performance for the outer loop. But such algorithms have not been examined
in this work.
Another limitation was to only look at speech transmission and not
packet data transmission.
1.5 Outline
In chapter two the background to the subject will be presented. Chapter
three describes the existing power control algorithm, and a problem state-
ment that will more precisely describe the problem. In chapter four the
simulation model is described and how the simulations where done to model
the different approaches of the outer loop will be presented in chapter five.
The results of the simulations and an analysis of the results will also be pre-
sented in chapter five. Finally, in chapter six a discussion is presented with
the conclusions and some ideas about future work.
2
11. Chapter 2
Background
The background theories about mobile systems, the GSM system, radio net-
work and impairments to radio transmission are presented briefly in this
section to give an initial understanding.
2.1 Evolution of cellular networks
The first generations cellular networks (1G) were analogue systems launched
in the beginning of the eighties. Examples are NMT450 and NMT900
(Nordic Mobile Telephone), TACS (Total Access Communication System)
in United Kingdom and AMPS (Advanced Mobile Phone Service) in USA
and Canada. All these systems where using Frequency Division Multiple
Access (FDMA). In FDMA each user has a dedicated frequency and hence
only one user per channel is allowed.
In the beginning of the nineties the second generation cellular systems
(2G) were developed. Examples of systems are GSM (Global System for
Mobile Communication), D-AMPS (Digital-Advanced Mobile Phone Service)
in America and PDC (Personal Digital Cellular) in Japan. The 2G systems
were improved with both digital transmission and Time Division Multiple
Access (TDMA). In TDMA users are sub-divided into a number of time slots
for each carrier frequency. For example, the GSM system uses both FDMA
and TDMA, where users are still assigned to a discrete slice of the frequency
spectrum, but these are divided into eight time slots (full-rate GSM). GSM
was designed for voice, but with data capabilities. [2]
The third generation cellular system (3G) was launched first in 2001. 3G
are designed for data, but with voice capabilities and allow different multi-
media services at very high bit rates. GPRS (General Packet Radio Services)
is a developed version of GSM and the first step towards the third genera-
tion. Other systems developed against 3G are GSM/EDGE (Enhanced Data
rates for GSM Evolution) and WCDMA (Wideband Code Division Multi-
ple Access). 3G are planned to be the key system for future multimedia
3
12. communication worldwide.
2.2 The GSM network
The GSM system was introduced to the European market in 1991. It is the
most widely spread of the 2G standards. GSM is active on the frequency
band around 900 MHz. As said before, GSM uses both FDMA and TDMA.
The GSM system is using a number of 200 kHz FDMA channels, each one
divided into eight TDMA timeslots for speech transmission [4]. Each of the
eight time slots has duration of 0.577 ms. Binary Gaussian Minimum Shift
Keying (GMSK) is used as the modulation technique, which provides one
information bit per symbol. This could be compared with 8 PSK (Phase Shift
Keying) that is used in for example EDGE that provides three information
bits per symbol. EDGE is developed from GSM and GPRS to support much
higher data rates. [2]
In the transmitter, the analog speech is digitized and divided into seg-
ments of 20 ms, called a frame. GSM digitize the speech by using an 8 kHz
sampling frequency and 8 bits resulting in a bit rate of 64 kbps. The bit
stream has been compressed and quantized by using a mix of a vocoder and
a waveform coder, resulting in a 13 kbps bit stream. There are also some
burst formatting, channel coding and interleaving before the information is
transmitted on a time slot. A sequence of bits sent during one time slot is
called a burst and each speech frame is spread over eight bursts.
The GSM radio network is built up with Base Transceiver Stations (BTS)
in a cellular structure. The BTS consists of a group of transmitters and
receivers that communicate with the mobile stations (MS) located in the
cell that it controls. The communication to different mobile stations takes
place in different channels, divided up as described above. The next level
in the network is the Base Station Controller (BSC), which communicates
with one or more BTS. In other words, the BTS has the radio equipment
for example antennas and transmitters that makes it possible for the BSC to
communicate with the mobile stations. The BSC is in charge of hand overs,
what transmit powers to use and other higher level tasks. The next step
in the traditional GSM hierarchy is the Mobile services Switching Center
(MSC). The MSC sets up, supervises and releases calls. This is a big switch
that interfaces several BSC, via a Gateway MSC (GMSC), to other telephony
and data systems such as PSTN (Public Switched Telephone Network) or
other networks. In Figure 2.1 there is an overview of the GSM hierarchy. [5]
4
13. GMSC
PSTN
MSC
BSC
BTS BTS
MS
MS MS
MS
Figure 2.1: Simple schematic view over the GSM hierarchy.
2.3 Radio network
The basic components in a radio network are the mobile station, the base
station that communicates with the mobile station, and the mobile switching
center, which sets up, controls and releases calls. All base stations control
different areas, also known as cells. Each cell has one base station that
mobile stations in that cell could connect to. However, several base station
are usually placed at one single site depending on the cellular structure.
Normally a cell structure of three base stations per site is used. A cell can
be of any size from a radius of tens of meters to a radius of tens of kilometers.
[1]
The transmission between the base station and the different mobiles in
a cell takes place in a set of channels, for example based on the number of
frequencies. Mobile systems only have a limited amount of bandwidth. Each
user requires a certain amount of that bandwidth. If each frequency only
were used once, also the number of users would be limited. This means that
5
14. frequencies used in one cell need to be reused in another cell at a certain
distance away. Users that use the same frequency will interfere with each
other. A group of cells that together uses all the available frequencies in
the system is called a cluster of cells. Clusters repeated over and over again
forms a cellular network. A schematic illustration of a cellular network is
seen in Figure 2.2, where the cells are represented as hexagons for simplicity.
The frequencies used in a cluster are divided into frequency groups. How
many frequencies there are in each group is dependent on the total number of
available frequencies and the required reuse factor. Different reuse patterns
can be formed but some examples are 4/12 or 3/9. For example, 4/12 means
that all the available frequencies are divided into 12 frequency groups, one
for each cell, which is located at 4 sites with 3 base stations each. In a system
like this the reuse factor is 12. As seen in the figure below each base station
site has three cells and is using directional antennas. [2]
Figure 2.2: A 4/12 frequency reuse pattern. The gray cells are using the
same frequency group and hence mobiles from these cells using the same
frequency could interfere with each other.
It is preferable to keep the number of base stations down, to decrease
costs of the BTS hardware and expensive sites when establishing new base
stations. The existing base stations should be used in the most efficient way
to avoid establishing new ones, including considerations about the limited
amount of bandwidth. The base stations that use the same set of frequencies
should be placed at sufficient distance apart from each other to decrease
interference. However, to keep the capacity at an acceptable level the base
stations have to be relatively close to each other. So there will always be
some interference.
The mobile stations in the gray cells in the Figure 2.2 above could ex-
perience co-channel interference because of the reuse of frequency carrier.
Cells that simultaneously uses the same carrier frequency interfere with each
other if they are close enough. To measure the amount of interference at
6
15. a connection one measurement that could be used is the carrier to interfer-
ence ratio (C/I). This measurement is the relation between the carrier signal
power C and the interference power I. The carrier signal power, in dB, for
one connection is defined as
C(t) = p(t) + g(t), (2.1)
where p(t) is the transmitted power in the downlink and g(t) is the (nega-
tive) gain between the mobile and the base station. The interference I(t)
contains both of the interference carrier power from other base stations and
the thermal noise, see [3]. However, the interfering power from neighboring
cells with the same carrier frequencies are above the noise floor. This means
that the system is interference limited rather than noise limited. The C/I,
in dB, for each mobile is defined as
C/I = C(t) − I(t) = p(t) + g(t) − I(t). (2.2)
Suitable ranges for this parameter are decided by the desired quality
profile. For example acceptable speech quality or required data bit rate.
However, a high C/I-value corresponds obviously to a high quality in the
specific connection [3].
2.4 Impairments to radio transmission
The problem with radio transmission is that it is impossible to control the
transmission environment. The impairments are known, but their effect as
a function of time is unpredictable and hence, it is difficult to accurately
model the transmission dynamically. There are several factors that affect
radio transmission conditions, in addition to noise. Some of the problems
with radio transmission are described below [5].
2.4.1 Fast fading
One effect that could occur is that the reflected signals could arrive at the
receiver with such an unfavourable phase that they cancel out each other.
This cancelling effect, which is also referred to as fast fading or Rayleigh
fading, is however dependent on the position of the transmitter or receiver,
so that often it is already sufficient to change position by less than half a
wavelength. Moreover, fading also depends on the transmitter frequency
which influences the phase of the signals at the reception site. This means
that the fading dips will appear at different places for different frequencies.
This fading effect is most common where the reflecting is high.
7
16. 2.4.2 Path loss
The amplitude of a signal diminishes when a signal travel further away from
the transmitter. This phenomenon is referred to as path loss. Path loss can
make it difficult to get enough signal strength in a large cell. An advantage
with path loss is the natural decrease in interference from other cells. This
is the principle on which the reuse in a cellular system is built.
2.4.3 Shadow fading
Another fading impairment, except for fast fading, is shadow fading or some-
times called slow fading or log normal fading. Obstacles, that are shadowing
the radio path between the transmitter and receiver, will cause slow varia-
tions in signal strength. Anything interrupting the free line of sight could
be considered as an obstacle.
2.4.4 Time dispersion
A reflecting object far away, such as a mountain, reflects signals and the
mobile station will hence receive both a direct radio signal and a fairly strong
reflected signal from the reflecting object. These two signals will arrive at
different times which could cause individual bits to overlap with each other
and disturb the overall received signal. This effect is called Inter Symbol
Interference (ISI).
2.4.5 Co-channel interference
Distant radio transmitters transmitting on the same frequency as the one
used by a special radio link will disturb. Even if they are very far away and
their amplitude has been attenuated due to path loss a disturbing effect will
be noticed in the receiver. This impairment is known as co-channel interfer-
ence and is an important part in mobile networks. Co-channel interference
is described more deeply in other parts in this report.
8
17. Chapter 3
Power control
This chapter describes power control in general and the existing power con-
trol algorithm. A description of the EMR (Enhanced Measurement Report)
is also presented. The chapter ends with a discussion about the problem and
how the problem might be solved.
3.1 General
Power control (PC) refers to the strategies or techniques required to adjust
the transmitted power. Power control regulates the transmitted power to
achieve a desired signal strength. A mobile far away from a base station
requires a stronger transmitted signal than a mobile close to a base station. If
the speech quality is better than necessary for one mobile the signal strength
for that specific mobile will be decreased. This implies that the system will
be improved, because of the reduction in interference. The single mobile will
also experience a decrease in battery consumption when transmitting to the
base station. This is the main idea with power control. Power control is
used both in uplink and in downlink between base station and mobile. In
this work only downlink is described. The principle is however equal.
The transmission power p from the base station to the mobile, Figure
3.1, should be controlled to optimize the system. The power should be high
enough to achieve a sufficient carrier signal power C at the mobile station
and low enough to minimize interference I at other mobiles. The transmitted
powers from base stations to mobiles are controlled by the power control
algorithm developed for the GSM network.
9
18. C
I
C
I
p2
p1
Figure 3.1: Transmission power p1 and p2 from base stations to mobile sta-
tions. Each mobile experience a carrier signal power C and interference I.
A common strategy to utilize available resources in cellular radio sys-
tems is therefore to control the transmitter powers described above. The
motivation is to maintain an acceptable quality throughout the lifetime of a
connection, in other words an acceptable C/I. That is, the aim with power
control is to increase the number of mobile stations with a C/I on an accept-
able level. The power control will optimize the transmitted power, and thus
increase the number of satisfied users if traffic is maintained, or keep the the
number of satisfied users if traffic is increased [6]. When power control is
used the total amount of radiated power is reduced compared to when it is
not used.
Keeping an acceptable level of quality should hold despite varying chan-
nel conditions and presence of disturbing interference from other users. The
existing power control algorithm considers the system quality and not the
quality for single mobiles as the main regulate factor. This means that some
single mobiles have to accept slightly worse quality to improve the total sys-
tem quality. However, when applying power control to real systems, some
challenges are prevalent. Available information in measurement reports is
crude, highly quantized and constrained to physical limits. So one chal-
lenge is to issue relevant power levels based on this information to obtain an
acceptable quality. [3]
3.2 Current power control algorithm
In the power control algorithm, quality and signal strength is both consid-
ered. Bad quality as well as low signal strength will increase the transmit-
ted power. For each measurement period (480 ms) two variables, rxqual
and rxlev, are reported based on measurement from all bursts during that
measurement period. The variables rxqual and rxlev stands for received
quality and received signal strenght. These variables are used to adjust the
transmitted power. Predefined values for quality respectively signal strength
defines values for controlling rxqual and rxlev in the regulation process. The
10
19. variables, rxqual and rxlev are filtered with nonlinear exponential filters in
order to eliminate variations of temporal nature. The measurement reporting
causes a delay that typically is three periods.
The controlling parameter for rxqual in the regulation is qdes, and for
rxlev the parameter ssdes. The qdes value is the target value that specifies
the desired quality. Internally, qdes and rxqual are converted to C/I-valus,
expressed in dB according to Table 3.1. Linear interpolation is used to realize
C/I.
Table 3.1: The conversion between qdes, rxqual and C/I. The unit dtqu
stands for deci-transformed quality units.
qdes [dtqu] 0 10 20 30 40 50 60 70
rxqual 0 1 2 3 4 5 6 7
C/I [dB] 23 19 17 15 13 11 8 4
The instruction for the change in power for the regulation is given by
pu = α ⋆ (ssdes − rxlevfiltered) + β ⋆ (qdesDB − rxqualDBfiltered) (3.1)
where α and β is the path loss respective quality compensation and qdesDB
and rxqualDBfiltered are the qdes and rxqual mapped to C/I as in table 3.1
[6]. The power level down regulation order is then given by
PL = INT(−
pu
2
) (3.2)
where INT truncates the power level to a higher value. PL could have values
from 0 to 15, and that represent a down regulation of 0 to 30 dB, which could
be seen in the final output power level by the BTS,
BTS output power = pmax − 2 ∗ PL (3.3)
where pmax correspond to full power.
The existing power control algorithm accepts lower quality in single con-
nections if the whole system experiences a gain in quality. It considers the
system quality and not the quality for single mobiles as the main regulate
factor. If a mobile experience lower signal quality than the target value,
the transmitted power to that mobile will be increased. However, it will
only increase the power so that the signal quality changes towards the tar-
get value and not actually achieve the target value. This regulation is to
prevent “party effect”. The party effect can be describes as, if one mobile
experience low signal quality, the transmitted signal power will be increased
to that mobile. This results with an increment of the interference at other
11
20. mobiles. These mobiles then require higher signal power and the interference
at the first mobile is increased. Finally all mobiles will be transmitting with
maximum power, and this effect it is called the party effect. The existing
power control algorithm prevents this by just regulating towards the target
value. This is done by setting β < 1 in equation 3.1 [6].
To get a good understanding in the power regulation, knowledge about
how much the output power will be down regulated for certain signal strength
or quality is necessary. Hence, the dependence between, signal strength,
quality and down regulation is important. A way of studying these quantities
is in a plot describing the behavior of the algorithm. This could be seen in
Figure 3.2. How great the down regulation is, depends on the values of
rxqual and rxlev.
Figure 3.2: Principle for down regulation. The values rxlev and rxqual are
the measured values before any exponential filtering.
In the figure above it is shown how the power is down regulated. The
controlling values for the desired signal strength and quality, ssdes and qdes,
are set to define the point where the two separate planes of the algorithm
meet, point marked 1 in Figure 3.2, and the positions of the planes, marked
2 and 3 in Figure 3.2. Point 1 is at approximately rxqual = 3, which is equal
12
21. to qdes = 30, and rxlev = 14. Plane 2 regulates mainly against the signal
strength to avoid lower power than the noise floor and plane 3 regulates
mainly towards quality.
3.3 EMR
The EMR (Enhanced Measurement Report) is a standardized measurement
report that contains additional information compared to the earlier mea-
surement report. Like the old measurement report it contains information
of the performance of the transmission, for example rxlev and rxqual. The
additional information in the EMR are the mean and the CV (coefficient
of variation) of the bit error probability (BEP). These values are called
MEAN_BEP and CV_BEP, and they are calculated as an average over
the frames in a measurement period. The CV_BEP has a general definition
as the standard deviations divided by the mean value.
3.4 Problem statement
The existing power control algorithm adjusts the transmitting powers to
track a predefined quality value, to keep the system quality at an acceptable
level. A fixed value of target BER, qdes, is used as the predefined value for
quality control. However, there are some problems with using qdes as the
predefined quality value. First, BER might not be a good measurement for
quality, which implies that quality might change but BER does not. The
other problem is the usage of a fixed target value for all different situations.
This means that a pre-study to determine the proper fixed value need to be
done, and the value could not be changed in an ongoing system.
However, the main problem with the existing power control algorithm
is the amount of parameters that needs to be predefined. Reducing these
parameters makes the algorithm less complex and more intuitive.
A method that automatically sets and adjusts this fixed target value
if the environment or other factors changes is desirable. This would be a
way to avoid predefining a number of parameters and thus make the power
control more intuitive. The problem with setting the value initially will also
be solved due to the automatic tracking.
13
22. Chapter 4
Simulator description
In this chapter the simulation model is described, for example how the simu-
lation environment looks like and how some functions works. The simulator
has been developed at Ericsson Research and is named FHRUNE and it has
been used in all simulations.
4.1 Simulation model
The simulation model is based on the real GSM system network. The model
contains an environment that takes things like propagation and thermal noise
into account. The model also gives opportunity to set parameters that con-
trol how the GSM system should work. In this work, the possibility to adjust
the parameters in the power control algorithm is of special interest. This im-
plies that there are possibilities to adjust parameters to change the behavior
of the system. Like the real GSM system the simulation model also uses a
measurement report to extract data from the environment.
FHRUNE uses three different time interval levels because updating all
parameters at the shortest time interval would be both unnecessary and in-
efficient. Parameters like power and mobile positions change only at defined
measurement period intervals, 480 ms in GSM. The longest time interval is
therefore represented by the measurement period. The next time interval
represents the length of a speech frame or a block, which contains 20 ms of
information. Each measurement period has 26 blocks. 24 blocks is used to
carry the information and signaling and empty bursts constitute together the
two extra blocks. The shortest time interval is for the parameters changing
fastest and is the burst level.
A schematic view of the main function in FHRUNE is shown in Figure
4.1. Before entering the main loop all variables and system parameters are
declared and initiated. In the main loop new mobiles are created and added
to the system. They are given an initial position and speed within the cell
plan. Path loss between all base stations and all mobiles are calculated
14
23. and allocation for new mobiles or hand-over for already existing mobiles
are also taken care of in the beginning of the main loop. Finally, before
entering the inner loop a number of parameters for both mobiles and base
stations are updated, for example the transmit powers. In the inner loop
some packet scheduling are done and then on burst level implemented by
matrix operations the C/I values for all mobiles are calculated. The C/I
values are used to estimate the quality for both speech and data users. When
the inner loop is finished data is extracted and logged. Finally in the main
loop, users with low speech quality are removed together with completed
calls and all remaining mobiles are given a new position and speed.
Measurement
period
Frame
period
Initiation
Create traffic
Path loss calculations
Allocation and blocking
Initiate and update user specific data
Packet scheduling
Calculate C/I and quality
(Burst level)
Extract and log data
Complete and drop calls
Move mobiles
Figure 4.1: Schematic view of the FHRUNE simulator.
15
24. 4.2 Basic functionality
The functions in the real GSM system are modeled by the system simula-
tor FHRUNE. Some of the basic functionalities are described below. The
simulator must both simulate the environment and the GSM system. The
propagation model and the fast fading are typical parts used to model the
environment. Frequency hopping, discontinuous transmission (DTX) and
power control are all functions in the GSM system and strives to improve
the system performance.
4.2.1 Propagation model
Once for each measurement period a G-matrix is calculated. The G-matrix
has a row for each mobile station and a column for each base station. A
value in the matrix includes path loss, antenna gain and slow fading and
each value represents a mobile/base station pair. The matrix updates once
a measurement period. The innermost level inside the inner loop calculates
different fast fading values for each burst and add them to the G-matrix.
This means that for each step of the inner loop four updated matrices are
generated. The values in the G-matrices are used to calculate the resulting
C/I at the receivers in the system for each measurement period. The up- and
downlink calculations are performed separately and hence there are different
matrices for each direction.
4.2.2 Fast fading
The fast fading is caused by multiple reflections close to the receiver pro-
ducing a Rayleigh distributed fading pattern. The fading values can vary
considerable because of the dependence of both the used frequency and the
position of the receiver.
In the simulator a Rayleigh fading map models the fast fading. The rows
of this matrix represent the available carrier frequencies and each column
represents a distance of the Rayleigh fading path. The fading pattern of the
Rayleigh path is defined by the used frequency and the coherence bandwidth.
The frequency used for each burst defines which row to use. Different maps
will be used for different values of the coherence bandwidths. The maps are
pre-generated, because of the complexity of the Rayleigh fading model, and
a parameter defines which map to use.
In FHRUNE, a fading path that represents a distance of 50 meters with
separate values at each millimeter is used. The length 50 meters is chosen to
avoid correlation with distant values. Due to the separate frequency bands
for up- and downlink in GSM, two separate maps for each value of the
coherence bandwidth, are used in parallel.
16
25. 4.2.3 Frequency hopping
Frequency hopping is an important option in GSM systems by which network
performance can be enhanced. Consider co-channel interference between
different connections. Not all of the slots are in use on all of the physical
channels on each site where they are reused. If we can take each caller on
a particular sector and jump them from frequency to frequency, then each
user runs a far lower risk of suffering from co-channel interference. This is
because the co-channel interference is shared by many users.
The simulator gives a possibility to choose between GSM pseudo-random
sequences or ideal sequences from the MATLAB random number generator.
In Figure 4.2 an example of frequency hopping sequences for some calls and
some bursts during a measurement period is shown. In the figure, GSM
pseudo-random sequences are used. For example, call 1 is transmitting on
channel 7 in the first burst, but changes to transmit on channel 3 in the
second burst. This hopping between which carrier frequency to transmit on
continues throughout the call.
Channel numbers
bursts (104 columns)
1
2
3
4
5
6
7 3 7
6 2 8
1 5 3
call
. . .
. .
.
. .
. .
. .
.
Figure 4.2: Format of the frequency hopping matrix.
4.2.4 Discontinuous transmission
Discontinuous transmission (DTX) means that the base station instructs
the mobile station to shut down the transmission during the silent periods
in a conversation. This is done to avoid unnecessary transmission and save
energy. The most important part in DTX is the voice detection, which has
to separate the voice from the background sounds.
The DTX is modeled in the simulator on speech frame level as a two
state machine, active respectively inactive. The switching between states is
controlled by parameters that indicate whether the state is active or inactive.
17
26. 4.2.5 Quality estimation
The C/I-values that is calculated in the inner loop for each burst is one
kind of quality measurement used in FHRUNE. The C/I-values describes
the relationship between the signal carrier power and the interference power.
However, the simulation model is designed to have a number of different
quality measurements available. In the sections below some definitions of
quality measurements used in this work are presented.
A better quality measure than the C/I-values is one that is based on
a method that maps the C/I-values to frame error probabilities for speech.
The C/I-values are used as input to a process that decides if each frame
is successfully received or not. These mappings are the result of link level
simulations and the process could be seen in Figure 4.3 and is described
below.
table lookup
group and calculate
two−dim. table lookup
random process
FER
C/I
BER
µ , σ
FEP
Figure 4.3: The steps in the mapping from C/I to FER.
In the first step of the process all the C/I-values are mapped to bit er-
ror probabilities for each burst, in other words each individual C/I-value
correspond to an individual bit error probability. The mapping is imple-
mented as a one-dimensional lookup table. The bit error probability values
are grouped in speech frames, and the mean µ, and the standard deviation
σ, are calculated for each frame as
µ =
1
n
n
i
BEPi, (4.1)
18
27. σ =
1
n − 1
n
i
(BEPi − µ)2
. (4.2)
The values BEPi are the bit error probabilities for each burst. The cal-
culations are done per frame and since each frame consists of eight bursts,
n is equal to 8. The mean and standard deviation are then used in the
two-dimensional lookup table, Figure 4.4, to get FEP. One value of error
probability is extracted from each pair of mean and standard deviation. [7]
0
0.1
0.2
0.3
0.4
0.5
0
0.1
0.2
0.3
0.4
0.5
0
0.2
0.4
0.6
0.8
1
mean
The lookup table as a figure
std
FEP
Figure 4.4: The lookup table for mapping mean and standard deviation to
FEP shown as a figure.
The FEP values are used in a random process to decide if each frame
is erroneous or not. In the random process a uniformly distributed random
vector is compared to the FEP according to
frameerror = random(size(fep)) < fep, (4.3)
where frameerror is a vector containing ones and zeros where a one indicates
a frame error, random(size(fep)) is a uniformly distributed random vector
and fep is the frame error probability. The frame error is used to calculate
the frame erasure rate (FER).
19
28. Chapter 5
Tested algorithms
The proposed method for dynamically regulating qdes is by adding an outer
loop to the existing power control. The implementation of the outer loop
in the simulator is included as a function in the main part of the existing
simulator FHRUNE. The outer loop has the purpose to calculate a qdes value
based on some information for the controlling of the inner loop. This target
value could be extracted in different ways. The following sections describe
the method in general as well as three different approaches, using different
information, to calculate qdes. This chapter also presents the result from the
simulations of the three different approaches and an analysis of the result.
Comparison with the theories is included to verify the results. Results from
both unsuccessfully and successfully algorithms are presented.
5.1 General
The motivation for including an outer loop to the existing power control
algorithm is that bit error rate (BER) is not necessarily well correlated to
quality. BER is the measurement used in the current power control to cal-
culate rxqual. Instead, for example the percentages of lost frames are more
relevant, since the effect of modulation, coding and interleaving is included.
The information that should be used, and how it should be used, depends
on the design of the outer loop. One possibility is the frame erasure rate
(FER). However, the objective with the outer loop is to assign a qdes value
for the existing power control algorithm to track. In Figure 5.1 the block
scheme over the existing power control in GSM is extended with a block for
the outer loop. The existing power control could be seen as an inner loop.
The outer loop may use information from the EMR or other measurements
to produce a qdes value for the inner loop to track.
20
29. Environment
PowerqdesTarget
Algorithm
Current PC
algorithm
information
Additional
Outer loop
Measurement report
RxQual
Inner
loop
Figure 5.1: The existing power control in GSM extended with the outer loop.
The purpose of the outer loop is to serve the inner loop with a dynamic
qdes value that changes automatically. The measurement report should give
the outer loop additional information about the current quality in the system.
The mean and the standard deviation of the bit error rate is additional
information in the enhanced measurement report and the outer loop may
use these measurements to adjust the qdes value. A couple of different
approaches of how to use the information are tested in this work. Algorithms
are set up, simulated and evaluated.
5.2 Parameter settings
Some of the parameters that describe the environment and also have an
important part in how to set the power control parameters are presented in
Table 5.1. These parameters were held constant throughout all simulations.
21
30. Table 5.1: Fixed parameters that is used in the simulations.
Name Value Description
Frequency band [MHz] 900 Could be 900 or 1800.
Number of frequencies 27 Number of 200 kHz bands.
Frequency groups 3 The reuse factor.
Sectors per site 3 Could be 1 or 3.
Cell radius [m] 500 Size of each cell.
Number of time slots 1 One instead of 8 for simplicity.
Simulator time step [s] 0.48 Length of measurement period in GSM.
5.3 Outer loop based on EMR
This method uses the additional information from the EMR as the input to
the algorithm in the outer loop. The MEAN_BEP and CV_BEP are in the
simulator calculated for each frame. In this model they are calculated as the
average value over a measurement period, in other words as an average of 24
values.
Environment
PowerqdesTarget Current PC
algorithm
CV_BEP
MEAN_BEP
Algorithm
EMRRxQual
Figure 5.2: Block scheme for the existing power control algorithm extended
with an outer loop based on EMR.
5.3.1 Outer loop based on frame erasure rate
This method is based on the frame erasure rate (FER). FER is defined as
the percentage of erroneous frames. A target value, FER_target, is used as
the input value. In this method the target value is 0.8%. Basically, the qdes
value is increased when FER is smaller than FER_target and decreased
when FER is greater than FER_target. The idea in this method is that
22
31. a specific qdes value is used for each specific mobile. Hence, qdes could be
different for each mobile depending on the quality of each connection. The
outer loop strives to increase quality in each specific mobile. In Figure 5.3
the blocks in the outer loop with the proper input values are displayed.
FER
Comparison
measurement
Extracting
quality
CV_BEP
MEAN_BEP
qdes old
change
qdes qdesFER_target
FER_filtered
Filtering
Figure 5.3: The outer loop when the control algorithm is based on FER.
The input values to the outer loop are partly estimated from the EMR
and partly defined by a user as a fixed value. From the measurement report
information to define the FER is used, as described in section 4.2.5 above.
The MEAN_BEP and the CV_BEP could be converted to the mean and
std in Figure 4.4 and hence be used to extract FER in the simulator. The
mean is equal to MEAN_BEP, and the std is calculated as,
σ = CV _BEP ∗ µ (5.1)
where σ and µ are the mean and the std. However, in the simulator a value
of FER calculated for each frame is used.
FER is than used to calculate a value that is compared to the fixed target
FER value, FER_target = 0.8%, defined by the user. The fixed target FER
corresponds to the percentage of useless frames that could be accepted. An
exponential filtering will be done on the FER for each single mobile,
FER_filteredn = α ∗ FER + (1 − α) ∗ FER_filteredn−1. (5.2)
where α = 0.5. This result is then compared to the FER_target to get the
difference and then multiplied with a constant C to get the difference in the
proper unit. The conversion constant is also used to minimize the effect of
quick changes in FER and will also decrease the change in qdes. Finally, the
change is added to the old qdes values,
23
32. qdes = qdesold + qdeschange, (5.3)
where qdeschange is defined as
qdeschange = C ∗ (FER_filtered − FER_target). (5.4)
This algorithm should slowly adjust the values of qdes, for each mobile to
hopefully strive against a FER lower than target FER.
The results from the simulations of this algorithm show no improvement
of the system. This could be described by the fact that the outer loop has too
much influence and disturbs the existing power control algorithm. Basically,
the outer loop takes away the handling if the party effect. This is because
the outer loop contradicts to the inner loop and will change qdes until the
desired FER is achieved. The FER value could be seen as a measure of
quality and when FER is high for a single mobile the outer loop strives to
lower the FER by regulate the qdes value for that specific mobile. The goal
is no longer to increase the whole system quality but just single mobiles.
Figure 5.4 shows the amount of satisfied users at different traffic loads
for system with or without an outer loop. The number of satisfied users is
defined as the amount of mobiles with the average FER, during the lifetime
of a connection, lower than one percent. The equation is
satisfied users =
1
M
M
i=1
FERi < 1%, (5.5)
where M is the total number of mobiles and FER is the frame erasure rate
for each mobile. This definition of the number of satisfied users are just one
of many.
24
33. 0 5 10 15
75
80
85
90
95
100
FER_target = 0.8%, alpha = 0.5Satisfiedusers[%](FER<1%)
Traffic load [Average users per cell]
PC without OL
PC with OL
no PC
Figure 5.4: Satisfied users at different traffic loads.
In the figure above it is obvious that there are more satisfied users when
the system uses a power control algorithm without an outer loop based on
FER. Although, the performance is better with the outer loop compared to
when no power control is used. This means that the effect of the power
control algorithm is decreased when this outer loop is included.
5.3.2 Outer loop based on coefficient of variation
This method is based only on the changes of CV_BEP. Removing the de-
pendence of the MEAN_BEP, removes the ignorance of the party effect, as
was presence in the outer loop based on FER. The benefit is that the outer
and the inner loop no longer contradicts each other. The idea is that the
values of CV_BEP are directly mapped into qdes values. More precisely,
the equivalent values of CV_BEP at the desired target FER level curve will
be found and depending on the position, the qdes is set to an appropriate
value. The qdes value is thus not totally dynamic, thou it changes between
fixed values depending on the value of CV_BEP. Although, the changes in
qdes are automatic. The fixed changes are performed on mobile level, in
other words each mobile have specific qdes.
The interesting part in Figure 4.4 for the lookup table is that for higher
25
34. values of the standard deviation it is possible to have a higher value of the
mean and still maintain at an acceptable FER level. For a specific target
FER, FER_target = 1%, a desired level curve could be extracted. In Figure
5.5, a plot over how the coefficient of variation of BER varies in relation to
BER is shown. CV of BER and BER is directly mapped from the bit values
CV_BEP and MEAN_BEP. In the figure there is also an example of how
an approximated desired target FER level curve could look like.
0 0.05 0.1 0.15 0.2 0.25
0
0.5
1
1.5
2
2.5
3
3.5
4
All mobiles at all times during 30 sec
BER [%]
CoefficientofvariationofBER
FER=1%
Figure 5.5: BER and CV of BER is plotted against each other. Every dot
gives the BER and CV of BER for each mobile at each measurement time.
An approximation of a level curve for FER_target = 1% is also in the
figure.
If there is possible to find all the equivalent values of CV of BER at BER
for the interesting part of the desired target FER level curve, it could be used
to create a function f(CV _BEP) that is independent of MEAN_BEP and
hence the only depending variable is CV_BEP. The function f(CV _BEP)
and the desired target FER level curve is then used as a mapping function
that indirectly maps the CV_BEP to new qdes values. Basically, a higher
value of CV_BEP means that qdes should have a higher value. How big
change depends on the FER level curve. It is possible to map to qdes because
BER could be converted to qdes.
26
35. The task is to examine whether the mobiles have CV_BEP that is higher
or lower than the average and change the qdes value according to that.
However, there are some problems. First, it is very hard to create a function
f(CV _BEP) that is independent of MEAN_BEP, in other words there are
hard to find all the equivalent values for the CV_BEP. Even if it would be
possible the CV_BEP for a single mobile varies too much from one time to
another. In other words at one time CV_BEP is higher than the average
and next time lower. In Figure 5.6 a plot over how CV of BER varies around
the average line for one mobile. The dots represent the values in the first
measurement period, the second measurement period and so on. This figure
shows that a control in this way is nearly impossible. Even if it could be
possible for some mobiles to say that CV of BER is high or low, the change
in qdes is very small. This is because the target FER level curve only gives
small changes in BER for a change in CV of BER.
0 0.05 0.1 0.15 0.2 0.25
0
0.5
1
1.5
2
2.5
3
3.5
4
The average line and values for one single mobile during a call
BER [%]
CoefficientofvariationofBER
1
2
4
1314
9
8
11
12
7
3
6
10
5
Average line
Figure 5.6: A plot over how CV_BEP changes around the average line for
one single mobile during a call. The call lasts for nearly 7 seconds and it is
impossible to see a trend for the CV_BEP values.
27
36. 5.3.3 Summary
None of the methods where the outer loop is based on EMR shows any
improvment to the total system. Using FER and controlling qdes for every
single mobile contradicts the existing power control by not considering the
party effect. This implies that the algorithm no longer strives for maximum
system quality.
Removing MEAN_BEP and only using CV_BEP as the input to the
outer loop removes the possibilities for the same drawbacks as in the previous
method. However, this method is hard to realize, very unstable and if it
would work only give small changes in qdes.
The methods for the outer loop using EMR as input are left behind and
other solutions for input measurements are considered.
5.4 Outer loop based on power distribution
This method is based on the idea that it is preferable in a system that
as many connections as possible are actually using power control, in other
words are not limited by the availible power range. It is also assumed that
this number can be controlled by the parameter qdes. This method controls
one qdes for the entire system.
5.4.1 Power distribution analysis
The transmitted powers in both uplink and downlink between mobile stations
and base stations are limited with a maximum and a minimum effect. From
the maximum effect pmax there are steps of 2 dBm down to the minimum
effect pmin. The maximum and minimum powers are pmin = 16 dBm, pmax =
30 dBm. The transmitted power are described in equation 3.3 in section 3.2.
If link quality is low, transmitted powers with higher effect are used and
lower effects are used when link quality is high. However, a simple method
that estimates the amount of users not limited by the power range could be
described as,
regulating fraction =
number of pbetween max and min
total number of p
. (5.6)
In equation 5.6 regulating fraction is defined as the number of users
transmitting with power level between the minimum and maximum value,
divided with the total amount of transmitted powers. Only looking at the
regulating fraction does not give any information about how to change qdes.
Another estimate that takes this into consideration is the difference between
the number of users transmitting with maximum respectively minimum ef-
fects,
28
37. PCdiff =
number of pmax − number of pmin
total number of p
. (5.7)
This value will be positive if many mobiles are transmitting with high power
and negative if many mobiles are transmitting with low power.
The idea is that if a high number of mobiles transmit with a power in
between the minimum and maximum value, then good system quality should
be achieved because there is high usage of power control. This holds only
if high usage of power control implies good system quality, in other words
if usage of power control correlates with the number of satisfied users. To
examine this some simulations on the existing system with fixed qdes has
been done.
In Figure 5.7, the power distribution are displayed in histograms. The
histograms are from two different occasions, one with a relatively low traffic
load and the other with a relatively high traffic load. As we see in the figure,
for the same qdes, when there are higher traffic load, a lot of mobiles are
transmitting on maximum effect. If the method could regulate the algorithm
to decrease the powers for some of those mobiles a higher regulating fraction
would be accomplished. This is the idea of how this method should work.
29
38. 16 18 20 22 24 26 28 30
0
5
10
15
20
25
30
Low load
Power [dBm]
Numberofusers[%]
16 18 20 22 24 26 28 30
0
5
10
15
20
25
30
High load
Power [dBm]
Numberofusers[%]
Figure 5.7: Histograms for two different occasions. In the left plot the system
has experienced a relatively low traffic load and in the right a relatively high
traffic load.
The correlation between the regulating fraction and the number of satis-
fied users gives a hint of whether there is a relation between the transmitted
power distribution and quality. As mentioned before, regulating fraction is
defined as the amount of powers that is not transmitting on minimum or
maximum effect. The number of satisfied users is defined as the amount of
mobiles with the average FER, during the lifetime of a connection, lower
than one percent, as defined in section 5.3.1.
In Figure 5.8 the amount of satisfied users are plotted against qdes for
a specific traffic load. The figure below shows how the amount of satisfied
users changes by an adjustment of qdes. This plot is for a situation with fixed
environment conditions. The qdes value that implies the highest amount of
satisfied users may however, vary with the environment conditions.
30
39. 0 10 20 30 40 50 60 70
70
75
80
85
90
95
100
qdes
Numberofsatisfiedusers[%] Satisfied users for different qdes
Figure 5.8: Number of satisfied users for a specific condition for different
qdes.
The correlation between the regulating fraction and the number of sat-
isfied users gives a hint of whether this method could be useful or not. .
In Figure 5.9 these two parameters are plotted against qdes for a fixed low
traffic load.
31
40. 0 10 20 30 40 50 60 70
70
75
80
85
90
95
Amount of users for different qdes, low load
qdes
Satisfiedusers[%]
0 10 20 30 40 50 60 70
0
50
100
Regulatingfraction[%]
Satisfied users
Regulating fraction
Figure 5.9: The usage of power control and the amount of satisfied users
plotted for different qdes. The traffic load is fixed and low.
The figure above shows that the maximum value for the both lines ap-
pear at approximately the same value of qdes, and the curves have similar
appearence. This high correlation indicates a relation between the regulating
fraction of the transmitted powers and the number of satisfied users. This
means that information of the power distribution could be used to increase
the amount of satisfied users. If the system strives to find the value of qdes
that gives the highest regulating fraction, this will also imply that the sys-
tem achieves the highest number of satisfied users. The same situation as
above is plotted in Figure 5.10, but with a higher load. In this plot the both
maximum values appear approximately at the same value of qdes. Obvious
from these two figures is that an increase in traffic load decreases both the
number of satisfied users and the regulating fraction.
32
41. 0 10 20 30 40 50 60 70
70
75
80
85
90
95
Amount of users for different qdes, medium load
qdes
Satisfiedusers[%]
0 10 20 30 40 50 60 70
0
50
100
Regulatingfraction[%]
Satisfied users
Regulating fraction
Figure 5.10: The plot shows the regulating fraction of the transmitted powers
and the number of satisfied users for a high traffic load.
The same situation as above is plotted in Figure 5.11, but with a even
higher traffic load. In this plot the both maximum values do not appear at
the same value of qdes. However, the results points to a relation between
the number of satisfied users and the regulating fraction of the transmitted
powers.
33
42. 0 10 20 30 40 50 60 70
70
75
80
85
90
95
Amount of users for different qdes, high load
qdes
Satisfiedusers[%]
0 10 20 30 40 50 60 70
0
50
100
Regulatingfraction[%]
Satisfied users
Regulating fraction
Figure 5.11: The plot shows the regulating fraction of the transmitted powers
and the number of satisfied users for a high traffic load.
Figure 5.12 below shows plots of the difference, PCdiff, for some differ-
ent traffic loads. A high value, in other words a high difference, means that
many mobiles transmit with maximum effect, which imply that a change in
qdes is desirable. Achieving the value zero correspond to finding the optimal
value of qdes for this situation.
34
43. 0 10 20 30 40 50 60 70
−40
−20
0
20
40
60
80
100
Difference between no. of max and no. of min powers
qdes
PCdiff[%]
Low load
Medium load
High load
Figure 5.12: The difference between number of mobiles transmitting on max-
imum effect and the number of mobiles transmitting on minimum effect for
some different traffic loads.
From the figure above it is visible that the lines for the different traffic
load are almost linear and have the same slopes. Assuming that they in
fact are linear with the same slope means that it is simple to adjust qdes
according to the difference. A change in PCdiff implies a change in qdes. If
the optimal values for the difference is zero this plots could be compared to
the plots showing the regulating fraction in Figure 5.9 to 5.11. The maximum
values of the regulating fraction appears at higher values when the traffic
load is increased. The same behaviour could be seen in Figure 5.12. Where
the lines crosses zero appears not exactly at the same qdes as where the
regulating fraction has the maximum value, for the same traffic load.
5.4.2 Simulated algorithm
This method is based on statistics of all mobiles for a measurement period.
In this simulations the environment is similar for each cell and the power
distributions is therefore for all cells in the entire system. The qdes value
controlled by the outer loop is in this method common for all mobiles. A
way to find out how to change the target value is to look at the difference
35
44. between the number of mobiles transmitting with the maximum respectively
minimum power, PCdiff. The block scheme for this method is shown in
Figure 5.13.
qdes old
qdeschange
qdesC
PCdiff_average
Averaging
PCdiff
Extracting
measurement
Power distribution
Figure 5.13: A block scheme of the outer loop based on the usage of power
control.
The value PCdiff is calculated from the power distribution as described
in equation 5.7. In the algorithm block, the first step is an avering of the
present value and the exponential filtered previous values. This difference
in power usage is than converted to a change in qdes and added to the old
value, qdesold, to create the new qdes,
qdes = qdesold + C ∗ PCdiff_average, (5.8)
where PCdiff_average is calculated from PCdiff,
PCdiff_average =
1
OLtime
OLtime
i=1
PCdiffi ∗ e(−(OLtime−i))
. (5.9)
This model performed a possible increment in quality for the system.
The main idea with this method is to adjust the qdes value automatically as
the environment changes. The target value is the same for the entire system
and is depending on the system quality. The equation 5.9 is not optimal and
an improvement might also improve the performance of the algorithm. In
Figure 5.14 a plot that shows how the algorithm works is displayed. This plot
is an example of the initial adjusting of qdes. It is clear how the algorithm
strives to find a qdes that is optimal for this specific situation. When the
environment condition changes the algorithm will find the new qdes. The
control of qdes is based on PCdiff, showed in Figure 5.12, and strives to
a PCdiff equals to zero. The power control algorithm without this outer
36
45. loop uses a fixed qdes value, which obviously is not the optimal value most
of the times.
0 20 40 60 80 100 120 140 160 180
0
10
20
30
40
50
60
70
The first adjusting of qdes, medium load
time [s]
qdes
Figure 5.14: The qdes value is adjusted to a proper value, depending on the
current environment in the cell.
The performance of this algorithm is not fully evaluated. However, in
Figure 5.15 a plot over satisfied users compared to power control with a fixed
qdes is presented. The result in the plot shows that the performance is ap-
proximately equal with or without an outer loop. However, this simulation is
done for an optimal value of qdes, which means that the power control with
a fixed qdes will be the currently best solution. In real systems, variations in
for example traffic load and radio transmission condition in each cell is com-
mon, which means that an optimal value of qdes is hard to find. This mean
that the power control algorithm might have to use this method to retain
this high system performance. This because the outer loop algorithm adjust
qdes to strive for a high system performance when environment changes.
37
46. 0 5 10 15
75
80
85
90
95
100
Traffic load [Average users per cell]
Amountofsatisfiedusers[%] Number of frequencies = 27, qdesinit = 45
no PC
PC without OL
PC with OL
Figure 5.15: The number of satisfied users with and without an outer loop
in the power control. The result with no power control is also displayed.
38
47. Chapter 6
Discussion
This chapter contains the conclusions of this work. A part will also propose
some ideas for future work.
6.1 Conclusions
It has been shown in this work that the power control algorithm extended
with an outer loop is a potential method to increase the performance of the
power control and increase the system performance. However, the outer loop
performance is depending on which parameters used for input measurements
and how they are used.
Using frame erasure rate (FER) as the quality measurement and adjust-
ing a specific qdes for every connection implies some problems. The main
problem, in this case, is that the entire control algorithm strives to get the
same quality for each mobile, which does not imply maximum system quality.
Instead, the algorithm is shown to have effects that eliminate the essential
principle of power backoff to avoid the so called party effect, and is therefore
not recommended. It has also been shown that the coefficient of variation of
the bit error probability (CV_BEP) has a little effect on how qdes should
be adjusted.
The most promising algorithm in this work is the one using the power
distribution as the input parameter to the outer loop. It is shown that there
is high correlation between the number of satisfied users and the number of
users within the regulating window, i.e. the number of users not limited by
the maximum or minimum power levels. The final proposed algorithm has
the difference between maximum and minimum transmitted powers as the
controlling parameter. The result is an algorithm that changes qdes as the
traffic load changes.
39
48. 6.2 Further studies
• The power distribution algorithm is in this work implemented and eval-
uated using information from the entire system. It would be intresting
to use information from each cell instead.
• The algorithm should be tested for mixed services, e.g. AMR FR/HR.
• This algorithm only considers interference. For powers around the
noise limit, regulating according to rxqual could be useful.
40
49. Bibliography
[1] L.Ahlin, J.Zander. ’Digital radiokommunikation - system och metoder’.
Studentlitteratur, Lund, Sweden, 1992.
[2] T.Rappaport. ’Wireless Communication - principles and practicer’. Sec-
ond edition. Prentice Hall, New Jersey, USA, 2002.
[3] F.Gunnarsson, F.Gustafsson, J.Blom. ’Estimation and outer loop power
control in cellular radio systems’. Linköping University, February, 2001.
[4] L.Ahlin, C.Frank, J.Zander. ’Mobil Radio Communication’. Studentlit-
teratur, Lund, Sweden, 1995.
[5] J.Tisal. ’The GSM network - GPRS evolution: one step closer towards
UMTS’. Second edition. Wiley, Chichester, England, 2001.
[6] M.Almgren, H.Andersson, K.Wallstedt. ’Power control in a cellular sys-
tem’. Stockholm, Sweden, 1994.
[7] Håkan Olofsson. “Improved Interface Between Link Level and System
Level Simulations Applied to GSM”. ICUPC ’97. 1997.
[8] 3rd Generation Partnership Project, Technical Specification, 45.008.
2004.
41
50. Appendix A
List of Abbreviations
3G 3rd Generation
BEP Bit Error Probability
BER Bit Error Rate
BSC Base Station Controller
BTS Base Tranceiver Station
C/I Carrier to Interference ration
CV Coefficient of Variation
DTX Discontinous Transmission
EDGE Enhanced Data rates for GSM Evolution
EMR Enhanced Measurement Report
FEP Frame Error Probability
FER Frame Erasure Rate
FDMA Frequency Division Multiple Access
GMSC Gateway Mobile services Switching Center
GMSK Gaussian Minimum Shift Keying
GPRS Global Packet Radio Services
GSM Global System for Mobile communication
ISI Inter Symbol Interference
MS Mobile Station
MSC Mobile services Switching Center
PC Power Control
PSK Phase Shift Keying
PSTN Public Switched Telephone Network
TDMS Time Division Multiple Access
42
