Decision Making Techniques For Cognitive Radios

DECISION MAKING TECHNIQUES FOR
COGNITIVE RADIOSMUBBASHAR ALTAF KHAN
830310-P391
maks023@gmail.com&
SOHAIB AHMAD
811105-P010
asho06@student.bth.se
This report is presented as a part of the thesis for the Degree of Master of Sciencein Electrical Engineering.

Blekinge Institute of Technology
March 2008

Blekinge Institute of Technology
School of Engineering
Supervisor: Tommy Hult
Examiner:

Tommy Hult

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

ACKNOWLEDGMENT
It is our pleasure to express our thanks to Tommy Hult, Blekinge Institute of Technology our
supervisor for the research. Without his moral support and guidance the progress in this researchwould not have been possible. We personally thank him for coordinating the research andproviding us with timely and valuable tips and suggestions towards the task.
We extend our sincere thanks to our family members especially our parents and all our friends
who helped us towards our task during the research.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

ABSTRACTSpectrum scarcity is one of the biggest challenges that the modern world is facing. The efficientuse of available licensed spectrum is becoming more and more critical with increasing demand
and usage of the radio spectrum. Different researches show that the usage is not uniform
throughout the licensed spectrum rather it is heavy in certain parts of the spectrum and has
portions that are utilized inefficiently. Some researchers even claim that more than 70% of thelicensed frequency band is not in use, most of the time. So, there is much room for work yet in theunutilized parts or the inefficiently utilized parts of the spectrum, to overcome the spectrum
scarcity problem.
Different researches are in progress and ways are being found to efficiently utilize the available
licensed spectrum. One of the ways is the use of “Cognitive Radio”, according to this; the alreadylicensed spectrum can be used more efficiently by introducing artificial intelligence, the decisionmaking to be specific, in the radio. This enables the radio to learn from its environment,
considering certain parameters. Based on this knowledge the radio can actively exploit thepossible empty frequencies in the licensed band of the spectrum that can then be assigned toother processes in such a way that they don’t cause any interference to the frequency band thatis already in use. This makes the efficient usage of the available licensed spectrum possible.
The users that are allocated the licensed frequency bands of the spectrum are the primary users
and the users that are allocated the empty frequencies within the licensed frequency band,
according to their requested QoS specifications, are known as the secondary users or thecognitive users. They are called as the secondary users as they utilize the unused spectrum
resources only, on non-interfering basis, with the primary users.
This thesis will focus on the implementation of different spectrum allocation techniques for thesesecondary users, based on Genetic Algorithms and an evaluation of the performance of thesetechniques using Matlab coding.
This research will focus on the decision-making process mainly, with an assumption that theradio environment has already been sensed and the QoS requirements for the application havebeen specified either by the sensed radio environment or by the secondary user itself.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

Nomenclature

AC
BER
BPSK
CR
CSM
DB
DSP
EM
FCC
FPGA
G.As
GMSK
GP
KHz
LISP
MAC
MHz
PER
PHY
QAM
QoS
QPSK
RF
RKRL
SBR
SDR
TSP
WCGA
WSGA

Alternating Current
Bit Error Rate
Binary Phase Shift Keying
Cognitive Radio
Cognitive System Monitor
DecibelDigital Signal Processor
Electromagnetic
Federal Communications Commission
Field Programmable Gate Array
Genetic Algorithms
Gaussian Minimum Shift Keying
Genetic Programming
Kilo Hertz
List Processing
Media Access Control
Mega Hertz
Packet Error Rate
Physical Layer
Quadrature Amplitude Modulation
Quality of Service
Quadrature Phase Shift Keying
Radio Frequency
Radio Knowledge Representation Language
Signal to Noise Ratio
Software Defined Radio
Travelling salesman Problem
Wireless Channel Genetic Algorithm
Wireless System Genetic Algorithm

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

LIST OF TABLES[TABLE 4.1]……………………………………………………………………………………….…....…37[TABLE 4.2]………………………………………………………………………………………….....…38[TABLE 4.3]………………………………………………………………………………………….....…39[TABLE 4.4]……………………………………………………………………………………….………40[TABLE 4.5]………………………………………………………………………………………….....…41[TABLE 4.6]…………………………………………………………………………………………….…41[TABLE 4.7]…………………………………………………………………………………………….…42[TABLE 4.8]……………………………………………………………………………………………….53[TABLE 4.9]……………………………………………………………………………………………….53[TABLE 4.10]…………………………………………………………………………………………...…54[TABLE 4.11]…………………………………………………………………………………………...…54[TABLE 4.12]…………………………………………………………………………………………...…55[TABLE 4.13]…………………………………………………………………………………………...…55[TABLE 4.14]…………………………………………………………………………………………...…55[TABLE 4.15]…………………………………………………………………………………………...…55[TABLE 4.16]…………………………………………………………………………………………...…56[TABLE 4.17]……………………………………………………………………………………...………57[TABLE 4.18]……………………………………………………………………………………...………59[TABLE 4.19]……………………………………………………………………………………...………60[TABLE 4.20]……………………………………………………………………………………...………60

LIST OF FIGURES[FIGURE 2.1]………………………………………………………………………………………………18[FIGURE 2.2]………………………………………………………………………………………………18[FIGURE4.1]……………………………………………………………………………………………….43[FIGURE4.2]……………………………………………………………………………………………….62[FIGURE4.3]……………………………………………………………………………………………….63[FIGURE4.4]…………………………………………………………………………………………….…63

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

Contents
ACKNOWLEDGMENT…………………………………………………………………………………...2
ABSTRACT………………………………………………………………………………………………....3
NOMENCLATURE………………………………………………………………………………………...4
LIST OF TABLES……………………………………………………………………………….………….5
LIST OF FIGURES……………………………………………………………………………………....…5
CONTENTS……………………………………………………………………………………………..…..6
CHAPTER 1………………………………………………………………………………………………...9INTRODUCTION……………………………………………………………………………………...…..91.1. SPECTRUM EFFICIENCY TASKS OR COGNITIVE TASKS..………………………...…10
1.2. RESEARCH CONTEXT…………..…………………………………………………………11
1.3. RESEARCH OBJECTIVES…..……………………………………………………………...11
1.4 THESIS OUTLINE…………..………………………………………………………………..13
CHAPTER 2…………………………………………………………………………………………….….14
LITERATURE REVIEW…………………………………………………………………………………14
2.1. BACKGROUND AND HISTORY OF COGNITIVE RADIOS……………………………..15
2.2. CHARACTERISTICS OF COGNITIVE RADIOS…………………………………………..16
2.2.1. COGNITIVE CAPABILITY……………………………………………………………….17
2.2.2. RE-CONFIGURABILITY………………………………………………………………….19
CHAPTER 3…………………………………………………………………………………………….….23
GENETIC ALGORITHMS (AN OVERVIEW)…………………………………………….…23
3.1. SOME OTHER OPTIMIZATION TECHNIQUES..……………..…………………………..23
3.2. WHY GENETIC ALGORITHMS …………………...…………..…………………………..24
3.3. METHODOLOGY OF GENETIC ALGORITHMS……………..…………………………..25
3.3.1. OUTLINE FOR THE GENETIC ALGORITHMS.……….……………………………….25
3.3.2. CHROMOSOME DEFINITION………………….……….…………………………….....26
3.3.3. THE SEARCH SPACE………………………….…….………..……………………….....27
3.3.4. THE FITNESS FUNCTION…………………….…….………..……………………….....27
3.3.5. OPERATIONS IN GENETIC ALGORITHMS…...….………..……………………….....27
3.3.6. THE CROSSOVER AND MUTATION OPERATIONS…..…..…………………….…...28
3.4. GENETIC ALGORITHMS IN THE RADIOS…………………..……………………….....29

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

3.4.1. CHROMOSOME DEFINITION FOR THE RADIO…………..…………………………..29
3.4.2. FITNESS FUNCTION DEFINITION FOR THE RADIO……..…………………………..30
3.4.3. FITNESS EVALUATION FOR THE RADIO..………………..…………………………..31
3.5. THE MULTI-CRITERIA OPTIMIZATION……………………..…………………………..32
3.5. THE DECISION-MAKING…………………………..…………..…………………………..33

CHAPTER 4…………………………………………………………………………………………….….35
RESEARCH METHODOLOGY…………………………………………………………………………35
4.1. THE CHROMOSOME………………………………….………..…………………………..36
4.1.1. THE FREQUENCY………………..…………….……….………………………………...36
4.1.2. THE POWER…………………...……………….…….………..…………………………..38
4.1.3. THE BIT ERROR RATE……………………………...………..…………………………..39
4.1.4. THE MODULATION SCHEME……………………..………..…………………………..40
4.1,5. THE CHROMOSOME STRUCTURE……………………..…..…………………………..40
4.2. THE FIRST POPULATION CHROMOSOMES GENERATION…………………………..41
4.3. THE FITNESS FUNCTION……………………………….……..…………………………..44
4.3.1. FITNESS OF THE FREQUENCY GENE….…………………..…………………………..44
4.3.2. FITNESS OF THE POWER GENE….……………..…………..…………………....……..46
4.3.3. FITNESS OF THE BER GENE……...……………..…………..…………………………..48
4.3.4. FITNESS OF THE MODULATION GENE….…....…………..…………………….……..49
4.3.5. TOTAL FITNESS…………………....……………..…………..…………………………..50
4.4. METHOD 1…………………………....……………..…………..…………………………...52
4.4.1. SLECTION………..………………....……………..…………..…………………………...52
4.4.2. CROSSOVER…………..…………....……………..…………..………………….………..53
4.4.3. MUTATION………………………....……………..…………..…………………………...54
4.4.4. FITNESS CALCULATION………....……………..…………..…………………………...56
4.4.5. NEW POPULATION GENERATION……………..…………..………………...….……..56
4.5. THE OPTIOMAL SOLUTION…....……………..…………..………………………….…....57
4.6. METNOD 2…………………………....……………..…………..……………………….…..58
4.6.1. ELITISM………….………………....……………..…………..……………………….…..58
4.6.2. CROSSOVER…….………………....……………..…………..….………………….……..58
4.6.3. MUTATION……….………………....……………..…………..…………………………..58
4.7. MATLAB RESULTS……………………………..…..…………..…………………………..59
4.8. CONCLUSIONS & BEHAVIOR OF G.A.S……..…..…………..…………………………..61

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

4.9.. FVISION IN FUTURE ….…………....……………..…………..…………………………..64
REFFERENCES………...…………………………………………………………………………………65

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

Chapter. 1
Introduction
Electromagnetic radiation (EM), the disturbance in space that propagates byitself in the space has both electric and magnetic field components that oscillate
in perpendicular to each other in phase and to the direction of energy. It carries
both energy and momentum. The EM spectrum is the range of all possible EMradiations. The EM spectrum or simply “Spectrum” of an object is the radiatedfrequency distribution of EM radiation from that object. The spectrum range
covers wavelengths equivalent to the size of an atom to thousands of kilometersand extends from the radio frequencies to gamma radiations. The spectrum isthus infinite and continuous in nature.
Our concern in this research is with the radio frequency (RF). The RF range of
3Hz to 300 Ghz corresponds to the frequency of alternating current (AC) signalsthat are used to detect radio waves. The radio spectrum is used for wirelesscommunication purposes, where transmission and reception of signals at theantennas are involved. With an increasing demand and usage, the radio
spectrum is becoming saturated in terms of its allocation. A need, therefore, isbeing felt for the efficient use of the available spectrum. This available spectrum
has both licensed and unlicensed frequency bands. An immense research is
being carried out in order to make the efficient use of the spectrum possible andthe idea of Cognitive Radio is one of the ways to achieve this efficiency in the
spectrum utilization. This idea, of cognitive radios, exploits the reality that thefrequency band assigned to the licensed users is never utilized completely bythem; rather it is heavy in certain parts and has the portions that are not utilized
efficiently. The Cognitive Radio exploits these unused or empty frequencies inthe spectrum. These unused frequencies are sensed using spectrum sensing
techniques and then made available to the other users. The users that areallocated the licensed frequency bands of the spectrum are the primary users
where as the users that are allocated the empty frequencies within the licensed
frequency band, according to their requested QoS specifications, are known as
the secondary users or the cognitive users. They are called as the secondaryusers as they utilize the unused spectrum resources only, on non-interfering
basis.
These secondary users actually create “Virtually unlicensed frequency bands” inthe licensed frequency bands of the spectrum. Once detected, the characteristicsof these “Virtually unlicensed frequency bands” are estimated. The cognitive
radio then adapts its parameters according to the detected spectrum
characteristics and secondary user’s QoS (Quality of Services) requirements.
This idea can more specifically be defined as a SDR (Software Defined Radio)
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

Page 9

Decision Making Techniques For Cognitive Radiosor smart radio that has the intelligence built in it to sense the environment andchange its transmission and reception parameters, to utilize these virtuallyunlicensed frequency bands, on non-interfering basis with the licensed users of
the spectrum. It has also to keep in view the needs and requirements of theapplication that is going to use these virtually unlicensed frequency bands. Once
having done the allocation, the next issue is reallocation. Reallocation isnecessary in case of detection of a primary user in the present allocated virtuallyunlicensed frequency band. The reallocation is basically done in order to avoid
the interference that may occur among the co-existing users in the same
spectrum. Care must be taken while reallocating, as it should not affect theongoing communications while we reallocate the spectrum. This is achieved by
the provision of a fair scheduling among the co-existing users in the same
spectrum. So, this as a whole makes the spectrum to be dynamically accessible,
at the real time.
The following steps and subtasks can explain this whole process more explicitly.

1.1. Spectrum efficiency tasks or cognitive tasks:
Cognitive tasks can be divided into the following subtasks.

• Spectrum sensing:It detects the empty frequencies in the licensed spectrum. These empty
frequencies create virtually unlicensed frequency bands within the licensed
frequency bands of the spectrum. This is done by the mutual coordination thatexists among the participating radios. Each of the radios coordinates by reporting
its spectrum usage. The spectrum distribution is carried out on the basis of
calculation of certain parameters. After this sensing process these whole
frequency bands are available to be used by the secondary users unless a
primary user is detected in the same frequency band.

• Spectrum policy or spectrum management policy:
Once detected the empty frequencies in the spectrum, the QoS of theapplication are considered, i.e. the secondary user that is to use these empty
frequencies, and the best available spectrum that meets these QoS is selectedamong these empty frequencies and is assigned to the secondary user.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios• Spectrum Reallocation:
If in case a primary user is detected in the same frequency band allocated tothe secondary user. The secondary user should be reallocated anotherfrequency band in the spectrum to avoid interference that may occur with theprimary user’s frequency band, otherwise. This transition should take place insuch a way that it does not affect the ongoing communications. Hence care must
be taken while reallocation, in order to maintain a seamless communication
during the transition.

• Spectrum sharing:
During the whole cognitive process, a fair scheduling is required between the
primary and the secondary users. This is necessary to avoid the interference that
may occur between them. Defining holding times for the users that have been
allocated the frequency band can do this scheduling. This promises for fairscheduling among the co-existing users that share the spectrum.

1.2. Research Context:
Cognitive radios are emerging as one of the ways for efficient utilization of theavailable spectrum. Due to the allocation of the available spectrum to thelicensed (primary) users, the spectrum is becoming more and more saturated.
Also, the number of the unlicensed (secondary) users trying to access the
spectrum is increasing enormously. The ground reality is that the licensed users
do not use the whole of the spectrum at all instances of time, so the idea is thatsensing the empty frequencies in the licensed frequency bands and thus defining
virtually unlicensed frequency bands, within the already licensed frequency
bands, can accommodate some other users. This makes the efficient utilizationof the available spectrum possible. To achieve this Cognitive Radios can beused, that can allocate these virtually unlicensed frequency bands, dynamicallyat real time by changing their parameters keeping in view the QoS requested by
the secondary user or simply the application, without interfering with the primaryusers.

1.3. Research Objectives:
An immense research is being carried out relating to the cognitive radios.
Certain techniques have been developed to allocate these virtually unlicensed
frequency bands to the secondary users. This particular thesis will focus on theimplementation of different spectrum allocation techniques for secondary user,
based on some Genetic Algorithms and an evaluation of the performance of
these techniques using Matlab coding.
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

Page 11

Decision Making Techniques For Cognitive RadiosThis research will focus on the decision-making process mainly, with anassumption that the radio environment has already been sensed and the QoS
requirements for the application have been specified either by the sensed radio
environment or by the secondary user itself.
The aims and objectives of our research are as follows:
•
•
•
•
•
•

Develop the basic understanding of the concept and working of Cognitive
Radios.
Study the characteristics (parameters) of Cognitive Radios’ and theapplications that are used in secondary user system.
Study different techniques for optimization.
Evaluate different techniques and methods for decision making forspectrum allocation.
Spectrum allocation techniques for secondary user based on Genetic
Algorithms.
Performance evaluation of the spectrum allocation techniques, using
Matlab toolbox.

This particular work will allow a more intensive access to and use of spectrum
than possible with the traditional, hardware-based radio systems. It will help in
effective use of the spectrum as well as dynamic spectrum management andspectrum allocation schemes in the wireless communication systems, in future.
There is still a lot of work to be done in this regard. This research work would be
an attempt towards better utilization of the spectrum.
With the increasing number of mobile users in today’s communication world, thecognitive radios are supposed to make sure that all the users get access to thespectrum. The users’ QoS requirements should also be met while they areallowed the dynamic access to the spectrum. The Cognitive radios at the sametime are also supposed to make sure the interference avoidance between the
primary and the secondary users, while the primary users keep changing theirlicensed frequency band utilization over real time.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

1.4 Thesis Outline:
Chapter 1:
An Introduction to the concept of cognitive radios, the context, scope andobjectives of the research
Chapter 2:
Literature overview with a brief history of cognitive radios, along with theopportunities that the cognitive radios can provide, in order to exploit theintermediate opportunities towards the efficient utilization of the spectrum in the
future.
Chapter 3:
Literature review of the concept of the Genetic Algorithms, particularly their use
in the Cognitive radios to carry out their decision-making process.
Chapter 4:
The methodology of this research, i.e. the focus is on what would be ourconsiderations and possible assumptions in this concept, in order to pursue ourresearch for the explanation of the decision-making in the cognitive radios.
Also, we shall discuss the conclusions that can be drawn from the research workand some of the directions in which the concept of cognitive radios can beextended in future, towards the goal.
The references for the research are included at the end, along with the Matlab
code used in the decision-making process for the cognitive radios.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

Chapter. 2
Literature Review:
This chapter gives a brief overview of the current work that is going on the
Cognitive Radios and the advancements made in the concept.
Due to the spectrum scarcity the software defined radios are the most popularthese days. The term “Software defined” implies the fact that the use of hardware
is kept as little as possible and the most of the signal processing is done using
the software. Cognitive radio S.D.R., is one of the newer versions of the software
defined radios, that has achieved a significant amount of improvement overservices offered by existing wireless networks. The basic characteristic of the“Cognitive radio” is that it can change its parameters at real time, considering the
QoS requirements of the application. This feature enables the Cognitive Radio tobe able to utilize the available spectrum efficiently. The cognitive radio S.D.R. isbased on software embedded in a mobile device that can change its operating
functionalities according to the environment, as the mobile user moves from one
place to the other.
Cognitive Radio is more intelligent and adaptable than just Software DefinedRadio. It is designed to be aware of and be sensitive to the changes in its
surroundings. Thus it learns from its environment and performs functions thatbest serve its user.
The activities of The Federal Communications Commission (F.C.C.) over the
past few years have raised the importance of cognitive radios to overcome thespectrum sacristy issues. This is our main concern in this research. The F.C.C. isreviewing its policies regarding the usage of licensed frequency bands by theunlicensed users [1], for efficient utilization of the spectrum. In the notice given in
may 2004 in which the FCC proposed to allow unlicensed radio transmitters tooperate in the broadcast television spectrum at locations where that spectrum is
not being used. The operation under Part 15, of the notice is subject to thecondition that the device does not cause harmful interference to authorized
services, and that it must accept any interference received. The current Part 15
rules provide substantial flexibility in the types of unlicensed devices that can be
operated. However, the rules prohibit the operation of unlicensed devices oncertain frequencies, including almost all of the frequency bands used forbroadcast television service.
“The FCC should allow unlicensed use of unused TV frequency band spectrum,
when and if the technology is ready.”[2].
In view of the possibility of this change the feature of the Cognitive Radio S.D.R.
mentioned above acquires a great deal of importance, in the future of the
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

wireless technologies and the spectrum usage. The main reason for that is dueto the fact that the Cognitive Radios provide with an opportunity to the unlicensed
users, commonly known as the secondary users of the spectrum to utilize theempty frequencies in the already licensed frequency bands, of the primary users.
The Cognitive Radios use these empty frequencies to identify virtually unlicensed
frequency bands in the licensed frequency bands of the primary user. So, thesevirtually unlicensed frequency bands are now available to be used by thesecondary users. It assigns the best possible frequency band in the spectrum tothe secondary user, keeping in view the QoS requested by it. As mentioned
earlier the Cognitive Radio is sensitive to the changes in its environment itchanges its parameters to fulfill the QoS requested by the secondary user. The
main idea during this procedure is to avoid its interference with the primary user’s
frequency band.
So, the Cognitive Radio now provides the users, both primary and thesecondary with sharing of the spectrum. Also, the Cognitive Radio must ensurethat it frees the spectrum resource for the primary user if it is detected in thevirtually unlicensed frequency band identified by it earlier. In case of detection ofa primary user it should vacate the spectrum for the primary user and should
move to a better virtually unlicensed frequency band, maintaining a seamless
communication along with that, in order to avoid its interference with the primaryuser’s frequency band. So, a proper scheduling mechanism is required during
this process so that it can satisfy both primary and the secondary users.
Thus the key tasks in Cognitive Radios can be identified as, sensing the RF
environment and based on that designing a decision-making module for the
spectrum allocation. This decision-making depends upon the current network
traffic, QoS requirements of the secondary user and the information collected
through RF environment sensing.

2.1. Background and History of cognitive radios:
The Cognitive Radios can formally be defined, as [3] “A Cognitive Radio is aradio that can change its transmitter parameters based on interactions with theenvironment in which it is currently operating.”
The Cognitive radios do not have the history of a century; rather thedevelopment of cognitive radio is still at a conceptual stage. As discussed earlier
the Cognitive Radio is an emerging technology, for the efficient use of the limited
available spectrum. Nevertheless, as we look to the future, we see that cognitive
radio has the capacity to make a significant difference to the way the radio
spectrum can be accessed, with much improved utilization. Indeed, given its
potential, the cognitive radio can justifiably be described as a “disruptive, but
unobtrusive technology”. Disruptive as it can make a great difference in the
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

technology. Unobtrusive as it attracts with its solution for utilization of the alreadylicensed frequency bands efficiently.
A brief history of the Cognitive Radio is given below.
Cognitive radio is not a single technology to be very explicit. It resulted frommany technologies coming together to result in the Cognitive Radio technologies,
due the verity that exists among its applications. For example, the development
of digital signal processing (DSP), development of math and signal processing
tools and source coding of voice, image and data etc.
If we were particularly specific to SDR, the roots of SDR design go back to
1987, when Air Force Rome Labs (AFRL) funded the development of aprogrammable modem as an evolutionary step beyond the architecture of theintegrated communications, navigation, and identification architecture (ICNIA).
This historical development is represented by the [figure 2.2], given below.
Coming back to the Cognitive radios again, the term “cognitive radio” was firstused and defined by Joseph Mitola III in an article published in 1999. [4] He
described, the way a cognitive radio could enhance the flexibility of personal
wireless services, through a new language called the “Radio Knowledge
Representation Language” (RKRL). The idea of RKRL was further expanded in
Mitola’s doctoral dissertation, presented at the Royal Institute of Technology,
Sweden, in May 2000 [5]. This dissertation presents a conceptual overview ofcognitive radio as an exciting multidisciplinary subject.
As mentioned earlier, the FCC published a report in 2002, aimed at the changes
in technology and the profound impact that those changes would have onspectrum policy. This report set the stage for a workshop on cognitive radio, heldin Washington DC, in May 2003. The papers and reports presented at that
Workshop are available at the Web page listed under [6]. This Workshop wasimmediately followed by a Conference on Cognitive Radios, held in Las Vegas,
NV, in March 2004 [7].

2.2. Characteristics of Cognitive Radios:
According to the definition of the Cognitive radios given in the section 2.1, the
Cognitive Radios can be categorized as having the following characteristics.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

2.2.1. Cognitive Capability:
The cognitive capability refers to, sensing of the RF environment, by the
Cognitive radio. In sensing of the RF environment the unused part in the
spectrum (referred to as empty frequencies) are specified. There are different
techniques used for this purpose. It includes power measurements, modulation
schemes and scheduling mechanisms. It is not that easy as has been specified
here, it involves very complex real time measurements and decision making toachieve the target. Nevertheless, we sense the RF environment and specify theempty frequencies in the licensed frequency band in such a way that there is nointerference to other users, after their allocation to the secondary users.
Since, the most of the spectrum has already been assigned, the most important
challenge is to share the licensed spectrum without interfering with thetransmission of other licensed users as illustrated in [Figure 2.1 and Figure 2.2],[8]. The cognitive radio enables the usage of temporally unused spectrum, whichis referred to as the empty frequency thus creating a virtually unlicensed
frequency band, to be allocated to the secondary users on non-interfering basiswith the primary users.
Once it has allocated the spectrum to the secondary user it has to make sure, tovacate the spectrum for the primary user in case of its detection. If the current
assigned frequency band to the secondary user is also used by some licensed
user, the cognitive radio moves to another empty frequency or stays in the same
frequency band, altering its transmission power level or modulation scheme to
avoid interference as shown in, the same figure 2.1. This is done to share thespectrum between the primary and the secondary user, on non-interfering basis.
So, a fair spectrum scheduling is also required between the primary and thesecondary users to share the spectrum in such a way that no interference takes
place. These decisions for the spectrum scheduling are taken in real time, toprovide fair scheduling among the users in order to avoid interference.
In other words, the cognitive capability of a cognitive radio enables real timeinteraction with its environment. This interaction helps to determine theappropriate communication parameters in order adapt to the dynamic radio
environment. The radio analyzes the spectrum characteristics and changes theparameters at real time to provide a fair scheduling among the users that share
the available spectrum.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios[Figure 2.1.] explains the procedure, identifying the allocated spectrum, the empty
frequencies and the dynamic access to the spectrum, along with a plot, along the time,
power and frequency axes.

[Figure 2.2.] Shows the tasks required for the adaptive operation in the spectrum, referred
to as, the cognitive cycle.

In the following subsections we provide an overview of the three main steps forthe cognitive cycle: i.e., spectrum sensing, spectrum analysis, and spectrum
decision.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios• Spectrum sensing:A cognitive radio identifies from the information available in the spectrum, theempty frequencies and the portions of the spectrum that are in use, by sensing
the spectrum.

• Spectrum Analysis:Once identified the empty frequencies, their characteristic are determined, inorder to make the allocation of the best possible frequency band among them, tothe secondary user, according to the QoS requested by it. This includes thedetermination of the data rate, the transmission mode, and the bandwidth of thetransmission.
The Cognitive Radio can then change its parameters according to the spectrum
analysis carried out in the above manner to allocate it to the secondary user.

• Spectrum Decision:
On the basis of the above spectrum analysis, the decision for the allocation of
the best possible frequency band to be assigned to the secondary user is taken.
This spectrum decision lies among the identified empty frequencies in the
spectrum sensing process.
Once the operating spectrum frequency band is determined, the communication
can be performed over this spectrum frequency band. However, there is still the
dynamic spectrum access part to be considered for scheduling purpose, as theradio environment changes over time and space, the cognitive radio should keep
track of the changes of the radio environment, at real time. If the current
spectrum band in use becomes unavailable, the spectrum mobility function is
performed, it must vacate the spectrum for the primary user, and move to abetter virtually unlicensed frequency band, maintaining the on going
communications along with a fair scheduling among the users and spectrum
sharing by them, in such a manner that no interference is observed among them.
Any environmental change during the transmission such as primary user
appearance, user movement, or traffic variation can trigger this adjustment.

2.2.2. Re-configurability:
Re-configurability implies to the feature of the Cognitive Radios that allows thechange in the parameters according to the changing the radio environment. As
mentioned earlier, the Cognitive Radio can adjust its operating parameters forthe transmission, without any modifications on the hardware components. This
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

enables the cognitive radio to adapt easily to the dynamic radio environment.
There are several reconfigurable parameters that can be incorporated into the
cognitive radio as [3] explained below:• The Carrier frequency:
A cognitive radio is capable of changing its carrier frequency. Based on theinformation about the radio environment; it determines the most suitable
operating frequency and performs the dynamic communication over that selected
frequency.
• The Modulation scheme:
A cognitive radio also reconfigures the modulation scheme that is adaptive tothe user requirements and channel conditions. The modulation scheme selection
depends upon the application. For example, for delay sensitive applications like
VOIP, the modulation scheme that enables the higher spectral efficiency is
selected, as the data rate is more important than the error rate, for these types ofapplications. Similarly, for the loss-sensitive applications like TCP enabledapplications, modulation schemes with low bit error rate are selected, as the error
rate is important for these applications.
• Transmission power:
Controlling the power constraints, in the Cognitive Radio, can reconfiguretransmission power. The cognitive radio provides with dynamic power
configuration. This provides with an efficient use of the available power, as itenables the radio to reduce the power of the transmitter to a lower level to savepower if higher power is not necessary for a certain operation. It increases theefficiency as it can accommodate more users by allocating lower powers to theusers that do not ask for a higher power for their operation. Hence more numberof users can share the spectrum, using dynamic power configuration. Also, alower power helps to decrease interference among the users sharing thespectrum.
• Communication technology:
A cognitive radio can also be used to provide interoperability among different
communication systems.
These transmission parameters of a cognitive radio can be reconfigured not
only at the beginning of a transmission, for the spectrum allocation when theempty frequencies have been identified, but also during the transmission, when
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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the radio environment changes, i.e. the dynamic allocation. So, the cognitive
radio is switched to a different frequency band, according to the spectrum
characteristics by reconfiguring these parameters.
In order to describe the dynamic nature of Cognitive Radios, each empty
frequency in the spectrum should be characterized considering not only the timevarying radio environment and but also the primary user activity and thespectrum band information such as operating frequency and bandwidth. Hence, itis essential to define some other parameters such as interference level, channel
error rate, path-loss, link layer delay, and holding time that can represent thequality of a particular spectrum band as follows:• Interference:
Some spectrum bands are more crowded compared to others. Hence, thespectrum band in use determines the interference characteristics of the channel.
From the amount of the interference at the primary receiver, the permissible
power of a user can be derived, which is used for the estimation of the channel
capacity.
• Path loss:
The path loss increases as the operating frequency increases. Therefore, if thetransmission power of a user remains the same, then its transmission range
decreases at higher frequencies. Similarly, if transmission power is increased tocompensate for the increased path loss, then this results in higher interference
for other users.
• Wireless link errors:
Depending on the modulation scheme and the interference level of thespectrum band, the error rate of the channel changes.
• Link layer delay:
To address different parameter changes like path loss, wireless link error, andinterference, different types of link layer protocols are required at different
spectrum bands. This results in different link layer packet transmission delay.
• Holding time:
The activities of primary users can affect the channel quality of the spectrum.
Holding time refers to the expected time duration that the user can occupy a
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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licensed frequency band before getting interrupted. Obviously, the longer theholding time, the better the quality would be.
• Channel capacity:
The channel capacity can be derived from the parameters explained above, isthe most important factor for spectrum characterization. Usually SNR at thereceiver has been used for the capacity estimation. However, since SNR
considers only local observations of users, it is not enough to avoid interferenceat the primary users. Thus, spectrum characterization is focused on the capacity
estimation based on the interference at the licensed receivers.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Chapter. 3
Genetic Algorithms (An Overview):
Genetic algorithm (GA) is a technique based on evolutionary computation tofind the approximate solutions to the optimization problems. Genetic algorithms
are inspired by Darwin’s theory of evolution and the best or simply the survivor
among the available pool is an evolved solution. The history of evolutionary
computation goes back to 1960s when Rechenberg first described it in his work
“Evolution strategies”. To be particular to the G.A.’s, they were invented anddeveloped by John Holland that lead to his book “Adaption in Natural andArtificial Systems” that was published in 1975. The evolutionary computation may
involve techniques like inheritance, mutation, selection and crossover to provide
for the best possible optimization. In 1992 John Koza introduced “Genetic
Programing” (G.P.).

3.1. Some Other Optimization Techniques:
The goal in case of optimization problems is to achieve an optimum solution but
the problem is that the search may get complicated and one may not know where
to look for the solution or where to start with. There are many methods that canhelp finding a suitable solution but the solution might not be the best one. Someof these methods are hill climbing, tabu search, simulated annealing and thegenetic algorithms. The solutions found using these methods are often
considered as good solutions as it is not always possible to define the optimum.
Game theory that is also at a nascent stage and is used for interactive decisionsituations provides analytical tools to predict the outcome of complex interactions
among the rational entities based on perceived result. It works on predictions ofprobabilities but demands for a precise knowledge of the total number of nodes,
but the dynamic nature of real networks one doesn’t even have the knowledge ofwhat nodes do enter or leave the network and at real time. Also, the definition ofa steady state and fear form an undesirable drift with increasing number of nodes
may cause worries and may make the implementation of Game theory concept in
the cognitive radios very complex.
Another approach in the race is fuzzy logic but it offers sever limitations thatcause its value in the dynamic nature of the wireless environment, to taper. It
permits the approximate solutions to be found in the face of uncertain inputs. Its
logic for approximation does not have an evolutionary ability to allow it to changewell in time with the environment encountered at real time. [13]Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Yet another approach is the neural networks that are well recognized as an AI
technique but it doesn’t offer with reliability in terms of surety that it will playwithin a set of operational constraints. Most of the neural networks require for an
extensive training to replicate the observed behaviors and usually behave in
unexpected manner when introduced to a totally new problem to solve. [13]3.2. Why Genetic Algorithms:
Genetic algorithms have applications in many fields nowadays, like computerscience, engineering, economics, chemistry, physics, mathematics andbioinformatics and many other fields.
Since their introduction, the G.A’s have been used to solve difficult problems
like, Non deterministic problems and machine learning as well as for theevolution of simple programs like evolution of pictures and music. The main
advantage of G.A.’s over the other methods is their parallelism. G.A.s travel in asearch space that uses more individuals for the decision-making and hence areless likely to get stuck in a local extreme like the other available decision-making
techniques.
Genetic Algorithms have the ability to converge very quickly on a specificsolution, have a wide range of solutions to address the unknown environments,
can be implemented on semiconductor devices and enable integration withwireless technologies. Also, the biological model for the G.A.’s provided rapid
prototyping using DSP or FPGA. These are some of the advantages of using
G.A.’s in the cognitive radios. The others are their chaotic search capability andflexibility along with their ability to be implemented on a vector based coprocessor. It provides with a wireless channel genetic algorithm (WCGA) tosense the wireless environment, a wireless system genetic algorithm (WSGA) toadapt to the radio along with a Cognitive System Monitor (CSM) based on metagenetic algorithm to monitor and change the behavior of the system, in the
cognitive radios. All these provisions work continuously in a cyclic process for thedecision-making. A distributed memory can be used to understand the pattern
and utilize the information in the past for the current decision-making.
The G.A.s are very easy to implement and can be reused to solve otherproblems. Once you have implemented a basic G.A. you just add a new object
i.e. just another chromosome and using the same encoding scheme just change
the existing fitness function and you can solve another optimization problem.
However some problems might find implementation of the encoding scheme andthe fitness function to be very difficult.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

The disadvantage of using G.A.s lies in the computational time. G.A.s can be
slower than the other available methods at times. But the availability of the option
to control the number of generations and terminate a longer run at any stage andfaster and faster computers these days, enable to ignore this factor.
To have a clear idea about the use of G.A.s we list some of their applications:[16]• Nonlinear dynamical systems - predicting, data analysis.
• Designing neural networks, both architecture and weights.
• Robot trajectory.
• Evolving LISP programs (genetic programming).
• Strategy planning.
• Finding shape of protein molecules.
• TSP and sequence scheduling.
• Functions for creating images.

3.3. Methodology of Genetic Algorithms:
The evolutionary computation starts from the selection of some randomly
generated population of individuals (known as chromosomes) that exhibit certain
characteristics, and follows them through the generations. The fitness of every
individual in a population at a generation is evaluated based on the stochastic
calculations and mutating them, thus giving rise to a new population, with a hopethat the new population will be better than the old one. The fitter the solutions,the more chances they have to be reproduced in the next generation of solutions.
An iterative algorithm is then followed to continue the process from generation to
generation, until some condition like a maximum number of generations havebeen reached or the optimum solution has been found. This may or may not givea particular satisfactory solution.
Genetic algorithms require the definition of two basic actions.
1. A genetic representation of the solution domain.
2. A fitness function to evaluate the solution domain.
3.3.1. Outline for the Genetic Algorithms:
1. Start: Generate a random initial population of n chromosomes thatconsists of the available solutions for the problem.
2. Fitness: Emulate the fitness of each of the chromosomes in the initial
population.
3. New population: Reproduce, according to the following steps until thenext generation completes.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios•••

Selection: Select two chromosomes that have the best fitness level
among the current population.
Crossover: Crossover the two selected chromosomes considering thecrossover probability, to form the off springs for the next generation. If thisoperation were not performed the offspring would be the exact copy of theparent chromosomes.
Mutation: Mutate the new offspring at each defined mutation point,considering the mutation probability and place it in the new population.

4. End Condition: Repeat the above steps until certain condition (maximum
no of population or the desired optimum has been reached), has been
met. [14]Implementation of the above outline for the G.A.’s is not that straightforward,
rather certain factors how to create the chromosomes and which encoding to use
to perform the G.A. operations on them. Also, the selection of parents anddefinition of criteria i.e. the fitness function definition are some issues to beaddressed. We discuss these factors in the following sections.
3.3.2. Chromosome Definition:
One of the standard representations of a solution domain is an array of bits
(other representations may also be used), essentially having the same size,
throughout in order to have a uniformity, to facilitate the operations to be
performed on them, like the crossover operation. Variable length representations
may also be used, but certain operations like crossover would get too complex inthat case.
A representation for the chromosome must provide the information about thesolution that it represents. The most popular of all representations is the binary
string. Where each bit in the string can represent the chromosome characteristics
or the whole string cumulatively can do this. The use of integer or real number
representations for the chromosomes can also be useful. This will be explained
further as we move towards our decision-making process.
This genetic representation is commonly known as chromosome definition for a
genetic algorithm. We select some of the parameters that contribute for the basic
structure of the chromosome. These parameters are the essential components,
without which the chromosome structure definition would be incomplete. An initial
population of these chromosomes is generated randomly, that provides a pool ofchromosomes that are the possible solutions to the optimization problem. Thesechromosomes are then transferred from generation to generation and certain
operations like selection, crossover and mutation are performed at each
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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generation. These processes actually act as filters for a population ofchromosomes that enable the best or at least the better ones among a
population of chromosomes to be transferred to the next generation, according totheir qualification for a satisfaction level of a defined fitness test.
3.3.3. The Search Space:Looking to find a solution that is the best among the others, in a space of all
feasible solutions i.e. the search space (the state space). Each of the points inthe search space represents a possible solution that can be marked by its value(the fitness value). [14] So looking for a solution in the search space can also beviewed as looking for the some extreme value i.e. a minimum or maximum value
and this is what optimization is all about. The G.A. methodology might lead to thegeneration of some other points in the search space, off course the possible
solutions as it proceeds with the evolutionary process.
3.3.4. The Fitness Function:
A fitness function is defined over the genetic representation to measure its
quality. This fitness function definition varies from problem to problem. The
fitness function is applied to every individual in a population based on thestochastic calculations. Each individual is mutated and the fitness of the solution
would contain the best of the individuals among the current population, according
to the defined fitness function, to give rise to the new population in, an iteration.
The fitness function actually filters out the individuals with a lower fitness level atthe current generation and transfers only those to the next generation that satisfy
the defined fitness function. This enables the presence of individuals with ahigher fitness level to be transferred to the next generation while filtering out theones that deviate too far away from the optimization solution. This fitness
function is applied at every generation, to a specified number of generations.
Once specified the genetic representation and the fitness function the GA
performs other functions like, initialization of a population of solutions and thenreproducing it from generation to generation by repeatedly applying the mutation,
crossover, inversion and selection operations.
3.3.5. Operations in Genetic Algorithms:
As demonstrated in the above outline for genetic algorithms, the operations in
the Genetic algorithms, i.e. the crossover and mutation are the most important forthe decision-making process. These two operations are performed considering
the probability for these operations to occur, i.e. how often the two operations will
be performed.
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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If there is no crossover operation on the parents, the off springs would be exact
copies of the parents otherwise the off springs would contain the parts of bothparent’s chromosome and a crossover probability of 100 % would cause all theoff springs be made by the crossover operation. Similarly a crossover probabilityof 0 % would yield the whole new generation be made from exact copies of thechromosomes from the old population but this does not mean that the whole newgeneration is the same as the old one, rather crossover is done in a hope that the
new chromosomes will contain good parts of the old chromosomes to result inbetter chromosomes in the new generation. These good parts are essential tosurvive in the next generation.
Also, the mutation probability implies, how often the parts of the chromosomes
would be mutated. The offspring are generated immediately after the crossoveror simply, directly copied without any change, if there is no mutation operation. If
the mutation occurs, one or more parts of the chromosome would be changed. A
mutation probability of 100 % would result in change of the whole chromosome
and a mutation probability of 0 % means no change in the chromosome. Mutation
attempts to prevent the G.A. to fall in local extremes but it should not occur muchoften as it can convert G.A. to a random search and the mutation rates areusually set low.
Population size definition is yet another factor to be taken care of while using
G.A.’s. i.e. the number of chromosomes to be included in a single generation. If
there are too few chromosomes in a generation, the G.A. might not be able toexplore the whole search space for the optimum solution and a selection of too
many chromosomes for a generation might make the decision-making process
too slow. Researches prove that after a certain limit a large population size
selection would prove to be useful as it makes it unable to solve the problem
faster than the moderate sized populations.
3.3.6. The Crossover and Mutation operations:
Once defined the chromosome structure and the fitness function, the G.A. thenperforms certain operations to increase the number of solutions in its pool. These
operations like crossover and mutation operations are performed in a defined
standard way. It’s the user that has the privileges to define both the crossoverand the mutation rates.
If a crossover occurs on two of parents to create an offspring, a set of randomcrossover points is selected in the chromosomes. These crossover points
separate the chromosomes into parts with reference to the parametric values;
this may split the individual genes for example a frequency gene in two. In such a
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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case the higher of the two frequency values will be preserved in one of the off
springs and the lower in the other. The crossover rates are normally set high, toincrease the probability for the occurrence of the operation.
Mutation operation then follows the crossover operation. The mutation operationis performed on some or all of the bits of the chromosome. For each gene a
random value is generated; if this value is less than the mutation probability
defined by the user according to its specified mutation rate, one of the bits in thegene is inverted. Mutation rate is usually kept low as it can have a huge effect on
the structure and fitness of the offspring produced, as a result. If the fitness of thegene is still not improving then the mutation rate can be increased in order toachieve the required optimization.

3.4. Genetic Algorithms in Radios:
The radios using the genetic algorithms require to be represented in the abovementioned way in order to be able to use the genetic algorithm and come up with
an optimization. Representing the radios in the above-mentioned genetic terms
actually allow them to accommodate the genetic algorithms in them and evolveas Cognitive Radios. This enables the cognitive radios to adapt themselves tothe constantly changing RF environment, at the real time.
The Genetic algorithms approach used for the optimization of the decisionmaking module in the radio, as they are well suited to the multi-objectivefunctions due to their convergence behavior towards the optimized solution andhelp the radios in adaptation for the decision-making process. Apart from this, thegenetic algorithms also provide the optimization in decision making with multiple
advantages. They provide with flexibility in problem analysis, as long as thechromosome and the objective functions are defined properly. Also, theconvergence behavior of the genetic algorithm is really helpful in our application,i.e. the Cognitive Radios. The genetic algorithms may have a long convergencetime for an optimal solution but normally do not take much time to give very goodsolutions.
The use of genetic algorithms in the radios involves the following actions.
3.4.1. Chromosome Definition for the Radio:
As mentioned earlier, the very first step in the design of a genetic algorithm isthe definition of the chromosome structure that is followed by the development ofa fitness (or objective) function in order to determine the fitness of a population ofchromosomes. The chromosome definition must represent the radio’s behavioral
traits for the decision-making process to achieve the required optimization. There
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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can be many possible traits that can be considered in this regard but we shallconsider only some of the basic traits for the radio in this research. Some of thepossible traits that can be considered are the occupied bandwidth, spectral
efficiency, power consumption and data rate. These traits will be explained
further in this report.
The chromosomes in the genetic algorithms would be represented as simple
vectors of data structures with different data types defining their genes. These
genes and chromosomes may have different representations. In this research weshall represent the genes and the chromosomes in terms of arrays of bits. We
shall use the minimum number of bits for the sake of simplicity. A radio
chromosome may have different genes representing its structure, but for thesake of simplicity we shall consider only a few basic ones in this research (four intotal), namely frequency, power, bit error rate and the modulation schemes. All
these four genes shall be discussed further in the research report in detail, in the
coming sections.
3.4.2. Fitness Function definition for the Radio:
The chromosome definition is followed by the definition of a fitness function thatserves for the analysis of the fitness for a population of these chromosomes. We
shall define a fitness function based on the performance parameters of thecurrent ratio channel. Each of the genes representing the chromosome will beanalyzed for its fitness by the defined fitness function according to its associatedweight. This associated weight will represent the relative importance that the user
has assigned with each definition.
The Pareto front [12] therefore will move such that the optimal solution provides
the most efficient performance for the user’s QoS requirements under
combinatorial constraints. This multi-objective decision-making procedureaccording to our defined fitness function comes up with a solution that provides
the best performance without wasting the spectrum resources. This implies thatthe solution will be obtained on the user preferences i.e. the application that theuser has to run on the identified virtually unlicensed frequency band. This willprohibit the wastage of the spectrum resources, as it considers the QoS and Qoe
requirements specified by the secondary user or the application itself. For
example, allocating a 100Mbps link with a 30 dB carrier to noise ratio for a user
to just send a mail would be a sheer wastage of the allocated resources. So, thedefinition of the fitness function should be able to handle these issues and thatmakes it very critical, for the best optimization in our decision-making process.
The fitness evaluation functions designed would reflect the current quality, bothat the PHY and the MAC layer of the current channel, it would include the mean
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radiostransmitting power, data rate, BER, packet error rate (PER), spectral efficiency,
bandwidth, interference avoidance, packet latency and packet jitter and many
more, to provide for the current QoS requirements, but for the sake of simplicity
at this level we shall focus only on a few of them in this research as mentioned in
the previous section.
Another attribute of the GA is “The dynamic fitness definition and evaluation”,
where the weight of assigned to the fitness of each individual gene can be
adjusted dynamically, but any fitness function that does not fulfill therequirements for the current radio channel might not be used. Actually a
database of all the fitness functions is maintained and fitness for each individual
can dynamically be weighed and added into the fitness evaluation for a particular
QoS requested by the user. This involves some higher-layer intelligence toevaluate the radio and the network performance.
3.4.3. Fitness Evaluation for the Radio:
As mentioned earlier the fitness functions are defined individually considering
the current user’s QoS specifications. These fitness functions are applied on arandomly selected population of chromosomes in a multi-objective decisionmaking process with the use of stochastic processes. This implies to theexistence of a trade-off among the parameters for a particular channel. This isanalyzed by the corresponding weights assigned by the user to each of them.
This is actually very useful in our decision-making process and provides with averity of solutions for the best optimization of a problem. For example, for loss
sensitive applications the BER would have a higher weight than the data rate inits fitness function, due to its importance indicated by the user application in its
QoS specifications. Similarly, a higher wattage assigned to the data rate than the
BER, by the user will be beneficial in case of time sensitive applications. So, theweight associated with each of the fitness functions enables a relative
measurement among different parameters and the one with higher weight in aparticular channel would pass the fitness test.
So, an evaluation of the fitness of the randomly generated population ofchromosomes is carried out in multiple dimensions and the ones that survivethese fitness tests are passed to the next generation as a reproduction of thecurrent one. This actually serves to be a filtering process, carried out at eachgeneration that results in an optimized solution at the end of the evaluation by the
G.A., This is described as the Pareto front [12].
The G.A.s may result in long and tedious calculations for the optimal solution insome cases but the diversity of the solutions in the selected population allows
multiple (some of them turn out to be very good) solutions to be tired at each
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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generation. This prevents the G.A. to get stuck, at any stage during the decisionmaking process. This implies that even though the G.A.s may not come up withand exact solution in some cases but they do provide the closest possibility
among the pool of solutions. This will be discussed in more detail in the coming
sections.

3.5. Multi-Criteria Optimization:
The multi-criteria optimization or the multi-attribute optimization is a process ofanalyzing multiple conflicting objectives subject to certain constraints. Multicriteria optimization problems are found in various fields, where maximization orminimization of some functions is required and a trade-off exists among theobjectives. Generally, there is no single solution to a multi-criteria optimization
problem, as maximizing one of the objectives to a certain extent can affect theother objectives in the same problem. So, we usually we have more than one
solution as the optimized result. Then we can pick up the best among them thatsuits our particular case. This gives the best optimization for the problem.
Multi-criteria optimization has much to do with the decision-making in order to
get an optimized solution. The reference [12] provides with a brief overview of theconcept of multi-criteria optimization. A generalized form of the multi-criteria
optimization problem can be represented as follows.

min/max( y), = [f1(x ), f2( x) ............fn( x)]Subject to:x = [x1, x2, x3.......,xm]EXy = [y1, y2, y3.......,yn]EY
Where there are n dimensions to consider in the search space and fn(x) defines
the mathematical function to evaluate dimension n. Both x, the set of input
parameters, and y, the set of dimensions, may be constrained to some space; X
and Y. EX and EY represent the functions for the spaces X and Y, respectively.
The optimal solutions to the above problem are a set of Pareto points. Where
improvement in one of the objectives may worsen the others. So, there exists a
trade-off among the individual genes.
In over research the Pareto points move in such a way that the optimal solution
provides the most efficient performance for the user’s requested QoS under theradio domain and the regulatory constraints. A point here should be noticed that
there should not be any wastage of the radio resources while decision-making. A
higher QoS than requested by the user would be wastage of the resources andshould not be allocated.
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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3.6. The Decision-Making:The unused or the inefficiently utilized spectrum bands that are spread over awide frequency range in the RF environment may include both unlicensed andthe licensed frequency bands. The empty frequencies detected in the frequency
band may show diverse characteristics due to the time-varying radio environmentand some parameters like frequency and the bandwidth. It’s the radio that should
decide the best spectrum band to meet the QoS requirements available
throughout the spectrum bands. The following discussion will further elaborate
the decision-making in the radio.
The focus in this research is the spectrum management in the cognitive radio;with an assumption that the user specifies the QoS requirements itself or theradio’s sensing environment provides this information. The receiver in the
cognitive radio receives the RF environment and then the radio involves itself inthe decision-making process to accommodate a new user requesting thespectrum allocation in that environment. It considers the user’s QoS
requirements as the modulation scheme, channel coding, power consumption
and data rate. It also has the information about the channel conditions, thenetwork interactions and the radio environment such as the empty frequency
bands, transmission and reception power for a specific application and theamount of interference that would be produced with the induction of the new user
in that environment, with the primary users already using the spectrum.
The sensor gives the spectrum status as an input to the spectrum allocator anda spectrum test is performed. If the user gets satisfied with the frequency band it
takes no further action and terminates the process, otherwise the appropriate
frequency bands are selected and certain spectrum decisions are taken. This
spectrum test must comply with the regulations of the spectrum allocations andtend to minimize the interference with the other users. These regulations are
considered in the decision-making process to meet the QoS requirements of thesecondary (new) user.
One of the most important factors that should be considered during thedecision-making about the allocation of the empty frequencies is that the primaryuser should always be preferred if it needs that particular frequency band, overthe secondary user.
There are two available techniques to implement the decision-making process.
1. Use of G.A.s that provide for a good convergence and suite well with themulti-criteria environment.
2. Use of expert systems that provide with an ability to learn at each iteration
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

but have a great chance to give a very bad decision at the beginning of
the decision-making process.
The following Chapter in the research will elaborate the development of thedecision-making algorithm based on the genetic algorithm approach, along withan analysis of the techniques used in the research. Further these techniques will
also be evaluated using the Matlab coding.

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Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

Chapter. 4
Research Methodology:
This research focuses on the spectrum management and mobility issues in the
cognitive radios, with an assumption that the sensing information is provided as
an input either from the radio environment or the secondary user itself specifies
the QoS requirements specifications to the radio. The Cognitive Radio receives
the RF environment at its receiver and involves itself in a decision-making
process to accommodate a new user requesting the spectrum allocation. Thisrequires a decision-making considering certain factors, such as the secondaryuser’s requirements as parameters like, its modulation scheme, channel coding,data rate and power consumption etc. The user or the application that needs the
spectrum to carry out its communications specifies its QoS requirements to the
cognitive radio that also gets the information about the RF environment from asensing module. This enables the decision-making process to make acomparison between the user’s specifications against the available pool of thesolutions received from the RF environment. Thus, this sensed information fromthe environment serves as the initial population for the genetic algorithm. We
shall generate random values that will serve as the initial population information
received from the RF environment and then take the decision for allocation as an
optimization and come up with the best solution after a comprehensive processdescribed in the following sections.
We shall just consider a few parameters only, in order to maintain the simplicity
in the research. These are the frequency bands, the modulation scheme, power
and BER. It is a simplified case, some other parameters such as data rate,
spectral efficiency, interference; system-to-noise ratio etc can be introduced in
the research at the advanced stages.
There are a number of approaches towards the solution to this problem. For
instance we could have a probabilistic approach, in which we could consider theamount of time a certain frequency band is free or the power availability of aband, at most of the time for transmission or we could specify the transmission
power for a majority of the applications that would use the cognitive radios, or we
could considerer the number of times a certain modulation scheme is in use for a
specific application etc.
Another possible approach can be the use of genetic algorithms that compare
the user QoS requirements to a number of available solutions in the pool andthen the best-optimized solution can be taken among that. The genetic
algorithms use the concept of biological characteristics such as chromosomes
and genes. Functions like crossover and mutation are applied on these
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Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

chromosomes to get the next population of the solutions. There is a possibility
that the new pool of solutions may be better than those in the original populationor they may be even worse than the original set of solutions.
The genetic algorithm approach begins with the definition of the structure of achromosome. We intend to keep the size of the chromosome as small as
possible or it can make the selection process for the best solution among thepool very complex and too slow. In this research we shall focus on the genetic
Algorithm approach and shall develop on it in the following sections.
4.1. The Chromosome:
Before we get started with the definition of the chromosome structure we musthave the information and understanding of the genes of the chromosome that will
constitute its structure. The genes in this particular research would be theindividual parameters that will be considered for the decision-making process.
These genes are basically a part of the solution.
In order to keep the things simple we shall start with the consideration of just a
few parameters (four in total). These four parameters are mentioned below.
1. Frequency
2. Power
3. Bit error rate
4. Modulation techniques
The following sections would focus of the explanation of the above-mentioned
four parameters in detail. We shall keep ourselves specific by focusing on theway they are used in a chromosome, without going in detail with the generalizedview.
4.1.1. The Frequency:
Any application that is able to transmit and receive requires a particularfrequency band to communicate. The frequency band that we shall consider forthis particular research would range form 0-10MHz. There is no theoreticalreason for the selection of this particular band. The step size to consider would
be 10KHz. So, if a frequency band of 10KHz is available for each of theapplication during a transmission period, we can calculate the number of usersthat can be accommodated in this interval. The step size of 10 KHz gives a totalof 1000 available frequency bands. So, a total of 1000 randomly generated
chromosomes are possible in the initial population of pool of solutions for thedecision-making process.
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Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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We shall see in the following sections that these frequency bands should be
represented in terms of bits for the application of certain functions. This would
help in carrying out certain operations during the decision-making process, likethe mutation operation on certain defined chromosomes. The number of bits
required to represent a 1000 frequency bands comes out to be 10, as a total of
10 bits can represent up to 1024 frequency bands that fulfills our requirement
and can easily accommodate a total of a 1000 chromosomes, only. Also, there
should be a check in the decision-making so that value no more than 10 bits forjust a 1000 bands are selected, as that can result in a complete wastage ofresources. Also, we shall convert it back to the decimal representation after the
mutation operation has been done, as it is easier to manipulate the inputs andoutputs in the decimal representation, after that.
This implies that each frequency band in the range of 0-10 MHz is represented
in terms of an integer. The first frequency band of 10 KHz i.e. from 0-10 KHz
would be given the Integer value of 0. The second band with the integer value of
1 that range from 11 to 20 KHz. Similarly after every step of 10 KHz the band
would be assigned a new integer value in the order of the frequency bands in theselected spectrum. Therefore the last frequency band will have a range of 9.99
MHz to 10 MHz and would be represented by an integer value of 999.
So, the band ranging 0-10MHz with a step size = 10KHz, gives integer values
ranging from 0 to 999, as provided by the following table 4.1.
Integer value
Frequency Band01
0-10KHz 11-20KHz

2
21-30KHz

……
……

999
9.99-10MHz[Table 4.1] Representation of the frequency gene.

It can be observed that each band is of 10 KHz or .01 MHz and there are a totalof 1000 frequency bands and one of these is allocated to each requesting
application. The user specifies its desired frequency band as an integer value,with in a range of 0-999. Therefore, an error message should be generated if theuser specifies a value out of this range.
In the mutation operation these frequency bands should be converted into thecorresponding binary representation. As each of these frequency bands needs
10 bits to represent, the frequency gene therefore will take 10 bits in the initial
population of chromosomes, for the frequency part. This will be explained when
we shall come to the mutation operation details, further in this research.

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Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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4.1.2. The Power:
One of the other important parameters that we shall consider for thechromosome structure definition is the power. This is the power required byapplication to provide for a good transmission, a power less than that may resultin the transmission of a weak signal and might not be useful in most of the cases.
The range of power values should be specified in such a way that allows theusers to communicate easily without giving too many errors, as we know that
increasing the power for a transmission of a signal increases the signal strengthand hence increases the chances of a successful communication process. Also,
increasing the power of a signal the chances of production of the number of
errors decreases, as is desired of any communication system.
The power range varies from -30dBm to 30dBm with a step size of 1 dBm in
between them. This gives a total of 61 values to be represented with an integer
value. The lowest power range i.e. from -29dBm to -30 dBm with a step size of 1
dBm would be represented by the integer value “0”. Similarly with a step of 1
dBm the power would be represented by an integer value, in the increasingorder. These integer values should also be converted to the corresponding binary
representation during the mutation operation, as the mutation is applied on bit
level only. The representation of these 60 values will require a total of 6 bits toenable the user to know whether the given input is with in the correct range ornot. Here again a check is maintained to keep these values in the range from 0 to
60. An error message should be declared otherwise.
The power gene would be the second one in the order of our chromosome’s
structure and a range from –30dBm to 30dBm with a step size of 1 dBm isrepresented in the corresponding integer values illustrated in the following table
4.2.
Integer value
Power (dBm)

0
-30

1
-29

2
-28

………
………

30
0

…………
…………

59
+29

60
+30

[Table 4.2] Representation of the Power gene.

The user inputs an integer value that it wants to be mapped against its required
power. For instance, if an application requires a power of 15 dBm for its
communication, it must enter an integer value that has been mapped against thispower value. i.e. 45 would be the value that has the corresponding value of 15
dBm mapped to it, according to the above table. This mapping of the actual
values in the integer form makes the values for our decision-making process
much simpler to manipulate.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

4.1.3. The Bit Error Rate (BER):
Another parameter that we shall consider in our chromosome’s structure is the“Bit error rate” (BER) that represents the radio’s average number erroneous bits
for a particular transmission. It is of great importance to know the BER as it
works as a specification and depends upon certain applications. The reason thatthe errors occur is not that much significant when we consider the bit error rate. It
depends on the application that requires the spectrum allocation that specifies
this bit error rate. There are some applications (error sensitive applications) that
require a low bit error rate for the transmission. These applications can sacrificea high bandwidth for that, for example the VoIP applications. On the other hand
there are applications that can compromise with a higher error rate but demand ahigh bandwidth (known as bandwidth sensitive applications), for example thevideo streaming applications.
As there are more chances of errors in the wireless communications than the
traditional wired circuits, the applications need to have some procedure to reduce
the occurrence of these errors, to the possible extent. The bit error rates caneither be reduced by the use of certain coding schemes at the receiver and/or at
the transmitter. Another way to reduce the bit error rates is to increase thetransmission power of the device.
In this research we shall consider the bit error rates with in a range of 10 −1 and10 −8 . This range fulfills the requirements of the most of the mobile applications.
The step size that we shall consider for the bit error rate would be 10 −1 . This will
give us eight different values of BER. Each of these values would be represented
by an integer value, in the same manner as done in the frequency and powercases, for the chromosome definition.
These values in decimal for BER would also be converted into theircorresponding binary values for the mutation operation.
The following table 4.3. Illustrates the way these eight integer values are
mapped against their corresponding BER values.
Integer value

BER

0

10

1
−1

10

2
−2

10

3
−3

10

4
−4

10

5
−5

10

6
−6

10

7
−7

10 −8

[Table 4.3] Representation of the BER gene.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

4.1.4. The Modulation Scheme:
Yet another, and the last of the four genes to be considered in ourchromosome’s structure definition is the modulation gene. There are a total offour modulation schemes that will we shall consider in this particular research.
They are BPSK, QPSK, GMSK, and 16QAM. So, to represent these four
schemes we need just 4 integers. The following table 4.4 illustrates the way
these four schemes are mapped to the integer values.
Modulation Scheme
BPSK
QPSK
GMSK
16 QAM

Integer Value0
1
2
3[Table 4.4] Representation of the Modulation gene.

So, in order to represent just four modulation schemes in binary we shall need
only two bits. Note that this binary representation will be required for the mutation
operation as that can be performed on the bit level, only. After mutation they areconverted back to the decimal notation, as in the other genes.
It is the application that provides its requirements for the modulation schemethat it will use, as was the case with all the other genes. The cognitive radio, by
its decision-making process, can then provide with the best possible match fromthe pool of solutions available to it. It maintains a trade-off among all the genes in
the chromosome’s structure according to the corresponding weight assigned toeach of them by the user itself.
4.1.5. The Chromosome Structure:
The above-mentioned four genes all together define our chromosome’s
structure. The order of their representation in the chromosome structure is thesame as the order in which they are explained in this report. The whole structureof the chromosome and the order of each of the gees can be explained with thehelp of the following table 4.5.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

Order
1
2
3
4

Gene
Frequency (F)Power (P)Bit Error Rate (BER)
Modulation Scheme (M)

Range
0-999
0-60
0-7
0-3

[Table 4.5] The chromosome structure representation.

As the above table explains, the frequency band is the very first gene in thechromosome structure and can have any value between 0-999. The power gene
follows the frequency gene in the order and can have any value between 0-60.
The third gene in the chromosome structure is the BER gene and can have any
value between of 0-7. The last gene in the order of the chromosome structure is
the modulation gene that can have a value between 0 and 3.
The user or the application specifies its requirements to the cognitive radio as achromosome and asks for an optimized solution that fulfills these requirements.
The optimized solution also has the same structure as that of the chromosome.
The representation of these four parameters in a chromosome requires 21 bits
in total, i.e. 10 + 6 + 3 + 2 = 21 bits.
As mentioned earlier the conversion from the decimal to the binary system isnecessary as the mutation operation is carried out at the bit level. The
corresponding binary representation of the above chromosome will look as
follows. Table 4.6.
Gene
Frequency
Power
Bit Error Rate
Modulation Scheme

Binary representation
29 28 27 26 25 24 23 22 21 20
25 24 23 22 21 20
22 21 20
21 20
Total

Number of Bits
10
6
3
2
21

[Table 4.6] Bit wise representation of the chromosome.

4.2. The First Population Chromosomes Generation:
Once the chromosome structure has been defined the next issue of concern isthe generation of the very first population chromosomes. This is done randomly
and there are no particular rules about the size limitations in the first populationof the chromosomes. To begin with, we can start with the generation of an initial
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

population of 100 chromosomes. This population size can be increased if theresults are not satisfactory, in the provided solution set.
This randomly generated first population of chromosomes then undergoes anumber of operations like selection, crossover and mutation, during the fitness
tests in the decision-making process and results in the next generation ofchromosomes.
These first population chromosomes are not only selected randomly but also
have random values for each of the gene specified in these chromosomes. So,
this randomly generated first generation population has diversity among thesolutions provided in it. Some of these solutions may be the good ones while theother ones may be the worst solutions. In order to increase the possibility of goodsolutions in the next generation we apply the fitness function to this initial
population and perform operations like selection, crossover and mutation.
The initial population of the chromosomes can be illustrated with the help of thefollowing Table 4.7.
Chromosome No.
1
2
3
…………
…………
99
100

Frequency
F1
F2
F3
…………
…………
F99
F100

Power
P1
P2
P3
…………
…………
P99
P100

BER
B1
B2
B3
…………
…………
B99
B100

Modulation
M1
M2
M3
…………
…………
M99
M100

[Table 4.7] Initial population Generation.

Before performing operations like crossover and mutation there is a need for thedefinition of a fitness function. This fitness function is basically responsible for thegeneration of the next generation population chromosomes. This fitness function
checks for the fitness of the chromosomes in the initial population of 100
chromosomes and passes the fittest of them to the next generation population.
A rough algorithm for the whole decision-making process can be plotted asshown in the following figure 4.1.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

Page 42Decision Making Techniques For Cognitive Radios[Figure 4.1.] The flow diagram of the process of decision-making.

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Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

4.3. The Fitness Function:
Once having the structure of the chromosome been defined and having
generated the first generation population of the chromosomes, we move to next
step, i.e. the fitness evaluation of each chromosome in the population. This first
population of chromosomes has a diverse pool of solutions to meet the specified
Qos requirements of the user or the application. Some of these solutions may
exactly satisfy the Qos required while others may just get close to thosespecifications. So, we can choose among this pool on the basis of the trade-off
conditions mentioned earlier. The important thing to notice here would be thatsome of these solutions might prove to be the worst of all. So, there is a need forthe definition of a test for these chromosomes individually such that thepossibility of the transfer of the chromosomes leading to worst solutions, to thenext generation can be minimized.
It is quite necessary to completely identify and define a fitness function
according to the genes of the chromosomes, before we move forward because
without this we shall not be able to perform the operations such as, selection,
crossover, and mutation, in order to move to the next generation population. So,
the next generation population of chromosomes is not possible without thedefinition of a fitness function. This is also obvious from the fact that it is thefitness function that enables us to select the best of the solutions and then apply
the crossover and mutation operations on these solutions to have an optimum
solution. So, in the absence of a fitness function we would not be able to get
optimal solutions.
In order to have something to start off with we can define the simplest of thepossible fitness functions, i.e. a fitness function that is equally dependent on all
the four parameters, defined above, as its genes. Although this might not be thepractical case in communication systems, as a change in any of these fourparameters affects the other parameters (genes) in reality, yet theoretically it
provides us with the simplicity. All the four parameters will have an equal weighti.e. 25% each, for this particular case.
The given user’s QoS requirements are compared against the population ofchromosomes. Their fitness is calculated as the cumulative sum of the individual
fitness of each gene (parameter) according to the procedure described in thefollowing sub-sections.
4.3.1. Fitness of The Frequency Gene:
The fitness of the frequency gene is calculated by just taking the absolutedifference between the frequency band of the chromosome under consideration
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Master of Science in Electrical Engineering
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Decision Making Techniques For Cognitive Radios

and the frequency band requested by the user (application), as described by thefollowing equation 4.1.
Difference = | (frequency band - QoS frequency band) |(4.1)

We can specify by ourselves the number of frequency bands that we shallconsider, for the fitness calculation, on either side of the frequency band
requested by the application. Let us generalize this consideration to an integer
variable “p”, where “p” can have any value up to 999. Where the limit 999 refers
to the maximum range the frequency gene can have, as described in thesections above.
If the absolute difference calculated above is less than this specified value of“p”, we further use the following equation 4.2 in the fitness calculation of thefrequency gene.
 x ⋅ frequency band - QoS frequency band (4.2)
Fitness of frequency gene = pThe above equation provides for the calculated fitness of the frequency gene.
The variable “x” in the equation is the associated weight that we assign to eachof the genes in a chromosome. For example in our case considered above, “x”
having a value of 25 implies that the frequency gene counts for 25% of the total
fitness of the respective chromosome. So, we can vary the assignment of weightby changing this value of “x” according to its respective participation of the genein the total fitness calculation for the chromosome.
If the equation 4.2 above results in an absolute value that were greater than thespecified value of “p”, then this value of “x” itself would have been fitness of thefrequency gene, given by the following equation 4.3.
Fitnessof frequencygene= x(4.3)

The more the fitness of the frequency gene would be, the less suitable solution it
would be. So, it gets fitter with the value of fitness of the frequency geneapproaching to zero. This can be explained by the following example, more
explicitly.
Let the user specifications of the QoS for the frequency band is 850, with aweight of 25% for the frequency gene in the total fitness, as considered in ourcase. This implies that “x” has a value of 25. Also, let us consider a value of 300
for “p”. The fitness of two of the chromosomes, one with a frequency band of 712
and the other with a value of 345, in their frequency genes, is required.
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Master of Science in Electrical Engineering
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Decision Making Techniques For Cognitive Radios

The fitness of each of the frequency genes would be calculated as follows.
The frequency gene with a frequency band of 712 would result in following
calculations.
Difference = |712 - 850| = 138, which is less than the specified p = 300.
So, we move to equation 4.2,
Fitness of the frequency gene = [{25 * 138}/ 300] = 11.5
For the other case, with its frequency gene having a frequency band of 345, thefitness calculation would be done as follows:
Difference = |345 - 850| = 505, which is greater than p = 300
So, the fitness of the frequency gene would be the value of “x” itself and equation
4.3 would be used, in this case. i.e.
Fitness of the frequency gene = 25.
So, comparing the above two results, the value of the fitness function in thesecond case is higher than that in the first one and leads to a worse result i.e. far
from the desired solution. So, it clarifies that the solution gets closer to thedesired results as the value of the fitness of the frequency gene approaches tozero.
4.3.2. Fitness of The Power Gene:
The fitness of the power gene is also calculated in the same way, as for thefrequency gene fitness calculation, i.e. we consider the absolute of the difference
between the power value of the chromosome under consideration and the power
requested by the user or application. The following equation 4.4 is used to
calculate this difference.
Difference= | (power- QoS power) |(4.4)

Here also, we can specify by ourselves the number of power values that weshall consider on either side of the power requested by the user or theapplication. Let this be denoted by a variable “q”. Where the “q” can be any value
between 0 and 60. Note that, the maximum value of 60 refers to the maximum
range that the power gene can have, as described in the sections above.
Now if the difference calculated using the above equation 4.4 is less than our
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Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radios

specified value “q”, then we use the following equation 4.5 for the fitness
calculation of the power gene.
 y ⋅ power - QoS power 
Fitness of power gene = 

q



(4.5)

The variable y in the equation above is the associated weight that we assign toeach of the genes in a chromosome. For example in our case, the value y= 25
implies that the power gene counts for 25% of the total fitness of the respective
chromosome. So, we can vary the weight of each gene in the total fitness of achromosome by varying the value of “y”.
If the difference in equation 4.4 were greater than the value of “q” then thevalue “y” itself would have been the fitness of the power gene, described by thefollowing equation 4.6.
Fitness of power gene = y(4.6)

The more the value of fitness of the power gene, the less suitable the solution
would be. So, there exists an inverse proportion between the value of thesuitability of the solution and the value of the fitness of power. This can be
explained by the following example more explicitly.
For example, a user asks for a power of 47, with q =12 and a specification of y
= 25, as its corresponding weight in the total fitness. We consider two
chromosomes for the calculation of their fitness. One with power value of 54 andthe other with 16.
For the power gene with the power value of 54, the fitness would be calculated
in the following way:
Difference = |54 - 47| = 7 that is less than q = 12
So, equation 4.5 would be used and the fitness of power gene is,
Fitness of power gene = [{25 * 7}/ 12] = 14.58
For the other chromosome with a power value of 16, the power fitness would be
calculated in the following way:
Difference = |16- 47| = 31, which is greater than q = 12
So, equation 4.6 would be used and the fitness of power gene is
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Master of Science in Electrical Engineering
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Decision Making Techniques For Cognitive Radios

Fitness of power gene = 25
So, comparing the above two results, the value of the fitness function in thesecond case is higher than that for the first one and leads to a worse result i.e.
far from the desired solution. So, it clarifies that the solution gets closer to thedesired results as the value of the fitness of the power gene approaches to zero.
4.3.3. Fitness of The BER Gene:
The fitness of the BER gene is also calculated by considering the absolutedifference. That is the difference between BER gene of the chromosome underconsideration and the BER requested by the user (application). The following
equation 4.7 is used to calculate the difference.
Difference = | (BER - QoS BER) |(4.7)

Here also, we can specify by ourselves the BER values that we shall consider
on either side of the BER requested by the user or the application. Let this bedenoted by a variable “r”. Where “r” can be any value between 0 and 7. Note
that, the maximum value of 7 refers to the maximum range that the BER genecan have, as described in the sections above.
Now if the difference calculated using the above equation 4.7 is less than our
specified value “r”, then we use the following equation 4.8 for the fitness
calculation of the BER gene.
 z ⋅ BER - QoSBER 
Fitness of BER gene = 

r



(4.8)

The variable “z” in the equation above is the associated weight that we assign toeach of the genes in a chromosome. For example in our case, the value z= 25
implies that the BER gene counts for 25% of the total fitness of the respective
chromosome. So, we can vary the weight of each gene in the total fitness of achromosome by varying the value of “z”.
If the difference in equation 4.7 were greater than the value of “r” then the value“z” itself would have been the fitness of the BER gene, described by the following
equation 4.9.
Fitness of BER gene = z

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system(4.9)

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Decision Making Techniques For Cognitive Radios

The more the value of fitness of the BER gene, the less suitable the solution
would be. So, there exists an inverse proportion between the value of thesuitability of the solution and the value of the fitness of BER. This can be
explained by the following example more explicitly.
For example, if a user asks for a BER of 10 −5 and specifies 25% (z=25) as theweight of the BER gene in the total fitness and the value of “r” as 3, and asks forthe calculation of the fitness of two chromosomes, one with a BER value of 6 andthe other with BER value of 1, in their BER genes.
The fitness for the BER gene having BER 6 would be calculated in the following
way:
Difference = |6 - 4| = 2 that is less than r = 3
So, equation 4.8 would be used and the fitness of BER gene would be,
Fitness of BER gene = [{25 * 2}/ 3] = 16.6
The fitness for the BER gene having BER value of 1 would be calculated in thefollowing way:
Difference = |1 - 4| = 3, which is equal to r = 3
So, equation 4.9 would be used, in this case and the fitness of BER gene is
Fitness of BER gene = 25
So, comparing the above two results, the value of the fitness function in thesecond case is higher that leads to a worse result i.e. far from the desired
solution. So, it clarifies that the solution gets closer to the desired results as thevalue of the fitness of the BER gene approaches to zero.
4.3.4. Fitness of The Modulation Gene:
The fitness of the modulation gene is calculated in a slightly different and simple
way than that for the other three genes. It just compares the modulation gene of
the considered chromosome and that of the QoS requested by the user
(application). The following equation 4.10 is used to calculate the fitness of themodulation gene.
Difference = | (Modulation - QoS Modulation) | = 0

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Master of Science in Electrical Engineering
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Decision Making Techniques For Cognitive Radios

But a fitness of 100% is achieved only if the difference calculated using theabove equation is zero, otherwise the fitness itself becomes zero.
If the difference is zero then, the fitness of modulation gene = 100% and it
perfectly fits to the modulation requested by the user in its QoS, otherwise weassign the fitness of the modulation gene its corresponding weighing factor “w” inthe total fitness, illustrated in the following equation 4.11.
Fitness of Modulation gene = weighting factor (w)(4.11)

This can be explained by the following example.
Lets suppose that the user asks for a modulation of BPSK (with the integer
value of 0, assigned in the sections above) and the chromosome has modulation
gene of QPSK (integer value of 1, assigned in the sections above). As we havesupposed equal weight for all four parameters, let the weight assigned to themodulation gene in total fitness be 25%, thenDifference = | (2– 1) | = 1
So, the Fitness of the modulation gene = 25, as the difference is not exactlyzero.
If it had matched the modulation technique requested by the user i.e. BPSK
(integer value of 0), then the fitness would have been 100% and had resulted in a
fitter result. So, the difference zero refers to the fittest modulation gene andbecomes less fitter as it approaches to the corresponding weighing factor in total
fitness, in contrast to the behavior of the other genes described above.
4.3.5. Total Fitness:
The Total Fitness is calculated by just summing up all the individual fitness
values of the four genes of a single chromosome. A larger value of the total
fitness would refer to a less fit chromosome whereas a smaller value would standfor a better solution. The Total Fitness of a chromosome is given by the following
equation 4.12.
Total Fitness = [Fitness of Frequency gene + Fitness of Power gene +Fitness of BER gene + Fitness of Modulation gene](4.12)

To get the values of total fitness of the chromosomes we calculate them inpercentages, using the following equation 4.13
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Master of Science in Electrical Engineering
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Total Fitness (%) = 100 - [Fitness of Frequency band gene + Fitness of Power gene +Fitness of BER gene + Fitness of Modulation gene](4.13)

So, in the comparison of the total fitness of the chromosomes in percentages,
the higher the Total Fitness (%), the better the solution would be and vice versa.
The variables “x”, “y”, “z” and “w” described in the sections above are thecorresponding weighting factors of the frequency gene, power gene, BER geneand the modulation gene, respectively. The sum of all the weighting factors of thefour genes should be equal to 100. That is; w + x + y + z = 100%.
With all this background, we use two approaches for the decision-making thatlead to the optimization for the best solution. Both these approaches discussed in
the following sections just slightly differ from each other in their implementation
part only, but both aim at the same goal, i.e. optimization for the best solution.
We shall also include a working matlab code for both these methods along withthe conclusions and generalized behavior of the G.A.s, in the research.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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4.4. Method 1:
The procedure followed in this method basically involves the selection of thefittest of the available chromosomes among the available pool of solutions.
Certain operations like, crossover and mutation are performed on these selected
chromosomes. This results in an evolution of a new generation population ofchromosomes and we choose the best of the chromosomes on the basis of theirfitness values, according to our defined fitness function. The process is explained
in detail in the following sub-sections.
4.4.1. Selection:
Selection involves the selection of the best offsprings within the pool ofavailable chromosomes. This selection is done in accordance with our own
defined fitness function, described in the previous sections. The “Roulette Wheel
selection” method, also known as “Fitness proportionate selection” method isused for the selection purpose. It is basically a genetic operator generally used in
the genetic algorithms for the selection of potentially useful solutions forrecombination in the next generation. It associates a probability of selection witheach individual chromosome, using the following equation 4.14.

P(i) = Nf (i)∑ f (i)(4.14)

j =1

Where, P (i ) is the associated probability of the individual chromosome among
the pool, f (i ) is the fitness of an individual i in the pool and N is the total
number of chromosomes in the population.
So, the chromosomes with the highest probability value would be transferred to
the next generation. This enables a mechanism that does not to allow thetransfer of the lower fitness value chromosomes to the new population ofchromosomes. After each generation the fittest chromosomes are transferred to
the new population until the new population reaches the maximum defined limit.
Once the fittest chromosomes among the pool have been selected, we canperform the operations like crossover and mutation (explained in the previous
sections), as we move towards the optimization.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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4.4.2. Crossover:The next step after the selection of the fittest chromosomes available in the poolis, to perform the crossover operation. The crossover operation is performed on apair of chromosomes, selected randomly. The operation is performed at thedefined crossover points that define the junction of the genes in the chromosome
structure. The following table 4.8 illustrates explicitly the range that the crossover
points may exist.
Gene
F
P
BER
M

Range for the definition of crossover points.
0-999
0-60
0-7
0-3

[Table 4.8] The range that crossover points may exist.

In a single iteration the crossover is performed randomly at one of thesecrossover points. The following table 4.9 illustrates a crossover point between
two chromosomes, at the juncture of frequency and the power gene (highlighted
in gray).
Gene
F
P
BER
M

Chromosome 1
861
23
5
1

Chromosomes 2
34
57
2
3

[Table 4.9] Two chromosomes before the crossover.

This crossover point in the above table marks a point at which the crossover
operation to be performed. The part of both the chromosomes after the crossover
points (highlighted in green) is swapped with each other. This results in totallynew combination with new values for each of the genes. The following table 4.10
illustrates the resulting combination after the crossover operation.

Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Gene
F
P
BER
M

Chromosome 1

Chromosome 2

861
57
2
3

34
23
5
1

[Table 4.10] The chromosomes after Crossover.

So, the crossover operation gives rise to new solutions in the solution space forthe problem.
Usually, the possibility for the crossover operation to occur between two
chromosomes is about 90%. This means that out of every 100 times thepossibility for the crossover operation to occur is 90 times and where as, there isa possibility of only 10 times out of 100 that the original chromosomes would notchange and there would be no crossover. It is the user that decides for the
crossover rates and can vary them.
4.4.3. Mutation:
The next step that follows the crossover is the mutation operation. The
crossover operation did not need any conversion, i.e. the operation was
performed on a randomly selected pair of chromosomes on the same decimalvalues of the genes. This is not the case with mutation operation. The mutation
operation involves the conversion to corresponding binary values withconsideration of only one chromosome at a time. In mutation, a randomly
selected bit belonging to any of the four genes is inverted. The random selectionof the bit indicates that it can be any of the bits from the total of 21 bits (explainedin the previous sections), representing the chromosome. The following table 4.11
illustrates the detailed structure of the chromosome represented in binary format.
Gene
Frequency
Power
Bit Error Rate
Modulation Scheme

Binary representation
29 28 27 26 25 24 23 22 21 20
25 24 23 22 21 20
22 21 20
21 20
Total

Number of Bits
10
6
3
2
21

[Table 4.11] The random selection of the bit for mutation operation.

The mutation operation can be explained by considering an example. Let there
be a chromosome with the following structure, table 4.12, in the decimal notation.
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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F
678

P
34

BER
3

M
2

[Table 4.12] Considered Chromosome Structure.

Let us assume that the randomly generated bit position to be mutated is 15.
Then the mutation operation on this chromosome would be performed in thefollowing way.
First of all convert the decimal values, of each gene (in the same order) to theircorresponding binary notations. The binary conversion in for this example would
result in following bit pattern. Table 4.13.
F
1

0

1

0

1

P
0

0

1

1

0

1

0

0

0

1

0

0

BER
1 1

M
1

0

[Table 4.13] Binary Representation of the Chromosome.

Where the 15th, (falls in the power gene range) bit (in red font) is the one thatwill undergo the mutation operation. It falls in the power bit range. The mutation
operation will just invert the 15th bit. i.e. it is changed to “0”, in this case,generating the following bit pattern after the mutation process, as shown in thetable 4.14.
F
1

0

1

0

1

P
0

0

1

1

0

1

0

0

0

0

0

BER
0 1 1

M
1

0

th

[Table 4.14] Inversion at the 15 bit as a result of mutation.

Where the “0” at the 15th bit (in red font), indicates the inversion, after the
mutation operation.
Now we can convert this resulting bit pattern back to decimal notation again.
The Corresponding values in decimal notation after the mutation operation canbe represented as follows. Table 4.15.
F
678

P
32

BER
3

M
2

[Table 4.15] Resulting chromosome after mutation.

Note that this is not the same chromosome as the original one, i.e. before themutation operation. So, the mutation operation also, provides with new solutions
Blekinge Tekniska Högskola
Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Decision Making Techniques For Cognitive Radiosin the solution space for the problem. The selection procedure for the selection ofchromosomes for the mutation operation is the same as that for the crossover
operation.
The possibilities for the mutation operation to occur are not that high as that forthe crossover operation. Usually it is between 2% to 5%. For example if themutation possibility is 3% then it means that the possibility for the mutation rate
to occur, is just three times out of every 100 times, on a chromosome. If themutation is not performed then the chromosome remains the same.
4.4.4. Fitness Calculation:
As the crossover and mutation operations resulted in the production of somenew chromosomes in the solution space for the problem. We apply the same
fitness tests, as in sections under 4.3, explained earlier in this research, toensure the selection of the fittest chromosomes among the population.
4.4.5. The New Population Generation:
The operations defined above along with the fitness tests have been performedon each of the chromosomes in the initial population and the new chromosomes
produced after these operations. The fittest chromosomes among them aretransferred to a new set. This is the new generation population set. The size of
this set is equal to the new generation population. The values in the genes forcertain chromosomes would differ from those in the initial population. This is dueto the operations performed during the process, explained in the sections above.
For example, if the initial population in a certain case looked as shown in thefollowing table 4.16.
Chromosome No.
1
2
…………
…………
99
100

Frequency Bands
F34
F861
…………
…………
F456
F589

Power
P23
P57
…………
…………
P39
P14

BER
B5
B2
…………
…………
B4
B1

Modulation
M3
M2
…………
…………
M1
M0

[Table 4.16] Initial population table.

The resulting new generation population after the operations defined in theabove sections may look like the following table 4.17.

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Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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Chromosome No.
1
2
…………
…………
99
100

Frequency Bands
F34
F765
…………
…………
F646
F589

Power
P16
P57
…………
…………
P39
P23

BER
B1
B4
…………
…………
B2
B3

Modulation
M0
M1
…………
…………
M3
M3

Table 4.17] The new generation chromosomes.

4.5. The Optimal Solution:
The above-mentioned steps in the process are repeated and a new populationof chromosomes is generated at every generation and the fitness tests are
performed on them. This process continues unless the maximum number of
specified populations has been reached. After the application of the fitness teston the last population, the chromosome with the maximum fitness value among
all is chosen. This is our desired optimized result. It may or may not exactly
match the requested solution but it will definitely be the closest of all the available
solutions in the solution set.
This completes our decision-making process, for this method. Now we shallmove to the other method that we shall include in this research for the decisionmaking process.

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Master of Science in Electrical Engineering
Department of Telecommunication/Signal Processing/Internet system

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4.6. Method 2:
The approach in this method is a bit different from the one adopted in theprevious method. Most of the operations like crossover and mutation are thesame as in the previous method, but they differ in the respect of theirimplementation. The main differences are that the “Roulette wheel selection” isnot used anymore instead the concept of elitism is used for the selection in thismethod.
4.6.1. Elitism:
The word “Elitism” comes from the word “Elite” that means the ones with themost distinguished attributes. Elitism used here in our decision-making process
selects the best among the population of chromosomes and transfers them to thenext generation, using the defined fitness function. It actually copies a few of thebest chromosomes to the new population before the other G.A. operations are
performed on that population and can increase the performance of G.A. as it
prevents the loss of the best possible solution. [15]The number of chromosomes to be picked up to be transferred to the next
generation after the Elitism operation is controlled by the user and can varied.
But we shall consider the selection only two of the fittest chromosomes from ageneration of population and transfer them to the next generation. This does not
require the crossover or mutation operations. So, we have two fittest
chromosomes among the population, just using Elitism.
4.6.2. Crossover:
The crossover operation is performed in the same manner as described formethod 1, in the section 4.4.2. There is no difference in the operation but the only
difference here in this method is with the meaning of the crossover rate. The
crossover rate here in this method, stands for the number of chromosomes thatwill undergo the crossover operation, rather than the possibility that the operation
will occur. So, a crossover rate of 90% in this method would mean that 90
chromosomes (45 pairs) out of every 100 among the population will undergo thecrossover operation and would be transferred to the next generation.
4.6.3. Mutation:
Mutation operation is also performed in the same manner as in method 1, insection 4.4.3; but the difference here is the way it is implemented in this method.
There is no difference in the operation but only the meanings of mutation rate aredifferent. The mutation rate here in this method, stands for the number of
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Master of Science in Electrical Engineering
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chromosomes that will undergo the mutation operation, rather than the possibility
that the operation will occur. So, a mutation rate of 3% in this method would
mean that 3 chromosomes out of 100 among the population would undergo themutation operation and would be transferred to the next generation. The bit
position for the mutation operation is chosen randomly even in this method.
The difference in this method is the random selection of chromosomes for the
mutation operation, rather than the use of “Roulette Wheel” for the selection
purpose, as in method 1.
The rest of the procedure after the mutation operation, that is fitness calculation,
new population generation and getting an optimal solution at the end of theprocess are the same, as described in the subsections 4.4.4, 4.4.5 and 4.4.6 formethod 1.
4.7. Matlab Results:
The QoS specifications are specified by the user (application) and include thefour parameters (genes), mentioned earlier in the research. The specification by
the user for decision-making should include the following four parameters.
User’s QoS frequency Band specification (F) for operation.
User’s QoS power specification (P) for operation during the communication.
User’s QoS Bit error rate (BER) specification.
User’s QoS Modulation scheme specification for communication.
The following QoS specifications in table 4.18 were given as an input andcalculations were performed five times, using the same input. Both method 1 andmethod 2 use the same input specifications, to perform their calculations. Boththese methods follow the methodology defined earlier in the report, to find the
optimal solution. They take the QoS specifications as an input from the user andinvolve themselves in a decision-making process and evaluate the fitness ofeach chromosome in the solution pool at each generation. The optimal solution
thus found by the G.A. might not match exactly the QoS specifications requestedby the user but is the closest possible in the solution pool, for each gene. Thisbehavior of the G.A. is due to the presence of multiple genes in the chromosome
structure and trade-off that exists among them. This behavior of the G.A.s is
explained in the next section in the report.
Frequency
623

Power
35

BER
4

Modulation
2[Table 4.18] Input specifications to test the codes, for Method1 and Method 2.

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The corresponding outputs produced by method 1, for the above input are given
in table 4.19.

S#
1
2
3
4
5

Matlab Results Using Method 1
Frequency Power Bit-Error Rate Modulation
427
34
4
1
789
37
4
1
782
45
4
1
648
44
3
1
603
49
3
2

[Table 4.19] Results for the input in table 4.18, using Method 1.

The corresponding outputs produced by method 2, for the above input are given
in table 4.20.

S#
1
2
3
4
5

Matlab Results Using Method 2
Frequency Power Bit-Error Rate Modulation
464
32
4
1
870
34
4
1
641
40
4
1
835
31
4
1
669
41
6
1

[Table 4.20] Results for the input in table 4.18, using Method 2.

Note that, the output or the optimal solution found by the G.A. is also achromosome having the same structure as that of the input given to it, as QoS
specification. Also, remember that, both the methods work using random values
for each gene and can produce very different values for the chromosome
structure each time they are run. Hence, they might not allocate the same values
for each gene much often.

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4.8. Conclusions & Behavior of G.A.s:
Each of the four parameters mentioned above contribute for the total fitness ofthe chromosomes, in accordance with their respective weights assigned to themby the user (application). This weight factor plays an important role in theselection process, discussed earlier. The genes allocated more wattage in thedecision-making process will have higher fitness values than those with lower
wattage, with increasing number of generations. Also, the user (application)
enjoys the privileges to control the number of values that it considers on eachside of its requested QoS specification, for each gene. Using the same seed forthe initial population ensures the generation of the same population every timei.e. the experiments would be performed on the same population. If we keep the
population size the same throughout our operation and vary the number ofgenerations only, we can study the behavior of both individual fitness for eachgene and the total fitness values in our operation. I.e. a study of the behavior of
G.A.’s operation “Fitness Vs the Number of Generations” can be carried out.
The researches show that the fitness of the individual genes (frequency, power,
bit-error rate and modulation in our case) increases with increase in number ofgenerations, but this behavior is not always absolutely linear rather it would havedips at certain points. This behavior of the G.A.s is due to the presence of othergenes in the chromosome structure that affect the decision-making process, toreach an optimal solution. If the behaviors of individual genes for the same
experiment are compared, they show almost an opposite behavior to each otherat the same instances against the number of generations. This is because of thefact that the optimal solution reached by the Genetic Algorithms may have tocompromise for a lower value of an individual gene to have a better fitness value
for another gene in the structure at the same instant and hence obtain a betteroverall fitness of the chromosomes. So, the G.A.s actually go for the closest
possible values for each gene among the available pool of solutions. Also, therange for decision-making associated with each gene affects the decision-making
process. A gene with a smaller range to choose (modulation gene in our case)would have a higher fitness value, while the one with a larger range to choose(frequency gene in our case) will enjoy a lower fitness value in the optimal
solution found by the G.A.s, over the number of generations. This implies that
although, the individual fitness values for the genes may not increase in thesame manner, yet the total fitness values stay above 80% throughout thegenerations, to find the closest possible optimum values in the available pool ofsolutions.
The following figure, figure 4.2 represents the behavior of the G.A.s for the total
fitness of the chromosome with increasing number of generations, to find the
optimum.
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Decision Making Techniques For Cognitive Radios[Figure 4.2.] The plot for total fitness for chromosomes versus the number of generations.
It shows that despite of the existence of the trade-off and the difference in the range forthe individual genes, the total fitness stays over 80% throughout the decision making
process to find the optimum.

The following figures, figure 4.3 and 4.4 represent the trade-off relationships
among the plots for the individual genes in the chromosome structure for method
1 and method 2, respectively. The plots are obtained using values from table
4.19 and table 4.20 for method 1 and method 2, respectively. The trade-offamong the individual genes and the affect of the range in the genes that exist
among them to reach an optimized solution are obvious in the plots.

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Decision Making Techniques For Cognitive Radios[Figure 4.3.] The plots for individual genes in the chromosome structure that show thetrade-off relationships among them and the affect of range for each gene, obtained using
the values from table 4.19 for method 1.

[Figure 4.4.] The plots for individual genes in the chromosome structure that show thetrade-off relationships among them and the affect of range for each gene, obtained using
the values from table 4.20 for method 2.

Another factor that can affect the fitness values in the decision-making to find the
optimum is the size of the initial population. If we increase the size of initial
population and keep all other factors, e.g. the associated wattage to each gene in
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the structure constant and analyze the individual fitness and the total fitness withincreasing number of generations, we can study the behavior of the G.A.s for ourdecision-making process.
The researches show that the fitness values for chromosomes again increase
with the increase in number of generations. These fitness values obtained byusing a bigger initial population would have greater values than those with asmaller initial population size. So, it can be concluded that increasing the initial
population size provides for better fitness values over the number of generations.
This may also result in an increase in the computational complexity for thedecision-making. So, the trade-off between the computational complexity and theinitial population size should always be considered.
So, summarizing the above discussion, we can simply state that the fitness
values for chromosomes increase either by increasing the size of the initial
population or by increasing the number of generations, or both of them.
4.8. Vision in Future:
The aim of this research was to explain the decision-making process involved in
the cognitive radios, mainly the use of G.As for the purpose. We considered only
the case of a single user system capable of allocating all the parametersconsidered i.e. frequency, power, bit error rate and modulation, at a single time
instant only, for the research. The allocation of these parameters to multiple
users at the same time can be a possible extension of the research in future.
Also, in this particular research, there was no mechanism defined to vacate andreallocate another band in the spectrum to the secondary user if a primary user isdetected in the same band. Hence, in future the research can be extended forthis purpose as well. This may involve the definition of holding times for thebands allocated to the secondary users so that the primary users requesting theband should always be proffered, even after the allocation.
The parameters considered for fitness function in this research wereindependent of each other. Yet another possible future extension of the research
can be the consideration of the interdependency among the parameters that exist
in the fitness function. Also, we considered only four parameters for our fitness
function i.e. frequency band, power, bit-error rate and modulation scheme only.
In future some more parameters like data rate, spectral efficiency and signal tonoise ratio etc. can also be added to the fitness function definition.

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REFFERENCES:[1] FCC, “Spectrum policy task force report,” ET Docket No. 02-155, Nov. 2002.
[2] Washington post Thursday, August 16, 2007; Page A14]
[3] FCC, ET Docket No. 03-222 Notice of proposed rule making and order, December 2003.
[4] J. Mitola et al., “Cognitive radio: Making software radios more personal,” IEEE PersonalCommunications, vol. 6, no. 4, pp. 13–18, Aug. 1999.
[5] J. Mitola, “Cognitive radio: An integrated agent architecture for software defined radio”, Doctorof Technology, Royal Inst. Technol. (KTH), Stockholm, Sweden, 2000.
[6] FCC, Cognitive Radio Workshop, May 19, 2003, [Online]. Available:
http://www.fcc.gov/searchtools.html.
[7]Proc. Conf. Cognitive Radios, Las Vegas, NV, Mar. 15–16, 2004.
[8]“Spectrum Agile Radio” by Stefan Mangold, available at http://www.comnets.rwth-aachen.de[9]http://www.cognitiveradio.wireless.vt.edu/dokuwiki/doku.php?id=research:spectrum.
[10]cognitive radio technology “communication engg series” book by Bruce Fette.
[11]S. Haykin, Cognitive radio: brain-empowered wireless communications, IEEE Journal on
Selected Areas in Communications 23 (2) (2005) 201–220.
[12] T. W. Rondeau, B. Le, D. Maldonado, D. Scaperoth, and C. W. Bostian, “Cognitive Radio[13] T. Christian James Rieser August 2004 Blacksburg, Virginia[14] http://cfpm.org/~david/talks/ga2005/web/ga-java-site/cs.felk.cvut.cz/_xobitko/ga/main.html[15] http://cfpm.org/~david/talks/ga2005/web/ga-javasite/cs.felk.cvut.cz/_xobitko/ga/selection.html[16] http://cfpm.org/~david/talks/ga2005/web/ga-java-site/cs.felk.cvut.cz/_xobitko/ga/recom.html

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