Paper-7 QoS Provisioning of Cognitive Radio using Soft Computing Techniques – A Survey

7/31/2019 Paper-7 QoS Provisioning of Cognitive Radio using Soft Computing Techniques – A Survey

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International Journal of Computational Intelligence and Information Security, May 2012 Vol. 3, No. 5

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QoS Provisioning of Cognitive Radio using Soft Computing Techniques

– A Survey

Maninder Jeet Kaur, Moin Uddin, Harsh K Verma

Department of Computer Science Engineering

Dr B R Ambedkar National Institute of Technology, Jalandhar, India

[email protected], [email protected], [email protected]

Abstract

This paper discusses QoS provisioning in cognitive radio using soft computing techniques. The comparison

of different techniques in context to cognitive radio is presented. Fuzzy logic and genetic algorithm are

reviewed. In this work we discussed the importance of QoS provisioning in cognitive radio. We reviewed the

past work done in this area by different researchers. Further, brief discussion on the approach of neural networks,

game theory fuzzy logic and genetic algorithm in the area of cognitive radio was discussed. We have

emphasized the two approaches for QoS provisioning of cognitive radio: genetic algorithm and fuzzy logic. The

previous works on the same has been stated in this chapter. We also identified some research gaps in the

literature review which were have mentioned later in the chapter.

Keywords: QoS(Quality of Service), Cognitive Radio, Fuzzy Logic, Genetic Algorithm. Neural Networks,

Game Theory.

1. Introduction

The spectrum which is a natural resource is not being utilized efficiently as shown in studies [1]. We present

a compiled report in Table 1 [2] for actual utilization of different frequency bands measured by shared spectrum

company (SSC) [3] and V. Valenta et al. [1].

Table 1 Occupancy for different frequency bands [1]

Frequency Band Maximum

Utilization

(%) SSC

Maximum

Utilization

(% ) Valenta

TV UHF 24.7 20.4

GSM 900

15.6

51.9

UMTS 3.8

CDMA 50.7

ISM 0.1 1

Total Average 1.7

(30 MHz 3

GHz)

6.96

(100 MHz- 3

GHz)

The International Telecommunication Union (ITU) allocates the spectrum frequencies to various countries

for usage. In this survey we discuss the issues faced by spectrum allocation authorities like Federal of

Communications Commission (FCC), and even Telecom Regulatory Authority of India [4] [5]. We in this survey

we emphasis on the need for change in spectrum regulatory policies and provides an insight on the optimization

techniques for dynamic spectrum allocation. The better spectrum utilization introduced the concept of cognitive

radio [6]. Further FCC recognized it being a promising technique so it is pushing its development. As a first step,

FCC proposes to experiment unlicensed cognitive sharing in TV bands [7-9]. Despite of advantages of using the

unutilized frequency bands for cognitive spectrum sharing, there are still some concerns to be solved. Firstly, can

secondary users operate without creating interference to the primary users and secondary can certain QoS

provisioning be provided under such constraints? In the present times, research work is going on in the area of QoS provisioning of cognitive radio in the context of optimization of spectrum sensing and management

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methods. In this chapter some of the major existing works on these areas have been reviewed. Also soft

computing techniques were discussed later in the chapter to give an insight how these can be utilized in QoS

provisioning of cognitive radio.

2. StandardizationMany standardization activities, such as IEEE 802.22, IEEE 1900 and some other organizations like ITU

and DARPA have contributed to realizing of cognitive radio’s potential for commercial use.

A. IEEE 802.22

Figure 1 Wireless Technologies[11]

IEEE 802.22 standard committee was formed in 2004 [10], which aim at constructing Wireless Regional

Area Network (WRAN) using cognitive radios. This is done by utilizing white spaces in already allocated TV

frequency spectrum within the television bands 54 and 862 MHz, especially within rural areas or where the

usage is lower. This is the first ever standard to fully incorporate the concept of cognitive radio. WRAN can be

compared to current wireless communication technologies when classifies to the coverage area. Some of other

important parameters and values of IEEE 802.22 are listed in Table 2.1. The specified range for IEEE 802.22 is

33 km as compared to 50 meters of IEEE 802.11 standards. This may be due to the higher power and favorable

propagation characteristics of TV bands [10] [11].

Table 2 Summarized service parameter values for IEEE 802.22 [11]

Sr. No. Parameter Value

1 Coverage 100 km

2 Average spectral

efficiency

3 bits/sec/Hz

3 Operational Frequency

Range

41-910 MHz

4 Channel Bandwidth 6, 7 and 8 MHz

5 Services Voice, data, Audio and Video

6 Throughput a. Downstream- 1.5 Mbps

b. Upstream – 384 Kbps

B. IEEE 1900

IEEE P1900 [12-13] standards committee was founded jointly by IEEE Communications Society (ComSoc)

and the IEEE Electromagnetic Compatibility (EMC) Society. The aim of this committee is to develop standards

for next generation radio and advanced spectrum management. It has following groups:

• IEEE 1900.1 - Definitions and Terminology relating cognitive radio.

• IEEE 1900.2 - Testing and verifying the operation of cognitive radio.

• IEEE 1900.3 - Approaches for qualifying software modules.

• IEEE 1900.4 - Architectural Building Blocks Enabling Network - Device Distributed Decision




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Making for Optimized Radio Resource Usage in heterogeneous Wireless Access Networks.

• IEEE 1900.a - Regulatory certification of cognitive radios.

The first draft of IEEE 1900 is now available [14]. Later, an IEEE Standards Coordinating Committee (SCC

41) was formed in 2007 [15].

C. Other OrganizationsOther major organizations and forums, working towards standardization and development of cognitive

radios, include International Telecommunication Union (ITU) [16], Defense Advanced Research Projects

Agency (DARPA) of DoD, USA [17] and Wireless World Research Forum (WWRF) [18-20].

3. Research Challenges

So far the subject of cognitive radio has been a hot research topic, resulting in numerous studies on both

technical and policy aspects, with number of research activities increasing around the world [21-28], [29]. There

are a number of next generation system standards IEEE 802.22 [7], IEEE SCC-41 that have adopted the concept

of cognitive radio. However, there are still some unresolved issues which need to be addressed while cognitive

radio implementation. Various solutions and algorithms have been proposed to solve various issues and

challenges in the implementation of cognitive radio, which aims to facilitate the wireless communication users touse the spectrum in a dynamic manner. However, the efficiency and reliability of these algorithms and solutions

is still under question. The issue of QoS provisioning in cognitive radio has not been considered much [30]. We

have therefore focused our research on the optimization of such solutions for QoS provisioning in cognitive

radio.

Due to the dynamic nature, cognitive radio needs to support time-varying quality of service (QoS)

requirements. QoS provisioning is important and relevant problem in cognitive radio scenario because of the

following reasons:

• Firstly, there is no dedicated static allocation of spectrum to the secondary users for the usage

of spectrum. As soon as the primary user needs the spectrum band the secondary user transmission is

disrupted so that it can be used for primary user functioning. Therefore the detection of the unused

spectrum band and its effective management is a challenging task.

• Secondly, in addition to the interference between primary and secondary users, the wirelesschannels also need to consider other factors such as fading, shadowing etc.

• Thirdly, because of the nature of the wireless environment, spectrum optimization is classifies

as non linear problem. Therefore problems like power control, optimization of the parameters and

system, decision making etc can vary widely depending upon the state of the wireless system.

In the next few sections, relevant existing work done in this regard has been discussed.

. QoS Provisioning in Cognitive Radio

Due to the dynamic nature, cognitive radio needs to support time-varying quality of service (QoS)

requirements. QoS provisioning is important and relevant problem in cognitive radio scenario because of the

following reasons.

Major concern is the QoS provisioning in cognitive radio, which has not been studied much by the

researchers. QoS provisioning is a challenging task in cognitive radio scenario because of varying channel

conditions and random fluctuations in received power levels. The main concern of cognitive radio is to improve

spectrum utilization and enhance communication performance by optimizing Quality of Service (QoS)

parameters. QoS is usually defined as the set of service requirements that need to be satisfied by a Cognitive

Radio system while transporting a packet from source to destination. The cognitive radio should be striving to

meet the QoS requirements exactly, not going higher or lower. Lower QoS is obviously bad. High QoS is not

desirable because it consumes available resources and is costly. QoS aspects are becoming more and more

important in each type of service provisioning.




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Table 3 Comparisons of various optimizations based on various proposals [30]

Proposals for MAC

in Cognitive Radio

Channel

Aggregation

QoS

Provisioning

Balanced usage

of

opportunities

Backup Channel

Single Radio

Adaptive Channel

Yes Yes - -

Cognitive Yes - Yes Yes

Opportunistic - - - -

Dynamic

Spectrum Allocation

Yes Yes - -

Distributed

Coordinated Spectrum

Sharing

- - - -

Cognitive Radio

CSMA/CA

- - - Yes

Cross Layer

Based Opportunistic

- - Yes -

Opportunistic

Spectrum Access

- - Yes -

Dynamic Open

Spectrum Sharing

- - - -

From the Table 3, it is concluded that QoS provisioning in cognitive radio has not been addressed by most

proposals. A support of QoS and its integration are difficult because of the missing coordination and cooperation

between the different radio systems operating in the same frequency band [31]. The communication channel has

a complicated impact on the QoS such as channel conditions, fading type, fading level, doppler spread, and path

length, will greatly impact bit error rate which ultimately translate to QoS. How to choose the proper waveform

like modulation, channel coding, interleaving and spreading are some important issues [32]. The effective

capacity and derived the optimal power and rate adaptation techniques which maximize the system throughput

under QoS constraints had been also analyzed [33]. The complexity of a cognitive radio system increases with

the increase the number of parameters and the performance objectives the system takes into account [34]. Theabove discussed works have contributed significantly to the QoS provisioning of cognitive radio. However the

approach of QoS parameters in the area has not been explored in detail.

Identification of QoS parameters in Cognitive Radio

The accuracy of the cognitive radio system depends upon the decisions made by the cognitive engine,

which further depends upon the quality and quantity of the inputs to the system. More the inputs, more informed

will be the system hence more accurate the decisions. Environmental parameters which give the information

about the current state of the environment are used as the inputs. Other important characteristics like signal-to-

noise ratio, bit-error-rate of the system etc. which can be controlled by the system are called transmission

parameters [35] [36].

A. Environmental Parameters

Table 4 Environmental Parameters [37]

Parameter Name Description

Path Loss Amount of signal degradation lost due to the channel

path characteristics

Noise Power Size in decibels of the noise power

Battery Life Estimated energy left in batteries

Power Consumption Power consumption of current configuration

Spectrum Information Spectrum occupancy information

These parameters inform the system about the surrounding environment conditions. For example noise

power of the channel, spectrum information, power consumption, path loss etc. [37]. These parameters directly

impact the objectives of the system as shown in the Table 4.




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B. Transmission Parameters

Table 5 Transmission Parameters [37]

Parameter Name Description

Transmit Power Raw Transmission Power

Modulation Type Type of Modulation format

Modulation Index Number of Symbols for given modulation

scheme

Bandwidth Bandwidth of transmission signal in Hz

Channel Coding rate Specific rate of Coding Scheme

Frame Size Size of transmission frame in bites

Time Division

Duplexing

Percentage of transmit time

Symbol rate Number of symbols per second

These parameters are referred to the list of parameters that are used to control the individual radio

components of the system. Defining complete list of parameters is not possible, as radios are developed

depending upon a particular application and each will possess unique list of parameters [37]. The parameters wehave chosen in our research have been commonly cited in the literature [38-40] shown in Table 5. Cognitive

radio should adapt to the available transmission parameters to achieve a specific performance goal. To do this

various adaptation techniques have been studied. Dynamic power adjustment schemes for wireless systems have

been proposed in [41-42].

Adaptive modulation has also become popular way to adapt a wireless system to a channel to achieve near

optimal throughput [43-46]. Even between transmit power and modulation there exists trade-offs between the

system throughput and system bit error rate [41].Several approaches exists for determining the preference

information of the set of objectives[47].

5. Soft Computing Techniques for QoS Provisioning in Cognitive Radio

Soft Computing was introduced by Zadeh as following [48]:

“Basically Soft Computing is not a homogeneous body of concepts and techniques. Rather it is a

partnership of distinct methods that in one way or another conform to its guiding principle. At this juncture, the

dominant aim of Soft Computing is to exploit the tolerance for imprecision and uncertainty to achieve

tractability, robustness and low solutions cost. The principle constituents of Soft Computing are fuzzy logic,

neurocomputing and probabilistic reasoning, with the latter subsuming genetic algorithms, belief networks,

chaotic systems and parts of learning theory. In the partnership of fuzzy logic, neurocomputing and probabilistic

reasoning, fuzzy logic is mainly concerned with imprecision and approximate reasoning; neurocomputing with

learning and curve-fitting & probabilistic reasoning with uncertainty and belief propagation”.

Figure 2 Role of Soft Computing Techniques in Cognitive Radio [67]

The applications of Soft Computing have proved two main advantages. Firstly it solves nonlinear problems,

in which mathematical models are not available. Secondly, it introduced the human knowledge such as

cognition, recognition, understanding, learning, and others into the fields of computing. This resulted in the

possibility of constructing intelligent systems such as autonomous self-tuning systems, and automated designed




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systems such as cognitive radio shown in Figure 2.

The main goal of Soft Computing is to develop intelligent machines and to solve nonlinear and

mathematically unmodelled system problems [49, 50]. It comprises of fuzzy logic, artificial neural networks,

evolutionary or genetic computing, probabilistic computing , case based reasoning, chaos theory . Each

technique has its own advantages and disadvantages as shown in the Table 6. We need to find out the technique

that suits our need the most. This research investigates two such techniques for “intelligent” control in cognitive

radio. In most general case, the secondary user (cognitive radio) uses information about the environment and

determines the best possible set of parameters to use to set some service performance objectives. These

measurements are the basics of the decisions made by the system, which in turn affects the efficiency (QoS) of

the system. The effective capacity, optimal power and rate adaptation techniques are analyzed in [62], which

studies how to maximize throughput under QoS constraints. The effective channel capacity for Markov wireless

channels are analyzed by Liu et al. in [63]. The normalized effective capacity in low power and wideband

regimes are investigated in [64]. The energy efficiency under certain QoS constraints is analyzed in this work

using variable power/variable rate and fixed power/variable rate transmission scheme.

Table 6 Comparison of Soft Computing Techniques for Spectrum Optimization

Technique Advantages Disadvantages

Game Theory Provides tools to predict the

outcome of complex interactions based

on the results from the environment

Based on prediction of probabilities

and demand precise knowledge of

number of nodes that makes it difficult

and complex to implement in cognitive

radio scenario[51]

Fuzzy Logic Provide suitable provisioning for

spectrum sensing and management as it

is suits the non linear cognitive radio

scenario [48].

It only permits approximate

solutions to be found with uncertain

inputs [51]

Neural Networks After proper training, the neural

network completely bypasses the

repeated use of complex iterative

processes for new cases presented to it.

Also robust in noise and underconditions of external Interference [52]

[53].

These require extensive training to

replicate to the observed behaviors so it

do not offer much reliability[52]

Genetic

Algorithms

Suboptimal Solutions,

Environment cannot be modeled offline

for every scenario. Several solutions

needs to be verified [54] [55].

Fast Convergence Rate, Vast search

space and global convergence. Easy

implementation and reusable makes it

more significant. Works with numerical

data, experimental data and analytical

functions as well. It suits well with

parallel computers [56].

Case Based

Reasoning

Efficient Reasoner, solves

problems by adapting old solutions

without any need to derive answers

from scratch each time [57] [58].

Users might rely on previous

experience without validating it in the

new situation [59].

ProbabilisticReasoning (Bayesian,

Markov Model )

Easy to model and understand [60]. Independence between variables areharder to determine, Calculations can be

intensive in larger domains [60].

Chaos Theory Suitable for highly non linear

systems, It uncovers system information

and relationships without having to

uncover the laws or equations of the

underlying dynamics [61].

The methods chosen to compute the

input parameters depends upon the

dynamics underlying the data and on kind

of analysis intended which in most cases

is highly complex and not always

accurate[61].

Some of the methods for mitigation of spectrum sensing were proposed in [65], [66]. Game theory was used

to find out the strategies for spectrum sharing in [67]. Spectrum allocation problem was considered in [68] but

the mobility of the secondary users was not taken into account. All these traditional approaches for spectrum

sensing assumed that if two secondary users are within distance of each other and use the same frequency band,they fail to access spectrum. Therefore monitoring the secondary users competing for using the spectrum band is

itself a challenging task.




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Overview of some previous work on cognitive radio using soft computing techniques is shown in Table 7.

Table 7 Overview of selected previous work on CR control algorithms [69]

Types of

Algorithm

Evaluation Type Performance

Objective

Adaptable

Parameter

Test Scenarios (in

implementation)

GA [70] hw, sim (i) max. power

(ii)min

BER/power

power,

modulation, FEC,

TDMA, timeslots

ratios

avoid interfering jammer

GA [71] hw, sim scenario

dependent

fitness function

power, frequency,

pulse shape, rate,

modulation

(i)avoid interfering station (ii)

minimize spectral efficiency

(iii) maximize throughput

Neural

Networks [72]

sim length of

scheduling

link activation

schedule

create test schedule for 24

nodes

Rule-Based

[73]

sim reduced

load/latency

AODV routing load distribution in network

Rule-Based

[38]

sim, hw throughput ,

latency

power, frequency,

MTU, FEC, rate

VoIP, datastream in presence of

jammer

Rule-Based

[74]

sim throughput TCP congestion

window

transmission at low SNR

Game Theory

[67]

sim throughput frequency depends on utility function

Fuzzy Logic Approach

It is a famous technique that started during the early development of soft computing [48]. Fuzzy Logic aims

to realize sophisticated control systems that are not easy to define by means of mathematical models. According

to fuzzy theory, when A is a fuzzy set and x is an object, the proposition “ x is a member of A” is not necessarily

true or false, but may be true or false only to some degree, the degree to which x is actually a member of A. The

degrees of membership in fuzzy sets is expressed by numbers on the closed interval [0, 1]. The extreme values in

this interval, 0 and 1, then represent, respectively, the total number or affirmation of the membership in the fuzzy

set.

Each object x can be labeled by a linguistic term, e.g. a word “near”, “distant” or “far” etc. so that x is

defined as the linguistic variable, which is further associated with term set T(x). Each element in T(x) is a fuzzy

set. A fuzzy set F in a universe of discourse U is characterized by a membership function µF which takes values

in the interval [0,1]: µF :U→[0,1]. The most important part of fuzzy logic system is the set of control rules

based on expert knowledge in the form:

IF(certain conditions are satisfied) THEN (certain consequences can be inferred)

Fuzzy logic system comprises of four modules: A fuzzifier, fuzzy inference engine, defuzzification module

and fuzzy rule base.

Figure 3 Fuzzy Logic System

The measured input variables are converted into appropriate fuzzy sets by the fuzzifier and the process is

termed as fuzzification. These fuzzified values are then used by the fuzzy inference engine to evaluate control

rules stored in the fuzzy rule base, which gives result in the form of fuzzy values. These fuzzy values are fed to

the defuzzifier to convert them into crisp values. The output from the defuzzifier represent actions of the control

system. Because it deals extensively with uncertainty in decision making and analysis, it has great potential for

application to cognitive radio. However, only a little work has so far been published in the field, notably byBaldo and Zorzi [74]. Their implementations suggest some interesting applications. A problematic aspect of this




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work is the amount of domain-specific rules required. Fuzzy logic must establish a rule related to the specific

situation in which it is used and recalls some of the limitations of expert systems, though still far more flexible

and powerful.

Fuzzy logic has potential in either problem solving areas or a subset or a part of a cognitive radio. Fuzzy

logic is proposed for cross layer optimization in cognitive radio system [74]. The challenges in cross layer design

include modularity, information interpretability, imprecision and uncertainty, complexity and scalability. The

goal is to optimize the parameters of different layers by taking into account the different needs of the different

services. Since there is no individual solution that can achieve optimal quality for all applications, the CRS

should have capabilities to understand the different service requirements and use artificial intelligence techniques

to adapt the configuration of the systems in the time variant operational environment. Fuzzy logic is proposed to

be used as a generic knowledge presentation and control implementation base for the cross layer optimization in

cognitive radios. This is achieved by describing the parameter values of the systems in the different layers in

linguistic variables. The fuzzy logic cross-layer optimization techniques can achieve performance improvements

and seems to be more modular and reusable than traditional cross layer solutions. However, it requires

significant standardization efforts. In [75] a cooperative spectrum sensing scheme is proposed. The technique

takes into account the reliability of the sensing results at different cognitive radio nodes. The final decision on

the presence of primary users (PU) is done based on the combined results from several cognitive radio nodes

whose decisions are weighted with the credibility. The credibility of the sensing node is determined in a trainingstage with fuzzy evaluation. However, the training process is not described well and the actual use of fuzzy

techniques remains unclear. Fuzzy decision making is used for cognitive network access where cognition refers

to the detection of the user needs [76] and the provision of wireless services most adequate to meet the

requirements. Fuzzy decision making chooses the most appropriate access opportunity by using cross layer

information, past history and shared knowledge among different devices through a knowledge base. Fuzzy

decision making is used to process cross layer communication quality metrics and to estimate the expected

transport layer performance that is compared to QoS requirements of the application the results indicate good

performance and fairness as well as flexibility. Fuzzy logic is found to be useful in taking account multiple

contradictory requirements as the reconfiguration decisions are multi-objective optimizations problems.

From the previous work on applications of fuzzy logic for cognitive radio system, the benefits of fuzzy

logic are lots capabilities to provide good results in multidimensional optimization problems with conflicting

requirements. Moreover together with other intelligent algorithms, fuzzy logic can be used to provide learningcapabilities to improve the performance. However, not much work exists on using fuzzy logic for environment

awareness techniques e.g. spectrum sensing.

Genetic Algorithm Approach

Genetic algorithm is biologically inspired technique which is mainly used for optimization problems

introduced by Holland [54]. It is based on Darwin’s theory of evolution in which the survivor amongst all

evolves as the optimized solution.

GA can be implemented using the following steps [56]:

• Initialization: A random initial population of n chromosomes is generated. This population

contains the available solutions for the specified problem.

• Fitness measures: Evaluation of the fitness of an initial population’s chromosomes.

• Construction of a new population: Try the following steps to reproduce, until the productionof the next generation completes:

- Selection: A selection of chromosomes will be done in a way such that these chromosomes

have the better level of fitness in the current available population.

- Crossover: The crossover is done to make new individuals for the incoming generation. So

with the defined probability of crossover, selected chromosomes reproduce to form new individuals.

- Mutation: The new created individual will be mutated at a definite point.

- Stopping Criteria: The process is repeated with all the above mentioned steps until a desired

optimum solution is obtained or a set of maximum numbers of the population are generated. To

implement the GA there are still several factors to consider, like creation of chromosomes, types of

encoding used to perform the genetic algorithms, selection of the optimum chromosomes, and different

criterion.

The crossover rate or the probability of crossover is the rate at which the crossover occurs betweenparents. Higher crossover rate increases new strings into the population faster and lower crossover rate




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will limit the exploration rate due to the lower number of solutions. Whereas, mutation rate is the

probability that each bit of the string undergoes a random flip after the selection of the new parent. It

affects the speed of searching. Higher rate makes it inefficient and lower rate limits the diversity of the

population making it difficult to find the best solution. Population size controls the amount of

chromosomes that the GA has in each generation. Higher the population size requires longer search to

identify best solutions and lower will limit the diversity. Maximum generations is the number of iterations that a genetic algorithm is allowed to make before a final solution is identified. The important

factor to consider is the time a genetic algorithm takes to converge to a final solution.

Figure 4 The flowchart for a basic genetic algorithm

Some advantages of genetic algorithms are as follows [56]:

• Continuous or discrete variables can be optimized with the GA.

• It doesn’t require derivative information.

• It can deal with a large number of variables.

• It suits well with parallel computers.

• It does not only provide a single solution but a list of optimum solutions.

• It may encode the variables so that the optimization is done with the encoded variables.• It works with numerically generated data, experimental data, or analytical functions.

Christian Rieser introduced the use of genetic algorithms early in cognitive radio research [77 - 78]. The

basic principles, as discussed throughout, are that the large search space involved in optimizing a radio and

optimization algorithms it can handle. Among those algorithms that are suited to the task, evolutionary,

specifically genetic algorithms offer a significant amount of power and flexibility. Cognitive Radios are likely to

face dynamic environments and situations as well as radio upgrades due to advancing technology, so genetic

algorithms are particularly applicable. Newman et al. [37] has also contributed significantly to the use of genetic

algorithm for cognitive radio. Newman’s work has developed a single, linear objective function to combine the

objectives of spectral efficiency minimization, power minimization and throughput maximization. Mahonen et

al. [79] are also doing work using genetic algorithms for cognitive radio. The topic of their research discusses

the use of a cognitive resource manager (CRM) to select an algorithm from a toolbox of algorithms to solve a

particular problem. Hang and Jun [80] proposed multiobjective evolutionary optimization algorithm forcognitive radio networks using genetic algorithm. They proposed models for optimization and analyzed required




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QoS for minimizing competing objectives. One drawback in this approach was that it considered some of the

objectives influence the cognitive radio and remaining objectives still needs to be explored.

Chantaraskul and Moessner [81] contributed to the implementation of genetic algorithm based decision

making framework for opportunistic radio. The results showed the optimal solutions for individual genes that

can accommodate the secondary user, with the specified QoS request but their behavior is not completely linear.

This behavior is due to the trade-off relationship that exists among the multiple genes present in the chromosomestructure.

6. Research Gaps

Some of the research gaps:

• Previous research done on cognitive radio using genetic algorithms validate the

implementation by changing the transmission parameters to different settings based upon the set of

objectives. The various transmission and environmental parameters for various different objectives were not

considered which we have addressed in our research work.

• Soft computing techniques have not been explored fully for spectrum sensing and management

scenarios. We have considered these techniques for optimization of spectrum and for autonomous parameter

adaptation.

• Although various methods have been explored for implementation of GA based cognitiveradio. However, the performance and the QoS of these methods have not been thoroughly analyzed. Also

the fitness functions employed by these algorithms have not been explored in details as all the parameters

have not been considered.

• The existing work done to change the power level of the secondary user implemented by game

theory gives no clear explanation about how the appropriate objective function was chosen to model the

situation. We use fuzzy logic to model the problem.

• Most of the existing methods and techniques for spectrum optimization for cognitive radio

focus on the efficient operation of the primary user. The efficiency and reliability of the QoS for the

secondary users also needs to be taken into account.

• The existing techniques of spectrum optimization have overlooked the integration of the

environmental parameters into the cognitive radio system. We have used these parameters using objective

weights to control the objectives of the system.

• Traditional approaches for objective representation are complex to implement. We use

weighted sum approach which is simple to implement with genetic algorithm and has the ability to control

the performance of the system.

5. Conclusion

In this chapter we discussed the importance of QoS provisioning in cognitive radio. We reviewed the pastwork done on this area by different researchers. The soft computing technique are also reviewed to give an

insight of the research work carried out in further chapters of the thesis. Further brief discussion on the approach

of fuzzy logic and genetic algorithm in the area of QoS provisioning for cognitive radio is done, which we will

use in coming chapters.

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[6] Mitola, J., III; Maguire, G.Q., Jr. “Cognitive radio: making software radios more personal," IEEE Personal

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Documents

Paper-7 QoS Provisioning of Cognitive Radio using Soft Computing Techniques – A Survey