METHODOLOGY TO MODEL COGNITIVE RADIO NETWORKS

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INTRODUCTION

The enormous development of wireless communication technology has occurred in the last decade
and changed everyone’s life worldwide. Make the mobile communication as an example: Figure 1.1
shows the number of mobile-cellular telephone subscriptions and mobile-broadband subscriptions in developed and developing countries from 2005 to 2016 [1]. The development of wireless communication not only makes people’s life more convenient but also promote economic prosperity. According to the World Bank database [2], the information communications technology (ICT) sector contributes 6% of total gross domestic product (GDP) all over the world and the contribution to GDP growth is signiﬁcant, as shown in Table 1.1.

Research gap

Form the investigation on the spectrum occupancy, such as in [9], it is evident that the radio resource available to the CR system is dynamical. Hence, the CR system must consider the time varying feature of the radio resource before it allocates the resources to its users. This requires the channel allocation protocol of the CR system to be ﬂexible and adaptable to any possible situations to make full use of the radio resources. Because the CR users are not licensed to use the spectrum, the demand of communication cannot be guaranteed to be fulﬁlled as the licensed user, also as known as PU. This complicates the management of the radio resource of the CR system further. Firstly, a quality of service (QoS) evaluation standard different from the traditional communication system is required to describe the extent to which the CR users’ demand can be fulﬁlled. Then a summary of the evaluations is needed to serve as guide in the radio resource allocation.

HYPOTHESIS, APPROACH AND RESEARCH GOALS

The hypothesis for this research can be stated as follows: “If the channel allocation protocol is modeled in a ﬂexible and adaptable way and the CR system is described comprehensively by such protocol, the practical objective of CR can be described and the optimal resource allocation to achieve certain objectives can be obtained efﬁciently and accurately.” The validation of the hypothesis, which is proved by the statements in the following chapters of this thesis, provides a route to analyze RA problem and achieve better performance in the CRN from the view of the basic components to a larger scope because the proposed analytical model is expandable with standard interfaces of input and output parameters. This is a positive advancement in the ﬁeld of RA in CRN.

Model of resources

The original concept of “resource” is something acting as source or supply, which can yield beneﬁt
if one can take advantage of it. There are two common features of resources in practice: limited availability and exclusivity. From the concept and features, an attempt was made to deﬁne “resource” in the CR system.

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CHAPTER 1 INTRODUCTION
1.1 PROBLEM STATEMENT
1.1.1 Context of the problem
1.1.2 Research gap
1.2 RESEARCH OBJECTIVES
1.3 HYPOTHESIS, APPROACH AND RESEARCH GOALS
1.4 RESEARCH CONTRIBUTIONS
1.5 OVERVIEW OF THESIS
CHAPTER 2 LITERATURE STUDY
2.1 CHAPTER OBJECTIVES
2.2 OVERVIEW OF COGNITVE RADIO TECHNOLOGY
2.2.1 The cognition cycle
2.2.2 Spectrum access technique
2.2.3 Cognitive radio network architecture
2.2.4 Object of study in this thesis
2.3 METHODOLOGY TO MODEL COGNITIVE RADIO NETWORKS
2.3.1 Queueing theory and queueing model
2.3.2 Model of resources
2.3.3 Model of data transmission
2.3.4 Evaluation of the performance
2.4 RESOURCE ALLOCATION PROTOCOLS
2.4.1 Channel independent protocols
2.4.2 Channel dependent protocols
2.4.3 Weakness of the existing protocols
2.5 OPEN PROBLEMS IN RESOURCE ALLOCAITON PROTOCOL DESIGN
2.6 CONCLUSIONS
CHAPTER 3 MECHANISM FOR COGNITIVE RADIO CHANNEL ALLOCATION: DISTRIBUTION PROBABILITY MATRIX
3.1 CHAPTER OVERVIEW
3.2 INTRODUCTION TO THE DISTRIBUTION PROBABILITY MATRIX
3.2.1 Concept and basis of distribution probability matrix
3.2.2 Application of the DPM
3.3 CHANNEL ALLOCATION PROTOCOL BASED ON DPM
3.3.1 System model and assumptions
3.3.2 Resource model
3.3.3 Secondary user system transmission model
3.3.4 Formulation of the queueing model
3.3.5 Performance evaluation under DPM
3.3.6 Numerical results and discussion
3.3.7 Comparison of DPM and other protocols
3.4 CONCLUSION
CHAPTER 4 MAXIMUM THROUGHPUT CHANNEL ALLOCATION PROTOCOL BASED ON DPM
4.1 CHAPTER OVERVIEW
4.2 BACKGROUND
4.3 FORMULATION OF THE QUEUEING MODEL FOR MULTIUSER MULTICHANNEL CRN
4.3.1 Basic model settings
4.3.2 Primary user activity model
4.3.3 Secondary system transmission model
4.3.4 Queueing model setup
4.3.5 Performance evaluations
4.4 MAXIMUM THROUGHPUT PROTOCOL
4.4.1 Basic ideas
4.4.2 Calculation of the potential throughput
4.4.3 Block probability index
4.4.4 Algorithm of the allocation protocol
4.4.5 Complexity analysis of the algorithm
4.5 NUMERICAL RESULTS AND DISCUSSIONS
4.5.1 Performance comparison with other protocols
4.5.2 Performance analysis of multiple SUs
4.5.3 Discussion on the trade-off between the protocols
4.6 CONCLUSION
CHAPTER 5 OPTIMIZATION OF CHANNEL ALLOCATION BASED ON DPM
5.1 CHAPTER OVERVIEW
5.2 BACKGROUND
5.3 OBJECTIVE FUNCTION FORMULATION
5.3.1 System model and assumptions
5.3.2 Primary user activity model
5.3.3 Secondary system transmission model
5.3.4 Performance evaluation and optimization method
5.4 OPTIMIZATION PROBLEM FORMULATION AND SOLUTION
5.5 NUMERICAL RESULTS AND ANALYSIS
5.5.1 Throughput and weighted throughput
5.5.2 Probability of overtime
5.5.3 Optimization and discussion
5.6 CONCLUSION
CHAPTER 6 CONCLUSION
6.1 SUMMARY
6.2 RECOMMENDATIONS FOR FUTURE WORK
6.2.1 Recommendations on efﬁcient algorithm design
6.2.2 Recommendations on consideration of practical factors
6.2.3 Recommendations on the standardization of the CR analytical framework .