A SURVEY OF RESOURCE ALLOCATION IN COGNITIVE RADIO NETWORKS

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INTRODUCTION

Wireless communication has over the years gained global acceptance, becoming the most integral part of modern telecommunication [1]. Its tremendous achievements in terms of ubiquity, mobility, massive ‘unlimited’ coverage capability, sustained reduction in component size (portability) and improved service costs (affordability), and a host of other positives have continued to make it a preferred choice over wired communication. As a result, demands for wireless applications and usage have been on an explosive exponential rise. It is estimated that by year 2019, mobile communication through phones alone would have reached a staggering 5.07 billion of the world population! [2]. The statistics about wireless and mobile network penetration in Africa have been equally impressive. The expectations are that, in the nearest future, the likelihood of a higher growth in capacities will result in an even wider coverage or reach. This will invariably imply an increase in wireless and mobile broadband demands in many countries of Africa and other parts of the world [3]. In other words, the recent but steady proliferation of ‘wireless’, if sustained, is set to result in some immense, almost insatiable ‘outbreak’ in wireless communication operations worldwide.

PROBLEM STATEMENT

Although significant advancements have been made in exploring and even experimentally deploying some prototypes of CRN, there are still a number of open-ended problems that require adequate investigation, if the promises of CRN are ever to materialise. One such problem, of high significance, is in designing methods for achieving the utmost in the allocation of the limited resources on which CRN usually have to build communication. It has already been well established that the amount of resources available for use in CRN is generally limited and that, the demands of users in CRN are usually large and diverse. Hence, unless adequate methods for efficiently utilising the resources of CRN are devised and the limiting problems addressed, it would be very difficult for CRN to achieve meaningful results.
The important research question that is sought to be addressed in this thesis is underscored thus: ‘how can the limited or scarce resources available to CRN be best administered so as to meet the varying demands of different users in order to achieve maximum utility and productivity of the overall network?’ The problem statement is therefore ‘to identify and proffer solutions to problems associated with the allocation of the limited resources of CRN in order to meet the diverse needs of the various users in the network, so as to optimise the overall CRN productivity’. The term, ‘resource allocation (RA) in heterogeneous CRN’, which essentially defines and describes this problem, is thus the focus of the thesis.

OVERVIEW OF THESIS

The remainder of the thesis is structured as follows: Chapter two focuses on presenting detailed background knowledge on the subject matter through a well-thought-out survey on RA in CRN. In the chapter, relevant aspects of, and recent works on, RA in CRN in the literature, are critically examined. The survey establishes the aspects of RA in CRN that have been properly addressed, but also identifies limiting factors to the optimal resourcefulness of the various RA solution models. The analysis of solutions to the identified limitations in RA optimisation for CRN forms the important direction for the research. It also helped shape the structure of the thesis, as the various studies conducted are logically presented in subsequent chapters of the thesis.

RESOURCE ALLOCATION IN COGNITIVE RADIO NETWORKS

Resources used up in wireless communication systems such as power, bandwidth and spectrum have always formed the backbone on which the operations of such systems depend. These resources being generally non-ubiquitous, the various wireless communication models, as developed, have had to factor into their design the mechanisms by which their scarce resources are to be allocated or administered in order to achieve the utmost in their operations. The concept of RA, which seeks to address that need, has therefore been an important aspect of all wireless communication designs. In fact, in several conventional wireless communication systems such as the OFDMA-based wireless networks, RA has been a rather active research topic. For example, a few of the works that have addressed RA problems in OFDMA communication systems can be found in references [35–41]. In general, RA problems in wireless communication essentially define how to optimise the limited resources in the communication network.

RESOURCE ALLOCATION PROBLEM FORMULATION IN COGNITIVE RADIO

NETWORKS There is already a sizeable amount of research work on solving RA problems in CRN. The various investigations have shown that, in almost all cases, RA problems in CRN are fully demonstrated to be optimisation problems. The knowledge of optimisation is therefore crucial to the understanding of, and in developing solution models to RA problems in CRN. In essence, optimisation can be explored and employed as a vital tool for solving RA problems in CRN. Optimisation, in itself, is a well-developed analytical tool for solving a host of scientific-related problems and is therefore used broadly in different fields of science such as mathematics, operations research, business and financial management, economics, engineering etc. In optimisation, there is usually an objective (there could be more than one objective too) to be achieved, either that of maximising or minimising an entity or a number of entities, and this is always captured in the objective function.
Then, there are certain limiting constraints that must be taken into consideration while seeking to achieve the objective. In solving, the constraints cannot be violated, otherwise the solutions to such problems, if ever obtained, become void. The final components of all optimisation problems are the decision variables. These variables are the parameters to be obtained while solving, in order to arrive at (optimal or suboptimal) solutions. Due to space limitations and also to help keep focus, the preliminaries on optimisation are not discussed in this thesis. The following materials are recommended in providing some fundamental knowledge on optimisation, should a reader require such: references [51–54].

Solution by separation or decomposition

Certain RA problems can be split into two (or more) simpler problems without significantly affecting the overall import of such problems. In other words, by a careful study of the problem structure, an original RA problem can be separated or decomposed into two or more simpler sub-problems and each solved individually, usually with a lot less difficulty. The solutions are later combined to give the exact (or close to exact) final response to the initial problem. There are several methods of decomposition that have been used in solving RA problems in CRN. One such decomposition method is the Dantzig-Wolfe decomposition [83]. Examples of RA problems in the CRN that have employed decomposition in arriving at solutions can be found in references [37, 57]. In [57], the authors obtained optimal solution to their RA problem by using a primal-dual decomposition method whereby, the overall problem is decomposed into individual power allocation sub-problems and solved for every decision variable pair.
Authors in [37] divided their RA problem (joint spectrum and power allocation for multiband CRN) into two stages and used an iterative dual decomposition method to solve it. In [84], the authors developed a CRN duality technique that decomposed their utility maximisation problem into three sub-problems – optimising signal-to-interference-and-noise ratio (SINR) assignment, optimising power and optimising interference temperature. Similarly, the work in [82] used a decomposition approach to jointly address the problem of spectrum sensing, channel assignment and power allocation in cellular CRN. The initial problem, which was a mixed integer non-linear programming (MINLP) problem, was decomposed into two sub-problems – optimal spectrum sensing and optimal channel assignment and power allocation. This was achieved without sacrificing optimality of the entire network. The advantage of this solution technique is the possibility of realising optimal solutions with reduced computational complexity. The major bottlenecks are that not all problems are decomposable, and some problems loose a significant part of their imports when attempted to be decomposed into smaller sub-problems.

TABLE OF CONTENTS :

• 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 AND APPROACH
  • 1.4 RESEARCH CONTRIBUTIONS AND OUTPUTS
  • 1.5 DEFINITION OF TERMS
  • 1.6 OVERVIEW OF THESIS
• CHAPTER 2 A SURVEY OF RESOURCE ALLOCATION IN COGNITIVE RADIO NETWORKS
  • 2.1 CHAPTER OBJECTIVES
  • 2.2 AN OVERVIEW OF COGNITIVE RADIO NETWORKS
  • 2.3 ARCHITECTURE OF COGNITIVE RADIO NETWORKS
  • 2.4 RESOURCE ALLOCATION IN COGNITIVE RADIO NETWORKS
  • 2.5 RESOURCE ALLOCATION PROBLEM FORMULATION IN COGNITIVE RADIO NETWORKS
  • 2.6 CLASSIFICATION OF RESOURCE ALLOCATION SOLUTION APPROACHES FOR COGNITIVE RADIO NETWORKS
  • 2.6.1 Solutions using classical optimisation
  • 2.6.2 Solutions by studying problem structure
  • 2.6.3 Solutions by heuristics or meta-heuristics
  • 2.6.4 Solutions by multi-objective optimisation
  • 2.6.5 Solutions through soft computing
  • 2.7 OBSERVATION AND OPEN-ENDED PROBLEMS IN RESOURCE ALLOCATION FOR COGNITIVE RADIO NETWORKS
  • 2.8 CONCLUSION
• CHAPTER 3 RESOURCE ALLOCATION SOLUTION MODELS FOR HETEROGENEOUS COGNITIVE RADIO NETWORKS
  • 3.1 CHAPTER OVERVIEW
  • 3.2 BACKGROUND
  • 3.3 HETEROGENEITY IN COGNITIVE RADIO NETWORKS
  • 3.4 RELATED LITERATURE ON HETEROGENEITY IN COGNITIVE RADIO NETWORKS
  • 3.5 SYSTEM MODEL
  • 3.5.1 General representation of the resource allocation formulation for heterogeneous cognitive radio networks
  • 3.5.2 Classification based on minimum rate requirement
  • 3.5.3 Classification based on user priority or sensitivity
  • 3.5.4 Classification based on delay tolerance
  • 3.6 RESULTS AND DISCUSSION
  • 3.6.1 Results based on minimum data rate classification
  • 3.6.2 Results based on priority and sensitivity classifications
  • 3.6.3 Results based on delay tolerance classification
  • 3.6.4 Effects of weight on resource allocation in heterogeneous cognitive radio networks
  • 3.7 CONCLUSION
• CHAPTER 4 RESOURCE ALLOCATION IN HETEROGENEOUS COOPERATIVE COGNITIVE RADIO NETWORKS
  • 4.1 CHAPTER OVERVIEW
  • 4.2 BACKGROUND
  • 4.3 RELATED LITERATURE ON COOPERATIVE DIVERSITY IN COGNITIVE RADIO NETWORKS
  • 4.4 SYSTEM MODEL
  • 4.5 RESOURCE ALLOCATION PROBLEM FORMULATION
  • 4.6 REFORMULATION AS AN INTEGER LINEAR PROGRAMMING PROBLEM
  • 4.7 ITERATIVE-BASED HEURISTIC
  • 4.7.1 Subchannel allocation
  • 4.7.2 Iterative bit and power allocation
  • 4.8 RESULTS AND DISCUSSION
  • 4.9 CONCLUSION
• CHAPTER 5 RESOURCE ALLOCATION SOLUTION FOR HETEROGENEOUS BUFFERED COGNITIVE RADIO NETWORKS
  • 5.1 CHAPTER OVERVIEW
  • 5.2 BACKGROUND
  • 5.3 RELATED LITERATURE ON BUFFERING IN RESOURCE ALLOCATION FOR COGNITIVE RADIO NETWORKS
  • 5.4 SYSTEM MODEL
  • 5.4.1 Queueing model
  • 5.4.2 Analysis of model
  • 5.5 RESULTS AND DISCUSSION
  • 5.6 CONCLUSION
• CHAPTER 6 CONCLUSION
  • 6.1 SUMMARY
  • 6.2 RECOMMENDATIONS FOR FUTURE WORK
  • 6.2.1 Recommendations based on heterogeneous considerations
  • 6.2.2 Recommendations based on the use of cooperative diversity for mitigating interference to PUs
  • 6.2.3 Recommendations based on the use of queueing models for addressing delay problems
  • REFERENCES
  • ADDENDUM A STATE REDUCTION ALGORITHM
  • A.1 THE GRASSMAN-TAKSAR-HEYMAN (GTH) ALGORITHM

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RESOURCE ALLOCATION OPTIMISATION IN HETEROGENEOUS COGNITIVE RADIO NETWORKS