Dynamic Spectrum Allocation for Cognitive Radio Networks

158 Pages·2015·2.84 MB·English

by  A Sabbah

Dynamic Spectrum Allocation for
Cognitive Radio Networks:
A Comprehensive Optimization Approach
by
Ayman Sabbah
A thesis submitted to the
Department of Electrical and Computer Engineering
in conformity with the requirements for
the degree of Doctor of Philosophy
Queen’s University
Kingston, Ontario, Canada
October 2015
Copyright (cid:13)c Ayman Sabbah
Abstract
In Cognitive Radio Networks (CRNs), the role of the Medium Access Control (MAC)
layer is very important since it enables Secondary Users (SUs) to access the spectrum
without affecting Primary Users’ (PUs) communications. SUs’ and PUs’ geometry
has an effect on the performance of the spectrum sharing algorithms. Also, SUs’
mobility changes the topology of the network as well as interference between the PUs
and SUs. The scenario of multiuser multichannel CRNs introduces new challenges
such as co-channel interference. Consequently, the power budget should be allocated
to the SUs subject to specific constraints. Hence, different SUs will have different
power and interference limits depending on the activity of PUs and on which SUs
will be causing co-channel interference to each other. In addition, enabling Energy
Harvesting (EH) in CRNs is promising to extend their lifetime so that the hybrid
interweave/underlay access scheme is adopted, which means that SUs can access the
active and non-active PU bands.
In this thesis, I propose new optimal and suboptimal Dynamic Spectrum Allo-
cation (DSA) algorithms that employ an interweave/underlay access scheme. I also
study the impact of the following factors: mobility of the SUs, spectrum mobility,
the Primary Exclusive Regions (PERs), the geographical locations of the nodes, con-
nectivity of SUs, correlated shadow fading, and the activity of both PUs and SUs. A
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cross-layer approach is adopted in order to benefit from the information of the other
layers.
Moreover, to increase both the energy efficiency and the spectrum efficiency, I also
propose a novel algorithm that enables SUs to harvest energy with minimal impact
on their spectrum access performance. The algorithm allows SUs to participate in
making decisions regarding their operating mode. Also, the algorithm ensures that
the energy level in CRN cannot be lower than a specific threshold.
Furthermore, I propose different optimal and suboptimal algorithms that optimize
the power allocation among SUs. The objective is to maximize the Spectral Efficiency
(SE) while respecting the power budget along with the other constraints.
Extensive simulations have been conducted and the results are presented for all
of the proposed algorithms.
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”Intellectual growth should commence at birth and cease only at death,”
Albert Einstein.
”Curiosity - the rover and the concept - is what science is all about: the quest to
reveal the unknown,”
Ahmed Zewail.
”Nature is the source of all true knowledge. She has her own logic, her own laws,
she has no effect without cause nor invention without necessity,”
Leonardo da Vinci.
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To my parents, with love & respect.
To my dear daughter, ”Harhoora Alsagheera”: Noora, with tenderness.
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Acknowledgments
I am deeply grateful to my thesis supervisor Prof. Mohamed Ibnkahla for his con-
tinuous guidance and support during the period of this work. This thesis would not
have been possible without his support and motivation. I am sincerely grateful for
his advice and suggestions.
I also would like to thank the members of my thesis committee, Prof. Abuelmagd
Noureldin, Prof. Hossam Hassanein, Prof. Praveen Jain, Prof. Sonia A¨ıssa, and
Prof. Andrew Pollard for their time and valuable comments. Many thanks to the
Communications Research Centre (CRC), Industry Canada, for their interest in my
research and for the collaboration opportunity.
My most heartfelt indebtedness goes to my beloved family for the endless support
they provided me with during the period of my study and my whole life. My deepest
gratitude is for my daughter Noora, thanks for giving me strength and hope when
life storms visited us. A big thank you goes to Mrs. Abida Khan and her family, who
stood beside me and Noora when we needed it the most. I can’t but send a special
thanks to Prof. Nihad Dib and his family. There are no words that can express my
gratitude to you, my lovely family.
I also want to thank many people at Queen’s. Special thanks goes to Debie
Fraser, Ita McConnel, Aphra Rogers, Prof. Kim McAuley, and many others. Also,
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many friends at Queen’s University and all over the world contributed to making the
years of PhD journey enjoyable and I would like to thank all my true friends for their
kindness and for the nice moments we spent together.
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Contents
Abstract i
Acknowledgments v
Contents vii
List of Figures xi
List of Acronyms xv
Symbols and Notations xix
Chapter1: Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1 Conventional MAC protocols Vs. CR-MAC Protocols . . . . . 3
1.1.2 Classification of MAC protocols for CRNs . . . . . . . . . . . 5
1.1.3 Functionalities to Enable CR Technology . . . . . . . . . . . . 7
1.1.4 Spectrum Mobility Management . . . . . . . . . . . . . . . . . 7
1.1.5 CR-MAC Requirements . . . . . . . . . . . . . . . . . . . . . 9
1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.3 Research Objectives and Contributions . . . . . . . . . . . . . . . . . 12
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1.3.1 Spectrum Allocation Algorithms with low computational-cost 13
1.3.2 Multiuser Hybrid Interweave/Underlay Resource Allocation . 13
1.3.3 Supporting Mobility of SUs . . . . . . . . . . . . . . . . . . . 14
1.3.4 Enabling Energy Harvesting in the Context of Dynamic Spec-
trum Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.3.5 Adaptive Power Allocation Algorithms . . . . . . . . . . . . . 15
1.4 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 15
Chapter2: Literature Review 16
2.1 Standardization of CR Technology . . . . . . . . . . . . . . . . . . . 16
2.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.1 Spectrum and Power Allocation . . . . . . . . . . . . . . . . . 17
2.2.2 Energy Harvesting . . . . . . . . . . . . . . . . . . . . . . . . 22
Chapter3: Efficient Spectrum Allocation Schemes for Cognitive
Radio Networks 25
3.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Traffic Flow Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3 Correlated Shadow Fading Map . . . . . . . . . . . . . . . . . . . . . 29
3.4 Protecting PUs’ Communications . . . . . . . . . . . . . . . . . . . . 34
3.5 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.6 The Proposed PAH-DSA Algorithm . . . . . . . . . . . . . . . . . . . 38
3.7 Results and Interpretation . . . . . . . . . . . . . . . . . . . . . . . . 41
3.8 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
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Chapter4: Mobility-Supported Dynamic Spectrum Allocation for
Cognitive Radio Networks 48
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2 Mobility Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.1 Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.2 Formulating DSA as Optimization Problem . . . . . . . . . . 53
4.4 Description of MSDSA Algorithm . . . . . . . . . . . . . . . . . . . . 57
4.5 Results and Interpretation . . . . . . . . . . . . . . . . . . . . . . . . 61
4.6 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
Chapter5: Integrating Energy Harvesting and Dynamic Spectrum
Allocation in Cognitive Radio Networks 70
5.1 Range of Harvesting . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.2 Energy Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.3 Framework of Enabling EH in CRNs . . . . . . . . . . . . . . . . . . 76
5.4 Results and Interpretations . . . . . . . . . . . . . . . . . . . . . . . 81
5.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
Chapter6: Power Allocation for Cognitive Radio Networks 88
6.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
6.2 The proposed Power Allocation Algorithms . . . . . . . . . . . . . . . 91
6.2.1 Optimal Power Allocation . . . . . . . . . . . . . . . . . . . . 91
6.2.2 Cap-Limited Heuristic (CLH) Algorithm . . . . . . . . . . . . 94
6.3 Results and Interpretation . . . . . . . . . . . . . . . . . . . . . . . . 96
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