Mathematical optimization techniques for resource allocation in cognitive radio networks

179 Pages·2017·1.03 MB·English

Loughborough University
Institutional Repository
Mathematical optimization
techniques for resource
allocation in cognitive radio
networks
ThisitemwassubmittedtoLoughboroughUniversity’sInstitutionalRepository
by the/an author.
Additional Information:
• A Doctoral Thesis. Submitted in partial fulfillment of the requirements
for the award of Doctor of Philosophy of Loughborough University.
Metadata Record: https://dspace.lboro.ac.uk/2134/8982
Publisher: (cid:13)c Yogachandran Rahulamathavan
Please cite the published version.
This item was submitted to Loughborough’s Institutional Repository
(https://dspace.lboro.ac.uk/) by the author and is made available under the
following Creative Commons Licence conditions.

For the full text of this licence, please go to:
http://creativecommons.org/licenses/by-nc-nd/2.5/
Mathematical Optimization Techniques for
Resource Allocation in Cognitive Radio
Networks
by
Yogachandran Rahulamathavan
A Doctoral Thesis submitted in partial fulfilment of the requirements
for the award of the degree of Doctor of Philosophy (PhD), at
Loughborough University.
October 2011
Advanced Signal Processing Group,
School of Electronic and Electrical Engineering,
Loughborough University, Loughborough
Leicestershire, UK, LE11 3TU.
(cid:176)c by Yogachandran Rahulamathavan, 2011
CERTIFICATE OF ORIGINALITY
This is to certify that I am responsible for the work submitted in this thesis,
that the original work is my own except as specified in acknowledgements
or in footnotes, and that neither the thesis nor the original work contained
therein has been submitted to this or any other institution for a degree.
................................ (Signed)
................................ (candidate)
I dedicate this thesis to my late parents and brother.
Abstract
Introduction of data intensive multimedia and interactive services together
with exponential growth of wireless applications have created a spectrum
crisis. Many spectrum occupancy measurements, however, have shown that
mostoftheallocatedspectrumareusedinefficientlyindicatingthatradically
new approaches are required for better utilization of spectrum.
This motivates the concept of opportunistic spectrum sharing or the so-
called cognitive radio technology that has great potential to improve spec-
trum utilization. This technology allows the secondary users to access the
spectrum which is allocated to the licensed users in order to transmit their
own signal without harmfully affecting the licensed users’ communications.
In this thesis, an optimal radio resource allocation algorithm is proposed
for an OFDM based underlay cognitive radio networks. The proposed al-
gorithm optimally allocates transmission power and OFDM subchannels to
the users at the basestation in order to satisfy the quality of services and
interference leakage constraints based on integer linear programming. To re-
duce the computational complexity, a novel recursive suboptimal algorithm
is proposed based on a linear optimization framework. To exploit the spatial
diversity, the proposed algorithms are extended to a MIMO-OFDM based
cognitive radio network. Finally, a novel spatial multiplexing technique is
developed to allocate resources in a cognitive radio network which consists
of both the real time and the non-real users. Conditions required for conver-
gence of the proposed algorithm are analytically derived. The performance
of all these new algorithms are verified using MATLAB simulation results.
Contents
1 INTRODUCTION 1
1.1 Evolution of Wireless Communication Systems 1
1.2 Cognitive Radio Networks 5
1.3 Thesis Outline 9
2 RESOURCE ALLOCATION TECHNIQUES FOR WIRE-
LESS COMMUNICATION NETWORKS 11
2.1 Introduction 11
2.2 Multiple Access Methods 12
2.2.1 Overview of OFDM 13
2.2.2 Overview of OFDMA 15
2.3 Adaptive Resource Allocation Techniques for OFDMA Based
Wireless Networks 15
2.3.1 MA Resource Allocation Problem 16
2.3.2 RA Resource Allocation Problem 20
2.4 Resource Allocation Techniques Using Multi-Antenna Tech-
niques 22
2.4.1 Multi-Antenna Techniques 22
2.4.2 Beamforming Techniques 24
2.4.3 Spatial Multiplexing Techniques 28
2.4.4 Resource Allocation Techniques Using Multiple An-
tennas 30
i
ii
Contents
2.5 Resource Allocation Techniques for Cognitive Radio Networks 32
3 OPTIMALRESOURCEALLOCATIONTECHNIQUESFOR
OFDMA BASED COGNITIVE RADIO NETWORKS 35
3.1 Introduction 36
3.2 SISO-OFDMA based Downlink Cognitive Radio Network 37
3.2.1 System Model and Problem Statement 37
3.2.2 Integer Linear Programming Problem Formulation 41
3.2.3 Simulation Results 43
3.3 MIMO-OFDMA based Downlink Cognitive Radio Network 46
3.3.1 System Model and Problem Statement 47
3.3.2 Integer Linear programming 50
3.3.3 Simulation Results 53
3.4 MIMO-OFDMA based Uplink Cognitive Radio Network 55
3.4.1 System Model and User scheduling 56
3.4.2 Power Allocation and Bit Loading 60
3.4.3 Integer Linear Programming Problem Formulation 61
3.5 Simulation Results 62
3.6 Conclusion 66
4 SUBOPTIMALRESOURCEALLOCATIONTECHNIQUES
FOROFDMABASEDCOGNITIVERADIONETWORKS 67
4.1 Introduction 68
4.2 A Rate Adaptive Technique based Suboptimal Resource Al-
location Algorithm 69
4.2.1 A Novel Recursive Based Linear Optimization Frame-
work 70
4.2.2 Computational Complexity Analysis 74
4.2.3 Simulation Results 77
4.3 Rate Balancing Techniques in Cognitive Radio Networks 80
iii
Contents
4.3.1 System Model and Problem Statement 80
4.3.2 Suboptimal Greedy Max-Min Algorithm 84
4.3.3 Computational Complexity Analysis 87
4.3.4 Simulation Results 87
4.4 Conclusion 88
5 RESOURCEALLOCATIONANDSPATIALMULTIPLEX-
INGTECHNIQUESFORCOGNITIVERADIONETWORKS 91
5.1 Introduction 92
5.2 MISO-based Cognitive Radio Networks 93
5.2.1 System Model and Problem Formulation 94
5.2.2 Analytical Framework 97
5.2.3 Computational Complexity Analysis 110
5.3 Simulation Results 114
5.4 MIMO-based Cognitive Radio Network 117
5.4.1 System model and Problem Statement 119
5.4.2 Algorithmic Solution 121
5.5 Simulation Results 129
5.6 Conclusion 131
6 SUMMARY, CONCLUSION AND FUTURE WORK 133
6.1 Summary and Conclusions 133
6.2 Future Work 134
Statement of Originality
The contributions of this thesis are mainly on the development of various
resource allocation algorithms for cognitive radio networks. The novelty of
the contributions is supported by the following international journals and
conference papers.
In Chapter 3, we propose various optimal resource allocation algorithms
forOFDMAbasedunderlaycognitiveradionetworks. Theresultshavebeen
published in:
1. Y. Rahulamathavan, K. Cumanan, L. Musavian and S. Lambotha-
ran, ‘Optimal subcarrier and bit allocation techniques for cognitive
radionetworksusingintegerlinearprogramming’, inProc. IEEESSP,
Cardiff, UK, Aug. 2009.
2. Y. Rahulamathavan, K. Cumanan and S. Lambotharan, ‘Optimal re-
sourceallocationtechniquesforMIMO-OFDMAbasedcognitiveradio
networks using integer linear programming’ in Proc. IEEE SPAWC,
Morocco, Jun. 2010.
3. Y. Rahulamathavan, K. Cumanan, R. Krishna and S. Lambotharan,
‘AdaptivesubcarrierandbitallocationtechniquesforMIMO-OFDMA
based uplink cognitive radio networks’, in Proc. UKIWCWS, Delhi,
India, Dec. 2009.
The contribution of Chapter 4 is on the development of low computa-
tionalcomplexityresourceallocationalgorithmsforOFDMAbasedunderlay
cognitive radio networks. These works have been presented in:
4. Y. Rahulamathavan, S. Lambotharan, Cenk Toker and A. B. Gersh-
man, ‘A Suboptimal Recursive Optimization Framework for Adaptive
ResourceAllocationinSpectrumSharingNetworks’,acceptedforpub-
lication in IET Proceedings on Signal Processing, Oct. 2011.