Development of an Improved Ergodic Capacity of Underlay Cognitive Radio with Imperfect Channel State Information.
ABSTRACT
This research work presents the development of an improved ergodic capacity of underlay cognitive radio (cr) with imperfect channel state information(csi).
An underlay cr network under a peak interference power (pip) constraint imposed by a primary user (pu) was considered.a closed formergodic capacity expression of the secondary user (su)under pip constraint was derived in order to determine the ergodic capacity under different fading types using nakagami-m distributions. the m-parameter of the nakagami-m fading channel measures the ratio of the line-of-sight (los) signal power to that of the multipath component.
The improved capacity expression developed was validated with the existing ergodic capacity expression at m=1.
The results showed that the ergodic capacity of the su could be extended to different fading types in the cr Path by adjusting the m-parameter. the impact of channel estimation errors, 2 and Channel correlation coefficient, on the ergodic capacity, was studied under different fading types to provide an insight on the capacity behavior of cr network.
The results obtained at different values of m parameters showed the ergodic capacity degraded As a result of increasing 2 and it increases with increasing 1.
it was observed that at M=1/2,1,2 and 3, significant capacity gains of 21.65%, 19.71%, 17.43%, and 15.82% Were achieved when 3% interference outage Was considered for all the m values From 2 1 to 2 0.
However, capacity gains of 23.59%, 22.31%, 21.02%, and 20.26% were also achieved respectively at 1% pout.
It was also found that at m=1/2,1,2 and 3, a spectral efficiency in (bits/s/hz) of 0.5007, 0.2888,0.2660, and 0.2596 were Achieved when 3% pout was considered from 0 to 1 , while a spectral efficiency Of 0.2548, 0.1771, 0.1211, 0.0931 were also achieved at 1% pout for the respective m Values.
TABLE OF CONTENTS
Chapter one: Introduction
Background to the study 1
Problem statement 1
Significance of research 2
Aim and objectives 3
Scope of the research 3
Chapter two: literature review
Introduction 4
Review of fundamental concepts 4
Cognitive radio 4
Cognitive tasks 5
Cognitive radio paradigm 7
Cognitive radio network 8
Wireless channel1
Channel state information 17
Channel capacity 19
Ergodic capacity 20
Review of similar works 26
Chapter three: materials and methods
Introduction 36
Materials 36
Methodology 36
System and channel models 37
Ergodic capacity 40
Ergodic capacity under the pip constraint 41
Chapter four: results and discussion
Introduction 47
Impact of 2 on the su capacity under different m values 47
Impact of 2 on su capacity at m=2 50
Impact of on the su capacity under different m-values 52
Impact of on the su capacity at m=2 54
Validation 56
Chapter five: conclusion and recommendations
Introduction 57
Conclusion 57
Significant contributions 58
Recommendations for further work 59
Reference 60
INTRODUCTION
Background To The Study
Wireless communications offer a wide range of services like mobile internet, data exchange, location tracking, space communications, etc.
However, in recent years there has been a dramatic increase in the demand for radio spectrum. However, the radio spectrum is a limited natural resource.
Access to it is regulated by the government agencies such as the Federal Communications Commission (FCC) in the United States, Nigerian Communication Commission (NCC), and the Nigerian Broadcasting Commission (NBC).
The limited radio spectrum led to the development of cognitive radio (CR) with a view to exploiting the available spectrum efficiently.
This has led to the emergence of a spectrum sharing technique in which unlicensed/ secondary users (SU’s) can share the spectrum of licensed/ primary users (PU’s) without harming primary communications (Sboui, 2013).
Cognitive radio has been studied widely as it provides ways to improve the spectrum efficiency by allowing SU to concurrently access the spectrum band licensed to the PU while causing limited interference to the PU (Haykins, 2005).
REFERENCES
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Deng, Y., Elkashlan, M., Yang, N., Yeoh, P. L., &Mallik, R. K. (2015). Impact of primary network on secondary network with generalized selection combining. IEEE Transactions on Vehicular Technology, 64(7), 3280-3285.
Gao, X., Zhang, J., Liu, G., Xu, D., Zhang, P., Lu, Y., & Dong, W. (2007). Large-scale characteristics of 5.25 GHz based on wideband MIMO channel measurements. IEEE antennas and wireless propagation letters, 6, 263-266.
Garg, V. (2010). Wireless communications & networking. Morgan Kaufmann, 47-84.
Ghasemi, A., & Sousa, E. S. (2007). Fundamental limits of spectrum-sharing in fading environments. IEEE Transactions on Wireless Communications, 6(2), 649-658.
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