Development of an Improved Ergodic Capacity of Underlay Cognitive Radio with Imperfect Channel State Information

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

Arunkumar, A., &Kumaran, M. S. (2016). Cooperative relaying spectrum sharing in cognitive radio networks. International Journal of Applied Theoretical Science and Technology, 2(1), 1012-1018.
Biglieri, E., Goldsmith, A. J., Greenstein, L. J., Mandayam, N. B., & Poor, H. V. (2012). Principles of cognitive radio. Cambridge University Press, 41-96.
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.