Ergodic Capacity Analysis for Underlay Cognitive Radio System

Cognitive radio technology has been proposed as a viable solution to the spectrum scarcity problem faced by world today. The technology allows opportunistic spectrum access to the licensed frequency band by unlicensed user without causing any harmful interference to the licensed primary user. In this paper, ergodic channel capacity is investigated for underlay spectrum sharing system under maximum and received power constraint at licensed primary receiver. The time varying discrete time fading channels are assumed to undergo Rayleigh flat fading environment. Numerical simulations have been done to support theoretical results.

[1]  Pravin Varaiya,et al.  Capacity of fading channels with channel side information , 1997, IEEE Trans. Inf. Theory.

[2]  Michael Gastpar On Capacity Under Received-Signal Constraints , 2004 .

[3]  Joel G. Smith,et al.  The Information Capacity of Amplitude- and Variance-Constrained Scalar Gaussian Channels , 1971, Inf. Control..

[4]  Amir Ghasemi,et al.  Capacity of Fading Channels Under Spectrum-Sharing Constraints , 2006, 2006 IEEE International Conference on Communications.

[5]  Patrick Mitran,et al.  Achievable rates in cognitive radio channels , 2006, IEEE Transactions on Information Theory.

[6]  Mohammad Ali Khojastepour,et al.  The capacity of average and peak power constrained fading channels with channel side information , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

[7]  A. Goldsmith,et al.  Capacity of Rayleigh fading channels under different adaptive transmission and diversity-combining techniques , 1999, IEEE Transactions on Vehicular Technology.

[8]  Neelu Jain,et al.  Resource Allocation Models for Cognitive Radio Networks: A Study , 2014 .

[9]  Sonia Aïssa,et al.  Capacity and power allocation for spectrum-sharing communications in fading channels , 2009, IEEE Transactions on Wireless Communications.

[10]  William D. Horne,et al.  Adaptive Spectrum Access: Using the Full Spectrum Space , 2003 .

[11]  Ghanshyam Singh,et al.  Analytical Modeling of Ad Hoc Cognitive Radio Environment for Optimum Power Control , 2014 .