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Performance Study of a UCRN Over Nakagami-m Fading Channels in the Presence of CCI

Lookup NU author(s): Jamal Ahmed Hussein, Professor Said Boussakta, Dr Salama Ikki

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

In this paper, the impact of co-channel interference (CCI) on the performance of an underlay cognitive radio network (UCRN) over Nakagami-m fading channels is thoroughly presented and analysed. More precisely, a decode-and-forward (DF) relay protocol for a cognitive cooperative network is considered. In this study, the impact of both the primary transmitter interference and CCI on the secondary system performance are considered. First, an exact expression for the equivalent signal-to-interference-plus-noise ratio (SINR) of the secondary system is obtained. Then, the corresponding exact and asymptotic cumulative distribution functions (CDFs) are derived. From this, the exact outage performance for the secondary network is investigated. Furthermore, the equivalent probability density function (PDF) is obtained and discussed. In addition, approximate expressions for the average error probability (AEP) and the system ergodic capacity (EC) performances are derived. From the results, it can be inferred that the presence of the CCI and primary network interference severely degrades the system performance. Moreover, a higher value of the shape parameter of the desired fading channel gives better performance and diversity gain. In addition, despite the impact of interferences, the secondary network performance gives better results in comparison to the Rayleigh fading channels scenario.


Publication metadata

Author(s): Hussein JA, Boussakta S, Ikki SS

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Cognitive Communications and Networking

Year: 2017

Volume: 3

Issue: 4

Pages: 752-765

Print publication date: 01/12/2017

Online publication date: 30/10/2017

Acceptance date: 10/10/2017

Date deposited: 06/11/2017

ISSN (electronic): 2332-7731

Publisher: IEEE

URL: http://doi.org/10.1109/TCCN.2017.2768061

DOI: 10.1109/TCCN.2017.2768061


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