NDLI: Performance Analysis of Relay Subset Selection for Amplify-and-Forward Cognitive Relay Networks

Content Provider	PubMed Central
Author	Sultan, Kiran Qureshi, Ijaz Mansoor Malik, Aqdas Naveed Zubair, Muhammad
Copyright Year	2014
Abstract	Cooperative communication is regarded as a key technology in wireless networks, including cognitive radio networks (CRNs), which increases the diversity order of the signal to combat the unfavorable effects of the fading channels, by allowing distributed terminals to collaborate through sophisticated signal processing. Underlay CRNs have strict interference constraints towards the secondary users (SUs) active in the frequency band of the primary users (PUs), which limits their transmit power and their coverage area. Relay selection offers a potential solution to the challenges faced by underlay networks, by selecting either single best relay or a subset of potential relay set under different design requirements and assumptions. The best relay selection schemes proposed in the literature for amplify-and-forward (AF) based underlay cognitive relay networks have been very well studied in terms of outage probability (OP) and bit error rate (BER), which is deficient in multiple relay selection schemes. The novelty of this work is to study the outage behavior of multiple relay selection in the underlay CRN and derive the closed-form expressions for the OP and BER through cumulative distribution function (CDF) of the SNR received at the destination. The effectiveness of relay subset selection is shown through simulation results.
Related Links	http://dx.doi.org/10.1155/2014/548082
Starting Page	548082
File Format	PDF
ISSN	23566140
e-ISSN	1537744X
Journal	The Scientific World Journal
Volume Number	2014
Language	English
Publisher	Hindawi Publishing Corporation
Publisher Date	2014-01-01
Access Restriction	Open
Rights Holder	Hindawi Publishing Corporation
Subject Keyword	Biochemistry, Genetics and Molecular Biology(all) Environmental Science(all) Medicine(all) Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Medicine Biochemistry, Genetics and Molecular Biology Environmental Science

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