IEICE Trans - Maximum Likelihood Detection of Random Primary Networks for Cognitive Radio Systems

Maximum Likelihood Detection of Random Primary Networks for Cognitive Radio Systems

Sunyoung LEE  Kae Won CHOI  Seong-Lyun KIM

Publication
IEICE TRANSACTIONS on Communications   Vol.E95-B   No.10   pp.3365-3369
Publication Date: 2012/10/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E95.B.3365
Print ISSN: 0916-8516
Type of Manuscript: LETTER
Category: Terrestrial Wireless Communication/Broadcasting Technologies
Keyword:
cognitive radio,  random geometric network,  cooperative spectrum sensing,  maximum likelihood detection,

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Summary:
In this letter, we focus on detecting a random primary user (PU) network for cognitive radio systems in a cooperative manner by using maximum likelihood (ML) detection. Different from traditional PU network models, the random PU network model in this letter considers the randomness in the PU network topology, and so is better suited for describing the infrastructure-less PU network such as an ad hoc network. Since the joint pdf required for the ML detection is hard to obtain in a closed form, we derive approximate ones from the Gaussian approximation. The performance of the proposed algorithm is comparable to the optimal one.