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Maximum Likelihood Detection of Random Primary Networks for Cognitive Radio Systems

Sunyoung LEE, Kae Won CHOI, Seong-Lyun KIM

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Sunyoung LEE
Radio Resource Management and Optimization (RAMO) Laboratory, School of Electrical & Electronic Engineering, Yonsei University
Kae Won CHOI
Department of Computer Science & Engineering, Seoul National University of Science and Technology (SeoulTech)
Seong-Lyun KIM
Radio Resource Management and Optimization (RAMO) Laboratory, School of Electrical & Electronic Engineering, Yonsei University

Corresponding author

Keywords:cognitive radio,random geometric network,cooperative spectrum sensing,maximum likelihood detection

JOURNALRESTRICTED ACCESS

2012 Volume E95.BIssue 10Pages 3365-3369

DOIhttps://doi.org/10.1587/transcom.E95.B.3365

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Published: October 01, 2012Manuscript Received: March 21, 2012Released on J-STAGE: October 01, 2012Accepted: -Advance online publication: -Manuscript Revised: June 03, 2012

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Article overview

Abstract

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.

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