OPTIMIZATION OF MULTIPLE-CHANNEL COOPERATIVE SPECTRUM SENSING WITH DATA FUSION RULE IN COGNITIVE RADIO NETWORKS

This paper focuses on multi-channel Cooperative Spectrum Sensing （CSS） where Secondary Users （SUs） are assigned to cooperatively sense multiple channels simultaneously. A multi-channel CSS optimization problem of joint spectrum sensing and SU assignment based on data fusion rule is for- mulated, whi...

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Bibliographic Details
Published inJournal of electronics (China) Vol. 29; no. 6; pp. 515 - 522
Main Authors Yu, Huogen, Tang, Wanbin, Li, Shaoqian
Format Journal Article
LanguageEnglish
Published Heidelberg SP Science Press 01.11.2012
National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China
Subjects
Electrical Engineering
Engineering
Greedy algorithm
Branch and Bound (BnB) algorithm
TN92
Cognitive radio
Cooperative Spectrum Sensing (CSS)
Online AccessGet full text
ISSN0217-9822
1993-0615
DOI10.1007/s11767-012-0863-2

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Summary:This paper focuses on multi-channel Cooperative Spectrum Sensing （CSS） where Secondary Users （SUs） are assigned to cooperatively sense multiple channels simultaneously. A multi-channel CSS optimization problem of joint spectrum sensing and SU assignment based on data fusion rule is for- mulated, which maximizes the total throughput of the Cognitive Radio Network （CRN） subject to the constraints of probabilities of detection and false alarm. To address the optimization problem, a Branch and Bound （BnB） algorithm and a greedy algorithm are proposed to obtain the optimal solutions. Simulation results are presented to demonstrate the effectiveness of our proposed algorithms and show that the throughput improvement is achieved through the joint design. It is also shown that the greedy algorithm with a low complexity achieves the comparable performance to the exhaustive algorithm.
Bibliography:This paper focuses on multi-channel Cooperative Spectrum Sensing （CSS） where Secondary Users （SUs） are assigned to cooperatively sense multiple channels simultaneously. A multi-channel CSS optimization problem of joint spectrum sensing and SU assignment based on data fusion rule is for- mulated, which maximizes the total throughput of the Cognitive Radio Network （CRN） subject to the constraints of probabilities of detection and false alarm. To address the optimization problem, a Branch and Bound （BnB） algorithm and a greedy algorithm are proposed to obtain the optimal solutions. Simulation results are presented to demonstrate the effectiveness of our proposed algorithms and show that the throughput improvement is achieved through the joint design. It is also shown that the greedy algorithm with a low complexity achieves the comparable performance to the exhaustive algorithm.
Cooperative Spectrum Sensing （CSS）; Cognitive radio; Branch and Bound （BnB） algo-rithm; Greedy algorithm
11-2003/TN
ISSN:0217-9822
1993-0615
DOI:10.1007/s11767-012-0863-2