Optimal Fusion Rule for Cooperative Spectrum Sensing in Cognitive Radio Networks

doi:10.1016/S1005-8885(11)60301-4

中国邮电高校学报(英文) ›› 2012, Vol. 19 ›› Issue (5): 58-65.doi: 10.1016/S1005-8885(11)60301-4

Optimal Fusion Rule for Cooperative Spectrum Sensing in Cognitive Radio Networks

王谦,岳殿武,闫秋娜

大连海事大学

收稿日期:2012-03-27 修回日期:2012-06-05 出版日期:2012-10-31 发布日期:2012-10-08
通讯作者: 岳殿武 E-mail:dwyue@hotmail.com

Optimal Fusion Rule for Cooperative Spectrum Sensing in Cognitive Radio Networks

WANG Qian1, YUE Dian-wu1, YAN Qiu-na1

1. College of Information Science and Technology, Dalian Maritime University, Dalian 116026, China 2. National Mobile Communications Research Laboratory of Southeast University, Nanjing 210096, China

Received:2012-03-27 Revised:2012-06-05 Online:2012-10-31 Published:2012-10-08

摘要/Abstract

摘要：

In this paper, we analyze performance of cooperative spectrum sensing under counting rules when exponential model is utilized to characterize the burst nature of primary user (PU) link. Our objective is to minimize the average error probability (AEP) so that the link utilization in the considered link achieves its maximum. We derive a closed-form expression of AEP as well as the probability of interference (PoI) by classifying cognitive transmission into six events. Then, we consider the minimization of AEP over counting rules under the constraint of interference. As the solution, we develop an efficient algorithm to evaluate the optimal fusion rule. Finally, we verify our analysis in numerical results.

关键词:

cooperative spectrum sensing, cognitive radio network, average error probability, countering rule, probability of interference, spectrum usage

Abstract:

Key words:

Cooperative spectrum sensing, cognitive radio network, average error probability, countering rule, probability of interference, spectrum usage.

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19. http://www.paper.edu.cn/index.php/default/en_releasepaper/content/4423313

Optimal Fusion Rule for Cooperative Spectrum Sensing in Cognitive Radio Networks

Optimal Fusion Rule for Cooperative Spectrum Sensing in Cognitive Radio Networks

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