Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/101831
Title: Optimal periodic cooperative spectrum sensing based on weight fusion in cognitive radio networks
Authors: Liu, Xin
Jia, Min
Gu, Xuemai
Tan, Xuezhi
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2013
Source: Liu, X., Jia, M., Gu, X., & Tan, X. (2013). Optimal Periodic Cooperative Spectrum Sensing Based on Weight Fusion in Cognitive Radio Networks. Sensors, 13(4), 5251-5272.
Series/Report no.: Sensors
Abstract: The performance of cooperative spectrum sensing in cognitive radio (CR) networks depends on the sensing mode, the sensing time and the number of cooperative users. In order to improve the sensing performance and reduce the interference to the primary user (PU), a periodic cooperative spectrum sensing model based on weight fusion is proposed in this paper. Moreover, the sensing period, the sensing time and the searching time are optimized, respectively. Firstly the sensing period is optimized to improve the spectrum utilization and reduce the interference, then the joint optimization algorithm of the local sensing time and the number of cooperative users, is proposed to obtain the optimal sensing time for improving the throughput of the cognitive radio user (CRU) during each period, and finally the water-filling principle is applied to optimize the searching time in order to make the CRU find an idle channel within the shortest time. The simulation results show that compared with the previous algorithms, the optimal sensing period can improve the spectrum utilization of the CRU and decrease the interference to the PU significantly, the optimal sensing time can make the CRU achieve the largest throughput, and the optimal searching time can make the CRU find an idle channel with the least time.
URI: https://hdl.handle.net/10356/101831
http://hdl.handle.net/10220/18756
ISSN: 1424-8220
DOI: 10.3390/s130405251
Rights: © 2013 The Authors. This paper was published in Sensors and is made available as an electronic reprint (preprint) with permission of The Authors. The paper can be found at the following official DOI: [http://dx.doi.org/10.3390/s130405251].  One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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