面向区域的认知无线电系统的频谱感知模型

doi:10.13229/j.cnki.jdxbgxb201604041

吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (4): 1304-1312.doi: 10.13229/j.cnki.jdxbgxb201604041

面向区域的认知无线电系统的频谱感知模型

栾磊^{1, 2}, 赵晓晖², 徐勇军²

1.空军大连通信士官学校无线通信系,辽宁大连 116600;
2.吉林大学通信工程学院,长春 130012

收稿日期:2013-08-26 出版日期:2016-07-20 发布日期:2016-07-20
通讯作者: 赵晓晖(1957-),男,教授,博士生导师.研究方向:信号处理.E-mail:xhzhao@jlu.edu.cn
作者简介:栾磊(1983-),男,讲师,博士研究生.研究方向:认知无线电.E-mail:luanleiv126@163.com
基金资助:
国家自然科学基金项目(61171079)

Region oriented spectrum sensing model for cognitive radio system

LUAN Lei^{1, 2}, ZHAO Xiao-hui², XU Yong-jun²

1.Department of Wireless Communication, Air Force Dalian Communication Sergeant School, Dalian 116600, China;
2.College of Communication Engineering, Jilin University, Changchun 130012, China

Received:2013-08-26 Online:2016-07-20 Published:2016-07-20

摘要/Abstract

摘要： 建立了面向区域的频谱感知模型(RSSM),根据路径损耗和衰落裕度理论推导出了RSSM感知区域的大小。基于RSSM,在单用户频谱感知中分析了阈值设定方法以及次用户位置对频谱感知性能的影响;在协作频谱感知中,利用D-S理论分析了硬决策协作频谱感知中次用户分散性对感知性能的影响。最后通过仿真实验证明了本文模型的正确性。

关键词: 通信技术, 认知无线电, 协作频谱感知, 频谱感知模型, D-S证据理论

Abstract: A Region-oriented Spectrum Sensing Model (RSSM) was proposed. First, the size of the sensing area of RSSM was deduced using path loss and fading margin theory. Then, in single-user spectrum sensing framework, the method of threshold setting and the impact of secondary user's location on the performance of spectrum sensing were analyzed. Furthermore, in hard decision-making cooperative spectrum sensing, the effect of the secondary user's dispersion on the performance of spectrum sensing was studied. Simulation results not only demonstrate the effectiveness and rationality of the proposed model, but also confirm the correctness of location information on the analysis of spectrum sensing performance.

Key words: communication, cognitive radio, cooperative spectrum sensing, spectrum sensing model, D-S evidence theory

中图分类号:

TN929

栾磊, 赵晓晖, 徐勇军. 面向区域的认知无线电系统的频谱感知模型[J]. 吉林大学学报(工学版), 2016, 46(4): 1304-1312.

LUAN Lei, ZHAO Xiao-hui, XU Yong-jun. Region oriented spectrum sensing model for cognitive radio system[J]. 吉林大学学报(工学版), 2016, 46(4): 1304-1312.

参考文献

[1] FCC. Spectrum policy task force report[R].Washington D C: Federal Communications Commission, 2002.
[2] Islam M H, Koh C L, Oh S W. Spectrum survey in Singapore: occupancy measurements and analysis[C]∥The 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications,Singapore, 2008: 1-7.
[3] Mitola III J, Jr Maguire G Q. Cognitive radio: making software radios more personal[J]. IEEE Journal on Personal Communications, 1999, 6(4): 13-18.
[4] Zeng Y, Liang Y C, Hoang A T,et al. A review on spectrum sensing for cognitive radio: challenges and solutions[J]. EURASIP Journal on Advances in Signal Processing, 2010, 2010(2): 1-15.
[5] Yucek T, Arslan H. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Journal on Communications Surveys & Tutorials, 2009, 11(1): 116-130.
[6] Jia P, Vu M, Le-Ngoc T. Capacity-and Bayesian-based cognitive sensing with location side information[J]. IEEE Journal on Selected Areas in Communications, 2011, 29(2): 276-289.
[7] Choi K W, Hossain E, Kim D I. Cooperative spectrum sensing under a random geometric primary user network model[J]. IEEE Transactions on Wireless Communications, 2011, 10(6): 1932-1944.
[8] Unnikrishnan J, Veeravalli V V. Cooperative sensing for primary detection in cognitive radio[J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(1): 18-27.
[9] Xia H H. A simplified analytical model for predicting path loss in urban and suburban environments[J]. IEEE Transactions on Vehicular Technology, 1997, 46(4): 1040-1046.
[10] Digham F F, Alouini M S, Simon M K. On the energy detection of unknown signals over fading channels[C]∥IEEE International Conference on Communications, Alaska, 2003: 3575-3579.
[11] Peh E, Liang Ying-chang. Optimization for cooperative sensing in cognitive radio networks[C]∥IEEE International Conference on Wireless Communications and Networking, Hong Kong, 2007: 27-32.

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面向区域的认知无线电系统的频谱感知模型

Region oriented spectrum sensing model for cognitive radio system

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