动态时变衰落信道下的频谱感知算法

doi:10.3969/j.issn.1000-436x.2014.07.008

通信学报 ›› 2014, Vol. 35 ›› Issue (7): 63-69.doi: 10.3969/j.issn.1000-436x.2014.07.008

动态时变衰落信道下的频谱感知算法

孙梦巍¹,赵龙¹,许巧春²,李斌¹,赵成林¹

¹ 北京邮电大学信息与通信工程学院，北京 100876
² 国家无线电监测中心检测中心，北京 100037

出版日期:2014-07-25 发布日期:2017-06-24
基金资助:
国家自然科学基金资助项目;国家自然科学基金资助项目;国家重大科技专项基金资助项目;教育部博士点基金资助项目

New spectrum sensing method under time-variant flat fading channels

Meng-wei SUN¹,Long ZHAO¹,Qiao-chun XU²,Bin LI¹,Cheng-lin ZHAO¹

¹ School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
² Inspection Center, The State Radio_Monitoring_Center Testing Center, Beijing 100037, China

Online:2014-07-25 Published:2017-06-24
Supported by:
The National Natural Science Foundation of China;The National Natural Science Foundation of China;Major National Science and Tech-nology Projects;Research Fund for the Doctoral Program of Higher Education of China

摘要/Abstract

摘要：

针对时变慢衰落信道中频谱感知问题，提出一种新颖的动态状态空间系统模型，将授权用户状态与时变衰落信道状态看作2个隐藏系统状态；在此基础上，提出一种联合估计时变信道增益与授权用户状态的新型频谱感知方法。仿真结果表明，提出的新方案能够显著提高动态时变衰落信道中的频谱感知性能。

关键词: 时变慢衰落信道, 频谱感知, 动态状态空间系统模型, 联合估计

Abstract:

A dynamic state-space model (DSM) was presented for the problem of spectrum sensing under time-variant flat fading channels (TVFF).The primary user (PU) state and the fading channel were characterized as hidden states.On this basis,a novel method was proposed to estimate the PU state and the fading channel gain jointly and in real time.The simulation results show that the approach proposed could improve the performance of spectrum sensing apparently in time-variant flat fading channels.

Key words: time-variant flat fading channels, spectrum sensing, dynamic state-space model, joint estimation

孙梦巍,赵龙,许巧春,李斌,赵成林. 动态时变衰落信道下的频谱感知算法[J]. 通信学报, 2014, 35(7): 63-69.

Meng-wei SUN,Long ZHAO,Qiao-chun XU,Bin LI,Cheng-lin ZHAO. New spectrum sensing method under time-variant flat fading channels[J]. Journal on Communications, 2014, 35(7): 63-69.

图/表 4

参考文献 14

[1]	Federal Communication Commission, Spectrum Policy Task Force Report[R]. ET Docket no.02-155, 2002.
[2]	HAYKINS . Cognitive radio: brain-empowered wireless communica-tion IEEE[J]. Journal on Selected areas in Communications, 2005,23(2):201-220.
[3]	AXELLE , LEUSG , LARSSON E G . Spectrum sensing for cognitive radio:state-of-the-art and recent advance[J]. IEEE Signal Processing Magazine, 2012,29(3):101-106.
[4]	DIGHAM FF , ALOUINI MS , SIMON MK . On the energy detection of unknown signals over fading channels[A]. Proc of IEEE International Conference on Communications (ICC)[C]. Anchorage, AK, 2003. 3575-3579.
[5]	CHEN H S , GAO W , DAUT D G . Signature based spectrum sensing algorithm for IEEE 802.22 WRAN[A]. Proc of IEEE International Conference on Communications (ICC)[C]. Glasgow, Scotland, 2007. 6487-6492.
[6]	SUTTON P D , NOLAN KE , DOYLE L E . Cyclostationary signature in practical cognitive radio applications[J]. IEEE Journal on Selected areas in Communications, 2008,26(1):13-24.
[7]	ALTRAD O , MUHAIDAT S . A new mathematical analysis of the probability of detection in cognitive radio over fading channels[EB/OL]. . http://link.springer.com/article/10.1186/1687-1499-2013-159.
[8]	WANG H S , CHANG P . On verifying the first order markovian as-sumption for a rayleigh fading channel model[J]. IEEE Transactions on Vehicular Technology, 1996,45(2):353-357.
[9]	WANG H S , MOAYERI N . Finite-state markov channel: a useful model for radio communication channels[J]. IEEE Transactions on Ve-hicular Technology, 1995,44(1):163-171.
[10]	MA J , LI G Y , JUANG B H . Signal processing in cognitive radio[J]. Signal Processing in Cognitive Radio, 2009,97(5):805-823.
[11]	DJURIC P M , KOTECHA JH , ZHANG J Q . Particle filtering[J]. IEEE Signal Processing Magazine, 2003,20(5):19-38.
[12]	DOUCET A . On sequential simulation-based methods for Bayesian filtering[J]. Statist Comput, 2000,10(3):197-208.
[13]	JOAQUINM , PETER MD . Blind equalization of frequency-selective channels by sequential importance sampling[J]. IEEE Transactions on Signal Processing, 2004,52(10):2738-2748.
[14]	ZHANG W , MALLIK R K , LETAIEF K B . Cooperative spectrum sensing optimization in cognitive radio networks[A]. Proc of IEEE In-ternational Conference on Communications (ICC 2008)[C]. Beijing, China, 2008. 3411-3415.

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动态时变衰落信道下的频谱感知算法

New spectrum sensing method under time-variant flat fading channels

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