基于置信传播分组的多任务压缩频谱感知

doi:10.3969/j.issn.0255-8297.2014.04.002

应用科学学报 ›› 2014, Vol. 32 ›› Issue (4): 341-348.doi: 10.3969/j.issn.0255-8297.2014.04.002

基于置信传播分组的多任务压缩频谱感知

王勇，李红，齐丽娜

南京邮电大学通信与信息工程学院，南京210003

收稿日期:2013-06-15 修回日期:2013-11-12 出版日期:2014-07-31 发布日期:2013-11-12
作者简介:齐丽娜，副教授，研究方向：认知无线电网络中频谱资源相关理论、宽带无线通信技术，E-mail：qiln@njupt.edu.cn
基金资助:
国家“973”重点基础研究发展计划基金(No. 2013CB329005)；江苏省高校自然科学基金(No. 12KJB510014)资助

Multi-task Compressed Spectrum Sensing Based on Belief Propagation Grouping

WANG Yong, LI Hong, QI Li-na

College of Communication and Information Engineering, Nanjing University of Posts
and Telecommunications, Nanjing 210003, China

Received:2013-06-15 Revised:2013-11-12 Online:2014-07-31 Published:2013-11-12

摘要/Abstract

摘要： 针对认知无线电中主用户覆盖范围存在交叠区域的情况，给出先次用户分组再组内协作感知的方法. 首先提出一种基于简化置信传播(belief propagation, BP)的次用户频谱相似性分组算法，挖掘各次用户前一次频谱感知信息并提取相关参数，再根据相关参数利用BP算法对当前频谱感知次用户进行相似性分组. 在组内采用恢复效果较好的多任务压缩频谱感知方法来完成感知任务. 仿真结果表明，所提出的频谱感知方法与现有方法相比，能在混杂频谱环境下保证较好的虚警概率，同时提高频谱感知的正确检测概率，且随着全网感知用户数的增加，频谱感知结果也不断改善.

关键词: 认知无线电, 压缩频谱感知, BP分组, 多任务, 混杂频谱环境

Abstract: As coverage of primary users overlaps in cognitive radio, a method grouping secondary users first and cooperative spectrum sensing within group afterwards is introduced. A grouping algorithm using similarity of secondary users based on simplified belief propagation (BP) is proposed to exploit the previous spectrum sensing information of all secondary users and extract relevant parameters from the exploited information.The current secondary users are then grouped with the BP algorithm according to the extracted relevant parameters. We use a multi-task compressed spectrum sensing method with better recovery ability in current cooperative spectrum sensing methods within each group. Simulation results show that, in a heterogeneous spectrum situation, the proposed method can improve probability of correct detection of the spectrum sensing under better probability of false alarm compared to other methods. The spectrum sensing results are improved with the increasing of the sensing users.

Key words: cognitive radio, compressed spectrum sensing, BP grouping, multi-task, heterogeneous spectrum environment

中图分类号:

TN911.7

WANG Yong, LI Hong, QI Li-na. 基于置信传播分组的多任务压缩频谱感知[J]. 应用科学学报, 2014, 32(4): 341-348.

WANG Yong, LI Hong, QI Li-na. Multi-task Compressed Spectrum Sensing Based on Belief Propagation Grouping[J]. Journal of Applied Sciences, 2014, 32(4): 341-348.

参考文献

[1] MITOLA J，MAGUIRE G Q . Cognitive radios: making software radios more personal [J] . IEEE Personal Communications, 1999, 6(4):13-18.

[2] 包亚萍，居清. 非白高斯噪声中基于导频的匹配滤波频谱检测[J]. 电子技术应用，2011,37 (2): 106-109.

BAO Yaping, JU Qing. Matched filtering spectrum detection based on pilot in non-white Gaussian noise [J] . Application of Electronic Technique, 2011, 37(2):106-109.

[3] SHANKER S, CORDEIRO C, CHALLAPALI K. Spectrum agile radios: utilization and sensing architectures[C] //IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, Maryland, USA, 2005: 160-169.

[4] GHOZZI M, MARX F, DOHLER M, et al. Cyclostationarity-Based Test for Detection of Vacant Frequency Bands[C]// The 1st IEEE International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Mykonos Island, Greece, 2006:1-5

[5] 岳文静，陈志，郑宝玉，等. 基于可靠次用户信息的协作频谱感知算法研究[J].电子与信息学报，2012,34(5):1208-1213.

YUE Wenjing, CHEN Zhi, ZHENG Baoyu Y, et al. Cooperative spectrum sensing based on reliable secondary user information [J] .Journal of Electronics & Information Technology,2012,34(5): 1208-1213.

[6] BARANIUK R G. Compressive sensing [J]. IEEE Signal Processing Magazine, 2007, 24(4): 118-120, 124.

[7] WANG Y, PANDHARIPANDE A, POLO Y, et al. Distributed compressive wide-band spectrum sensing[C]//IEEE International Conference on Information Theory and Applications, San Diego, CA, USA, 2009:178-183

[8] 顾彬，杨震，胡海峰，等. 基于压缩感知信道能量观测的协作频谱感知算法[J]. 电子与信息学报，2012, 34(1): 14-19.

GU Bing, YANG Zhen, HU Haifeng, et al. Cooperative wideband spectrum sensing algorithm based on compressed sensing channel energy measurements[J]. Journal of Electronics & Information Technology, 2012, 34(1):14-19.

[9] 曾凡仔，刘洁，李仁发，等.基于一致优化的分布式宽带合作频谱感知算法[J].通信学报,2011, 32(9): 147-152.

ZENG Fanzi , LIU Jie, LI Renfa, et al. Distributed cooperative spectrum sensing based on consensus optimization [J]. Journal of communication , 2011,32(9):147-152.

[10] BAZERQUE J A , GIANNAKIS G B. Distributed spectrum sensing for cognitive radio networks by exploiting sparsity [J]. IEEE Transactions Signal Processing, 2010, 58(3):1847–1862.

[11] RIBEIRO F C, de CAMPOS, M L R WERNER. Cooperative distributed spectrum sensing with adaptive combining[C]// 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 2012: 3557 - 3560

[12] WU Q H, DING G, WANG J L, YAO Y D. Spatial-temporal opportunity detection for spectrum-heterogeneous cognitive radio networks: two-dimensional sensing[J]. IEEE Transactions. Wireless Communications, 2013, 12(2):516-526

[13] TIPPING M E, FAUL A. Fast marginal likelihood maximisation for sparse Bayesian models[C] //International Workshop on Artificial Intelligence and Statistics , Key West, Florida, 2003: 1-8

[14] JI S H, DUNSON D, CARIN L. Multitask compressive sensing [J]. IEEE Transactions. Signal Processing, 2009, 57(1): 92 - 106.

[15] HUANG X L, WANG G, HU F. Multitask spectrum sensing in cognitive radio networks via spatiotemporal data mining [J]. IEEE Transactions Vehicular Technology, 2013, 62(2): 809-823.

[16] JI S H, XUE Y, CARIN L. Bayesian compressive sensing [J]. IEEE Transactions. Signal Processing, 2008,56(6):2346–2356.

[17] ZHANG Z, HAN Z, LI H , et al. Belief propagation based cooperative compressed spectrum sensing in wideband cognitive radio networks[J]. IEEE Transactions. Wireless Communications, 2011,10(9): 3020-3031.

基于置信传播分组的多任务压缩频谱感知

Multi-task Compressed Spectrum Sensing Based on Belief Propagation Grouping

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