吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (4): 1165-1170.doi: 10.13229/j.cnki.jdxbgxb201404040

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理论的宽带多用户认知系统合作检测

贺岩, 赵晓晖   

  1. 吉林大学 通信工程学院, 长春 130012
  • 收稿日期:2013-02-05 出版日期:2014-07-01 发布日期:2014-07-01
  • 作者简介:贺岩(1980-), 男, 博士研究生.研究方向:信号处理.E-mail:yanhe08@mails.jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61171079)

Compressed sensing based multi-user collaborative detection for wideband cognitive radio networks

HE Yan, ZHAO Xiao-hui   

  1. College of Communication Engineering, Jilin University, Changchun 130012, China
  • Received:2013-02-05 Online:2014-07-01 Published:2014-07-01

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摘要： 利用信道相关估计、压缩采样以及基追踪的信号重构理论, 并应用平均一致算法实现了多用户合作检测。在瑞利衰落信道环境中, 将本文算法与传统能量检测算法进行了仿真比较。仿真结果表明:与传统能量检测算法相比, 本文算法在较低信噪比环境中, 使用较少的采样数量即可获得较好的检测性能;同时, 当认知用户增加时, 检测性能也有所提高。

关键词: 通信技术, 认知无线电, 合作检测, 压缩感知, 多用户

Abstract: Using correlated channel estimation, compressive sampling theory, signal reconstruction based on the basis pursuit technique and the average-consensus algorithm, the proposed algorithm realizes effective and accurate signal detection. The proposed algorithm is compared with the traditional energy detection scheme in the condition of Rayleigh fading. Results show that the proposed algorithm can achieve better performance than the energy detection scheme in low SNR and less sampling rate scenario. Meanwhile, the detection performance can also be improved when the cognitive radio users increase.

Key words: communication, cognitive radio, cooperative detection, compressed sensing, multi-user

中图分类号: 

  • TN925
[1] FCC. ET Docket No 03-222 Notice of Proposed Rule Making and Order[R]. 2003.
[2] Mitola J, Maguire G Q, Jr. Cognitive radio: making software radios more personal[J]. IEEE Pers Commun, 1999, 6(4): 13-18.
[3] Haykin S. Cognitive radio: brain-empowered wireless communications[J]. IEEE Journal on Selected Areas in Commun, 2005, 23(2):201-220.
[4] Candes E, Romberg J, Tao T. Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information[J]. IEEE Trans Inf Theory, 2006, 52(2):489-509.
[5] Candes E. Compressive sampling[C]∥Proc Int Congr Mathematics, 2006:1433-1452.
[6] Donoho D L. Compressed sensing[J]. IEEE Trans Inf Theory, 2006, 52(4):1289-1306.
[7] Tian Z, Giannakis G. Compressed sensing for wide-[7] band cognitive radios[C]∥Proc IEEE Int Conf (ASSP), 2007, Hawaii, USA.
[8] Polo Y L, Wang Y, Pandharipande A, et al. Compressive wide-band spectrum sensing[C]∥Intern Conference on Acoustics, Speech and Signal Processing, 2009: 2337-2340.
[9] Havary-Nassab V, Hassan S, Valaee S. Compressive detection for wide-band spectrum sensing[C]∥Intern Conference on Acoustics, Speech and Signal Processing, 2010: 3094-3097.
[10] Wang Y, Pandharipande A, Polo Y L, et al. Distributed compressive wide-band spectrum sensing[C]∥Proc Inf Theory Applicat Workshop, 2009: 178-183.
[11] Wang Y, Tian Z, Feng C. A two-step compressed spectrum sensing scheme for wideband cognitive radios[C]∥IEEE Global Telecom Conf, Miami, FL, 2010.
[12] Wang Y, Pandharipande A, Leus G. Compressive sampling based MVDR spectrum sensing[C]∥Proc Int Workshop on Cognitive Information Processing, Elba Island, Italy, 2010:333-337.
[13] Kirolos S, Ragheb T, Laska J, et al. Practical issues in implementing analog-to-information converters[C]∥IEEE International Work on System-on-chip for Real-time Applications, 2006:141-146.
[14] Laska J, Kirolos S, Massoud Y, et al. Random sampling for analog-to-information conversion of wideband signals[C]∥IEEE Dallas Circ and Sys Workshop on Design, Applications, Integration and Software, 2006:119-122.
[15] Li Y, Seshadri N, Ariyavisitakul S, et al. Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels[C]∥IEEE Journal on Selected Areas in Communications, 1999:461-471.
[16] Chen S S B, Donoho David L, Saunders Michael A. Atomic decomposition by basis pursuit[J]. SIAM J Sci Comput, 1998, 20(1): 33-61.
[17] Xiao L, Boyd S, Kim S J. Distributed average consensus with least-mean-square deviation[J]. Journal of Parallel and Distributed Computing, 2007, 67(1): 33-46.
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