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

• • 上一篇    下一篇

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

RICH HTML

0   

PDF (PC)

220

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.
[1] 周彦果,张海林,陈瑞瑞,周韬. 协作网络中采用双层博弈的资源分配方案[J]. 吉林大学学报(工学版), 2018, 48(6): 1879-1886.
[2] 单泽彪,刘小松,史红伟,王春阳,石要武. 动态压缩感知波达方向跟踪算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1938-1944.
[3] 孙晓颖, 扈泽正, 杨锦鹏. 基于分层贝叶斯网络的车辆发动机系统电磁脉冲敏感度评估[J]. 吉林大学学报(工学版), 2018, 48(4): 1254-1264.
[4] 董颖, 崔梦瑶, 吴昊, 王雨后. 基于能量预测的分簇可充电无线传感器网络充电调度[J]. 吉林大学学报(工学版), 2018, 48(4): 1265-1273.
[5] 牟宗磊, 宋萍, 翟亚宇, 陈晓笑. 分布式测试系统同步触发脉冲传输时延的高精度测量方法[J]. 吉林大学学报(工学版), 2018, 48(4): 1274-1281.
[6] 丁宁, 常玉春, 赵健博, 王超, 杨小天. 基于USB 3.0的高速CMOS图像传感器数据采集系统[J]. 吉林大学学报(工学版), 2018, 48(4): 1298-1304.
[7] 陈瑞瑞, 张海林. 三维毫米波通信系统的性能分析[J]. 吉林大学学报(工学版), 2018, 48(2): 605-609.
[8] 张超逸, 李金海, 阎跃鹏. 双门限唐检测改进算法[J]. 吉林大学学报(工学版), 2018, 48(2): 610-617.
[9] 关济实, 石要武, 邱建文, 单泽彪, 史红伟. α稳定分布特征指数估计算法[J]. 吉林大学学报(工学版), 2018, 48(2): 618-624.
[10] 李炜, 李亚洁. 基于离散事件触发通信机制的非均匀传输网络化控制系统故障调节与通信满意协同设计[J]. 吉林大学学报(工学版), 2018, 48(1): 245-258.
[11] 孙晓颖, 王震, 杨锦鹏, 扈泽正, 陈建. 基于贝叶斯网络的电子节气门电磁敏感度评估[J]. 吉林大学学报(工学版), 2018, 48(1): 281-289.
[12] 武伟, 王世刚, 赵岩, 韦健, 钟诚. 蜂窝式立体元图像阵列的生成[J]. 吉林大学学报(工学版), 2018, 48(1): 290-294.
[13] 袁建国, 张锡若, 邱飘玉, 王永, 庞宇, 林金朝. OFDM系统中利用循环前缀的非迭代相位噪声抑制算法[J]. 吉林大学学报(工学版), 2018, 48(1): 295-300.
[14] 王金鹏, 曹帆, 贺晓阳, 邹念育. 基于多址干扰和蜂窝间互扰分布的多载波系统联合接收方法[J]. 吉林大学学报(工学版), 2018, 48(1): 301-305.
[15] 石文孝, 孙浩然, 王少博. 无线Mesh网络信道分配与路由度量联合优化算法[J]. 吉林大学学报(工学版), 2017, 47(6): 1918-1925.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发