吉林大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (5): 1506-1511.doi: 10.7964/jdxbgxb201405044

• • 上一篇    下一篇

基于指数熵的认知无线电频谱感知算法

李一兵, 常国彬, 叶方   

  1. 哈尔滨工程大学 信息与通信工程学院,哈尔滨 150001
  • 收稿日期:2013-04-03 出版日期:2014-09-01 发布日期:2014-09-01
  • 通讯作者: 叶方(1980),女,副教授,硕士生导师.研究方向:认知无线电,LTE系统干扰抑制.E-mail:yefang0815@sina.cn
  • 作者简介:李一兵(1967), 男, 教授, 博士生导师.研究方向:认知无线电.E-mail:liyibing0920@sina.cn
  • 基金资助:
    黑龙江省青年科学基金项目 (QC2012C070).

Exponential entropy-based spectrum sensing algorithm in cognitive radio

LI Yi-bing,CHANG Guo-bin,YE Fang   

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001
  • Received:2013-04-03 Online:2014-09-01 Published:2014-09-01

RICH HTML

0   

PDF (PC)

611

摘要： 针对目前认知无线电中频谱感知技术在低信噪比下检测性能差、对噪声功率不确定性鲁棒性差等问题,提出了一种基于指数熵的频谱感知算法。该方法根据H0、H1下接收信号熵特性的不同,估计接收信号的指数熵,然后通过与预设的门限进行比较,进而判断主用户信号是否存在。该方法的主要优点为:具有对噪声功率不确定性具有鲁棒性、不需要信号的先验知识以及在低信噪比下可以得到较高的检测概率。Monte Carlo仿真结果表明:在噪声功率不确定性为3 dB,采样点数为1024的情况下,当信噪比大于-8 dB时,可以达到100%的检测概率。

关键词: 通信技术, 频谱感知, 指数熵, 鲁棒性

Abstract: Current spectrum sensing methods in cognitive radio possess several shortcomings, such as poor detection performance in low Signal-to-Noise Ratio (SNR) simulation and poor robustness to the uncertainty of noise power, etc. To overcome these shortcomings, an exponential entropy-based spectrum sensing algorithm is proposed. According to the difference of exponential entropy between the situation of H0 and H1, the value of the exponential entropy of the received signal is estimated, and then compared with the threshold to judge whether the primary user's signal exists or not. The algorithm has the advantages of robustness to uncertainty of noise power, no requirement of prior information of the primary user's signal, and high detection probability in low SNR, etc. Monte Carlo simulation results show that under the situation of noise power uncertainty of 3 dB, sampling number of 1024, the detection probability can reach 100% when SNR is higher than -8 dB.

Key words: communication technology, spectrum sensing, exponential entropy, robustness

中图分类号: 

  • TN92
[1] FCC. reporting of the spectrum efficiency working group[R/OL].[2012-11-05]. Spectrum Policy Task Force, 2002. http:www.fcc.gov/sptf/reports/html.
[2] Fu S, Li Y B, Ye F. spectrum sensing technology defending against SNR attack based on geographic location information[J]. Journal of Information and Computational Science, 2012,9(13):3721-3729.
[3] Mchenry M. Report on spectrum occupancy measurements[R]. Shared Spectrum Company Report,Virginia,2005.
[4] Mitola J, Maguire G Q. Cognitive radio: aking software radios more personal[J]. IEEE Personal Communications,1999, 6 (4):13-18.
[5] Haykin S. Cognitive radio: brain-empowered wireless communications[J]. IEEE Sel Areas Commun,2005, 23:201-220.
[6] 李文生, 李一兵. 一种认知无线电频谱检测新算法[J]. 应用科技,2011,38(1):49-52.Li Wen-sheng, Li Yi-bing. A new algorithm for spectrum detection in cognitive radio system[J]. Applied Science and Technology,2011,38(1):49-52.
[7] 赵春晖,腾志军,马爽.基于广义功率谱密度的分布压缩宽带频谱感知[J].吉林大学学报:工学版, 2012,42(4):1015-1020.Zhao Chun-hui, Teng Zhi-jun, Ma Shuang. Distributed compressive wideband spectrum sensing based on generalized power density[J]. Journal of Jilin University(Engineering and Technology Edition),2012,42(4):1015-1020.
[8] Zhang Ya-lin, Zhang Qin-yu,Melodia Tommaso. A frequency-domain entropy-based detector for robust spectrum sensing in cognitive radio networks[J]. Communications Letters, IEEE, 2010, 14:533-535.
[9] Zhang Ya-lin, Zhang Qin-yu,Wu Shao-hua. Entropy-based robust spectrum sensing in cognitive radio[J].Communications,IET,2010,4(4):428-436.
[10] Sabat Samrat L, Srinu S, Raveendranadh A, et al. Spectrum sensing based on entropy estimation using cyclostationary features for Cognitive radio[C]∥Communication Systems and Networks. 2012 Fourth International Conference on, Bangalore,2012:1-6.
[11] 潘喆, 吴一全. 二维指数熵图像阈值选取方法及其快速算法[J].计算机应用,2007,27(4): 982-985.Pan Zhe, Wu Yi-quan. Method of thresholding using two-dimensional exponential entropy and its fast algorithm[J]. Computer Applications, 2007, 27(4): 982-985.
[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] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 姜来为, 沙学军, 吴宣利, 张乃通. LTE-A异构网络中新的用户选择接入和资源分配联合方法[J]. 吉林大学学报(工学版), 2017, 47(6): 1926-1932.
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