基于广义功率谱密度的分布压缩宽带频谱感知

›› 2012, Vol. 42 ›› Issue (04): 1015-1020.

基于广义功率谱密度的分布压缩宽带频谱感知

赵春晖, 腾志军, 马爽

哈尔滨工程大学信息与通信工程学院, 哈尔滨 150001

收稿日期:2011-04-23 出版日期:2012-07-01 发布日期:2012-07-01
基金资助:
国家自然科学基金项目(61077079);高等学校博士学科点专项科研基金项目(20102304110013);哈尔滨市优秀学科带头人基金项目(2009RFXXG034).

Distributed compressive wideband spectrum sensing based on generalized power spectrum density

ZHAO Chun-hui, TENG Zhi-jun, MA Shuang

College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China

Received:2011-04-23 Online:2012-07-01 Published:2012-07-01

摘要/Abstract

摘要： 为实现在Alpha稳定分布噪声下宽带频谱感知,扩展了传统功率谱密度概念,提出广义功率谱密度的概念,在此基础上提出基于广义功率谱密度的分布压缩宽带频谱感知。此方法能有效地抑制Alpha稳定分布噪声。首先,将接收的宽带信号进行分数低阶处理,计算自相关函数,再结合压缩感知原理将分数低阶自相关向量压缩和融合重构,最后经傅立叶变换得到广义功率谱密度。仿真结果表明,该方法具有良好的宽带功率谱密度估计性能,在Alpha稳定分布噪声下能较好地完成宽带频谱感知。

关键词: 通信技术, 宽带频谱感知, 广义功率谱密度, Alpha稳定分布噪声, 分数低阶自相关, 压缩感知

Abstract: In order to achieve wideband spectrum sensing under Alpha stable noise, generalized power spectrum density is proposed, based on which the distributed compressive wideband spectrum sensing is put forward. This method can effectively restrain the Alpha stable noise. First, the received wideband signal is processed by fractional lower order, then, the autocorrelation vectors are calculated. Combined with the compressed sensing theory, the fractional generalized power spectrum density is derived by Fourier transform. Simulation results show that the method performs well in the estimation of the wideband power spectrum density, and can achieve wideband spectrum sensing under Alpha stable noise.

Key words: communication, wideband spectrum sensing, generalized power spectrum density, Alpha stable noise, fractional lower order autocorrelation, compressive sensing

中图分类号:

TN929.52

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]. , 2012, 42(04): 1015-1020.

ZHAO Chun-hui, TENG Zhi-jun, MA Shuang. Distributed compressive wideband spectrum sensing based on generalized power spectrum density[J]. , 2012, 42(04): 1015-1020.

参考文献

[1] Chen X F, Nagaraj S. Entropy based spectrum sensing in cognitive radio//The 7th Annual Wireless Telecommunications Symposium, Ponoma, CA, United States, 2008:57-61.
[2] Cabric D, Mishra S M, Brodersen R W. Implementation issues in spectrum sensing for cognitive radios//Proc Asilomar Conf on Signals, Systems, and Computers, Pacific Grove, CA, United States, 2004:772-776.
[3] Jarmo L, Visa K, Anu H, et al. Spectrum sensing in cognitive radios based on multiple cyclic frequencies//Proceedings of the 2nd International Conference on Cognitive Radio Oriented Wireless Networks and Communications, CrownCom, Orlando, United States, 2007:37-43.
[4] Renzo M D, Imbriglio L, Graziosi F, et al. Cooperative Spectrum Sensing for Cognitive Radios:Performance Analysis for Realistic System Setups and Channel Conditions[M]//Mobile Lightweight Wireless Systems, Berlin, Springer, 2009:125-134.
[5] Lunden J, Koivunen V, Huttunen A, et al. Collaborative cyclostationary spectrum sensing for cognitive radio systems[J]. IEEE Transactions on Signal Processing, 2009, 57(11):4182-4195.
[6] Hu Z, Guo N, Qiu R. Wideband waveform optimization with energy detector receiver in cognitive radio//IEEE SoutheastCon 2010 Conference:Energizing Our Future, Charlotte-Concord, NC, United States, 2010:198-203.
[7] Tian Z, Giannakis G B. Compressed sensing for wideband cognitive radios//IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, HI, United States, 2007:1357-1360.
[8] Polo Y L, Wang Y, Pandharipande A, et al. Compressive wide-band spectrum sensing//IEEE International Conference on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, 2009:2337-2340.
[9] Ma X Y, Chrysostomos L N. Joint estimation of time delay and frequency delay in impulsive noise using fractional lower order statistics[J]. IEEE Transactions on Signal Processing, 1996, 44 (11):2669-2687.
[10] 吕铁军,郭双兵,肖先赐. 调制信号的分形特征研究[J]. 中国科学:E辑, 2001, 31(6):508-513. Lü Tie-jun, Guo Shuang-bing, Xiao Xian-ci. Research of fractal features of the modulated signal[J]. Science in China(Series E), 2001, 31(6):508-513.

相关文章 15

[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.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed