期刊文献+

基于多尺度功率谱子带梯度的宽带频谱感知算法与性能分析 被引量:2

Multi scale power spectral density subband gradient-based spectrum sensing algorithm and performance analysis
下载PDF
导出
摘要 为了应对复杂环境下非合作通信、电磁频谱监管等宽带接收中存在的先验信息缺失、信道失真严重以及频域呈现不平坦色噪声的挑战,提出一种基于多尺度功率谱子带梯度的宽带频谱感知算法,该算法不要任何的先验信息,对功率谱进行分段计算梯度,再进行自适应双阈值检测,通过多尺度的技巧提高了宽带频谱感知的稳定性。对该算法在不同信道模型下的统计特性、虚警概率、检测概率以及判决门限的表达式进行了理论推导。理论分析和实验仿真表明,算法适用于高斯噪声信道和平坦衰落信道,能够有效克服色噪声,并且能够实现用户频带范围定位,运算复杂度低、实时性强,对噪声不确定度具有稳健性,能够用于低信噪比场合。 In response to the challenge of the complex environment such as non-cooperative communication and wide- band electromagnetic spectrum regulation, where existing missing priori information, serious channel distortion and un- even color noise in frequency domain, a novel spectrum sensing algorithm based on multi-scale power spectral density subband gradient(MPSG) was proposed. The proposed algorithm, not relying on any prior information, calculated gradi- ents of sections for the power spectrum and detected the narrowband signals by adaptive double threshold, which im- proved the stability of wideband spectrum sensing through multi-scale technique. Theoretically expressions of statistical properties, false alarm probability, detection probability and decision threshold in different channels were deduced. The theoretical analysis and simulation results prove that the novel algorithm overcomes effectively the color noise with low computational complexity and strong real-time in (3aussian noise and fading channel, which can accomplish occupying band range localization of prime users. In addition, the proposed algorithm is robust for noise variance uncertainty, even in the low SNR environment.
出处 《通信学报》 EI CSCD 北大核心 2016年第2期190-198,共9页 Journal on Communications
基金 国家自然科学基金资助项目(No.61072046) 河南省基础与前沿计划基金资助项目(No.132300410049)~~
关键词 宽带频谱感知 功率谱子带 多尺度 色噪声 衰落信道 低信噪比 wideband spectrum sensing, power spectral density subband, multi scale, color noise, fading channel, low SNR
  • 相关文献

参考文献23

  • 1IKER S, PAULO S R. Energy detection technique for adative spec- trum sensing[J]. IEEE Transactions on Communications, 2015, 63(3): 617-627.
  • 2MOHAMED H, NICLAS B. Energy and eigenvalue-based combined fully-blind self-adapted spectrum sensing algorithm[J]. IEEE Transac- tions on Vehicular Technology, 2015,99:1.
  • 3NAIR P R, VINOD A P, SMITHA K G. Fast two-stage spectrum de- tector for cognitive radios in uncertain noise channels[J]. IET Com- munications, 2012, 6(11):1341-1348.
  • 4ABHAY S, CHANDRA R. Group testing-based spectrum hole search for cognitive radios[J]. IEEE Transactions on Vehicular Technology, 2014, 63(8):3794-3805.
  • 5TADILO E B. Wideband sensing and optimization for cognitive radio networks with noise variance uncertainty[J]. IEEE Transactions on Communications, 2014, 63(4): 1091-1105.
  • 6曹开田,杨震.基于随机矩阵理论的DET合作频谱感知算法[J].电子与信息学报,2010,32(1):129-134. 被引量:17
  • 7JOHANNA V, HARRI S. A blind signal localization and SNR estima- tion method[C]//Military Communications Conference. c2006:1-7.
  • 8JANNE J L, J V. Analysis of the LAD Methods[J]. IEEE Letters on Signal Processing, 2008, 15:237-240.
  • 9VARTIAINEN J, LEHTOMAKJ J J, SAARNISAARI H. Double threshold based narrowband signal extraction[C]//IEEE 61 st Vehicular Technology Conference. c2005:1288-1292.
  • 10JOHANNA V, HELI S. Spectrum sensing with LAD based meth- ods[C]//IEEE 18th International Conference on Personal Indoor and Mobile Radio Communications. c2007:1-5.

二级参考文献36

  • 1季虎,孙即祥,毛玲.基于小波变换与形态学运算的ECG自适应滤波算法[J].信号处理,2006,22(3):333-337. 被引量:25
  • 2罗佳,张文明,陶华敏,陈志杰.通信侦察测频接收机的建模与仿真[J].系统仿真学报,2006,18(10):2940-2944. 被引量:8
  • 3尚海燕,水鹏朗,张守宏,张雅斌,朱天桥.基于时频形态学滤波的能量积累检测[J].电子与信息学报,2007,29(6):1416-1420. 被引量:9
  • 4Akyildiz I F, Lee Won-Yeol, and Vuran M C, et al.. Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey [J]. Computer Networks, 2006, 50(13): 2127-2159.
  • 5Zeng Yong-hong, Koh Choo Leng, and Liang Ying-chang. Maximum eigenvalue detection theory and application [C]. IEEE International Conference on Communications, Beijing, May 19-23, 2008: 4160-4164.
  • 6Unnikrishnan J and Veeravalli V V. Cooperative sensing for primary detection in cognitive radio [J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(1): 18-27.
  • 7Zhang Wei, Mallik R K, and Ben Letaief K. Cooperative spectrum sensing optimization in cognitive radio networks [J] IEEE International Conference on Communications, Beijing, May 19-23, 2008: 3411-3415.
  • 8Ma Jun, Zhao Guo-dong, and Li Ye. Soft combination and detection for cooperative spectrum sensing in cognitive radio networks [J]. IEEE Transactions on Wireless Communications, 2008, 7(11): 4502-4507.
  • 9Tulino A M and Verdu S. Random Matrix Theory and Wireless Communications [M]. Hanover, USA: Now Publisher Inc., 2004: 3-73.
  • 10Johnstone I M. On the distribution of the largest eigenvalue in principle components analysis [J]. The Annals of statistics, 2001, 29(2): 295-327.

共引文献47

同被引文献2

引证文献2

二级引证文献5

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部