一种快速低复杂度宽带频谱压缩感知算法
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摘要
为提高双选择性衰落环境下的宽带频谱感知性能,提出一种快速低复杂度线性Bregman算法。利用该算法可实现基于循环谱估计的宽带压缩频谱检测。针对一般线性Bregman算法存在冗余迭代计算的缺陷,通过增加辅助变量,估计线性Bregman算法中余量保持不变过程的迭代次数,更新辅助变量值,跳出冗余迭代的过程,从而加速算法的收敛速度,同时降低算法复杂度。实验结果表明,与一般线性Bregman算法相比,该算法在双选衰落环境下的压缩采样重构效果、检测概率和收敛速度性能均有所提高。
This paper presents a kind of fast and low complexity linear Bregman algorithm and lists the specific implementation steps of the algorithm in order to improve the performance of the wideband spectrum sensing in doubly selective fading environment,which is used for the process of testing wideband compressed spectrum based on cyclic spectrum estimation. This algorithm aims at the flaw of redundant iterative computation in general linear Bregman algorithm. It can accelerate the convergence speed by adding auxiliary variables to estimate iterations of residual staying almost constant in linear Bregman algorithm and updating auxiliary variables to step out the process of redundant iteration. At the same time,it reduces the complexity of the algorithm. As a result,compared with general linear Bregman algorithm,the effect of compressive sampling reconstruction,the detection probability and convergence speed of the modified algorithm are improved in doubly selective fading environment.
This paper presents a kind of fast and low complexity linear Bregman algorithm and lists the specific implementation steps of the algorithm in order to improve the performance of the wideband spectrum sensing in doubly selective fading environment,which is used for the process of testing wideband compressed spectrum based on cyclic spectrum estimation. This algorithm aims at the flaw of redundant iterative computation in general linear Bregman algorithm. It can accelerate the convergence speed by adding auxiliary variables to estimate iterations of residual staying almost constant in linear Bregman algorithm and updating auxiliary variables to step out the process of redundant iteration. At the same time,it reduces the complexity of the algorithm. As a result,compared with general linear Bregman algorithm,the effect of compressive sampling reconstruction,the detection probability and convergence speed of the modified algorithm are improved in doubly selective fading environment.
引文
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[13]Dobre O A,Zhang Qiyun,Rajan S,et al.Second-order Cyclostationarity of Cyclically Prefixed Single Carrier Linear Digital Modulations with Applications to Signal Recognition[C]//Proceedings of GLOBECOM’08.Washington D.C.,USA:IEEE Press,2008:1-5.
[2]漆源,彭涛,钱蓉蓉,等.认知无线电中基于循环平稳特征的频谱感知[J].重庆邮电大学学报,2009,21(3):353-357.
[3]Tian Zhi.Cyclic Feature Based Wideband Spectrum Sensing Using Compressive Sampling[C]//Proceedings of IEEE ICC’11.Washington D.C.,USA:IEEE Press,2011:1-5.
[4]Rebeiz E,Jain V,Cabric D.Cyclostationary-based Low Complexity Wideband Spectrum Sensing Using Compressive Sampling[C]//Proceedings of ICC’12.Washington D.C.,USA:IEEE Press,2012:1619-1623.
[5]Tian Zhi,Sadler B M,Tafesse Y.Cyclic Feature Detection with Sub-nyquist Sampling for Wideband Spectrum Sensing[J].IEEE Journal of Selected Topics in Signal Processing,2012,6(1):58-69.
[6]刘锋,郑鹏,张鑫,等.一种改进的循环谱估计算法[J].电路与系统学报,2013,18(1):397-402.
[7]Tian Jinfeng,Guo Haiyou,Hu Honglin,et al.OFDM Signal Sensing over Doubly-selective Fading Channels[C]//Proceedings of Global Telecommunications Conference.Washington D.C.,USA:IEEE Press,2010:1-5.
[8]Yin W,Osher S,Goldfarb D,et al.Bregman Iterative Algorithms for l1-minimization with Applications to Compressed Sensing[J].Society for Industrial and Applied Mathematics,2008,1(1):143-168.
[9]张慧,成礼智,李建平.重复加权极小化MR图像模型的分裂Bregman方法重构[J].中国科学,2011,41(4):440-449.
[10]Osher S,Mao Yu,Dong Bin,et al.Fast Linearized Bregman Iteration for Compressive Sensing and Sparse Denoising[J].Communications in Mathematical Sciences,2010,8(1):93-111.
[11]Dandawatk A V,Giannakis G B.Statistical Tests for Presence of Cyclostationarity[J].IEEE Transactions on Signal Processing,1994,42(9):2355-2369.
[12]刘留.高速移动条件下宽带无线接入关键技术的研究[D].北京:北京交通大学,2010.
[13]Dobre O A,Zhang Qiyun,Rajan S,et al.Second-order Cyclostationarity of Cyclically Prefixed Single Carrier Linear Digital Modulations with Applications to Signal Recognition[C]//Proceedings of GLOBECOM’08.Washington D.C.,USA:IEEE Press,2008:1-5.