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基于压缩感知的多径信道估计研究
作者:齐婷,温柯,王有政
来源:本站原创
更新时间:2014-1-16 9:56:00
正文:

                       (信息科学与技术国家实验室,清华大学 北京100084)

摘要:本文研究基于压缩感知的多径信道估计方法。多径信道往往呈现明显的稀疏特性,适合应用压缩感知技术进行处理。本文分析比较了传统的线性估计算法-最小二乘法与多种压缩感知算法(Lasso、CoSaMP和IHT)在多径信道估计上的性能差异,并提出一种基于迭代型伪逆的高效信道估计方法,该方法与经典的压缩感知算法相比,在恢复精度、时间复杂度和恢复成功率方面有明显的性能提升。
关键词:信道估计;压缩感知
中图分类号:□□□□□        文献标识码:A        文章编号:

                       Study on Channel Estimation based on Compressive Sensing
                                       QI Ting, WEN Ke, WANG You-zheng
  (Tsinghua National Laboratory for Information Science and Technology (TNList), Tsinghua University,
Beijing 100084, P. R. China. Correspondence Address: WANG You-zheng: yzhwang@tsinghua.edu.cn)
Abstract:In this paper, the channel estimation of multi-path transmission, which usually shows sparse features, is studied by compressive sensing. It is compared between traditional linear algorithm, such as Least Square (LS), and several compressive sensing methods, including Lasso, CoSaMP and IHT algorithms. We propose an efficient algorithm based on iterative pseudo-inverse and compare it with typical compressive sensing methods. It is observed that the performance of the proposed algorithm is obviously improved in terms of restoration precision, time complexity and success rate of recovery.
Key words:Channel Estimation;Compressive Sensing

 

 

参考文献 (References)
[1] GSM, Group Speciale Mobile (GSM) Recommendations, Series 01-12. 1990.
[2] Adireddy S., Tong L., and Viswanathan H., “Optimal placement of known symbols for frequency-selective block-fading channels,” IEEE Trans. Info. Theory, vol. 48, pp. 2338–2353, August 2002.
[3] Tong L., Sadler B., Dong M., Pilot-assisted wireless transmissions: General model, design criteria, and signal processing. IEEE Signal Processing Magazine, 2004, 21(6):12-25
[4] Mazzenga F., “Channel estimation and equalization for M-QAM transmission with a hidden pilot sequence,” IEEE Trans. on Broadcasting, vol. 46, p. 170176, June 2000.
[5] David Donoho, "Compressed sensing," IEEE Trans. on Information Theory, 52(4), pp. 1289 - 1306, April 2006.
[6] Emmanuel Candès, "Compressive sampling," Int. Congress of Mathematics, 3, pp. 1433-1452, Madrid, Spain, 2006.
[7] Bajwa W., Sayeed A., and Nowak R., “Learning sparse doubly selective channels,” in Proc. 46th Annu. Allerton Conf. 2008.
[8] Emmanuel Candès and Terence Tao, "Near optimal signal recovery from random projections: Universal encoding strategies?" IEEE Trans. on Information Theory, 52(12), pp. 5406 - 5425, December 2006.
[9] Needell D., Tropp J. A., "CoSaMP: Iterative signal recovery from incomplete and inaccurate samples", Applied and Computational Harmonic Analysis, 2009.
[10] Blumensath T., Davies M. E., "Iterative hard thresholding for compressed sensing", Applied and Computational Harmonic Analysis, 2009.
[11] Venkatesh Saligrama, "Deterministic designs with deterministic guarantees: Toeplitz compressed sensing matrices, sequence design and system identification". Arxiv preprint arXiv:0806.4958 v1, 2008
[12] Zhang C Y, Wang Y Z. Signal Reconstruction for Compressive Sensing via Iterative Pseudo-Inverse Multiplication. ICSAI2012, 2012.

作者简介:
  齐  婷(1990-),女,博士研究生,研究方向为卫星通信,无线网络技术。
  温  珂(1988-),男,硕士研究生,研究方向为压缩感知技术。
  王有政(1969-),男,副研究员,研究方向为卫星通信、卫星广播和空间信息网络。

 
 
   
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