Please wait a minute...
浙江大学学报(工学版)
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)  2018, Vol. 52 Issue (9): 1747-1752    DOI: 10.3785/j.issn.1008-973X.2018.09.015
Computer Technology     
Group sparse channel estimation method based on pilot placement optimization
MA Heng-da, YUAN Wei-na, FU Wei
School of information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
Download:   PDF(839KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

Channel estimation using group sparsity methods were studied for sparsity of OFDM system. These methods considered both the time-selective fading and frequency-selective fading of the channel. The concept of group sparsity was introduced by the sparse representation of the channel coefficients. The non-zero components of sparse signals tended to cluster in a region. This characteristic could be used to improve the quality of the reconstruction. At the same time, the pilot pattern played an important role in channel estimation. Estimation of distribution algorithm (EDA) was used to optimize pilot pattern in sparse channel estimation. The algorithm is more robust than other methods and unlikely to trap into local minima. Both theoretical analysis and simulation results show that the scheme is more effective than the traditional estimation method. The simulation results indicate the applicability of the scheme by using different reconstruction methods and group sizes.

Received: 23 June 2017      Published: 20 September 2018
CLC:  TN929  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Cite this article:

MA Heng-da, YUAN Wei-na, FU Wei. Group sparse channel estimation method based on pilot placement optimization. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(9): 1747-1752.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2018.09.015     OR     http://www.zjujournals.com/eng/Y2018/V52/I9/1747


基于导频放置优化的组稀疏信道估计方法

针对OFDM系统信道的稀疏性，研究组稀疏信道估计方法；考虑信道的时间选择性和频率选择性，由信道系数的稀疏表示引入组稀疏概念，利用稀疏信号的非零分量趋向于成簇出现的组稀疏特性，提高重建质量.考虑到导频对信道估计性能的重要作用，采用分布估计算法（EDA）优化组稀疏信道估计中的导频放置模式.该方法具有较好的鲁棒性，不会陷入导频搜索的局部最小值，可以得到更小相关性的感知矩阵.理论分析和仿真结果均表明，该方案与传统估计方法相比，均方误差性能更加优异.仿真又采用了不同的重构方法和分组大小进行对比，均能证明该方法的适用性.

[1] ENGELS M. Wireless OFDM systems:How to make them work?[M]. Norwell:Springer Science & Business Media, 2002:33-36.
[2] BERTHOLD U, JONDRAL F K, BRANDES S, et al. OFDM-based overlay systems:a promising approach for enhancing spectral efficiency[Topics in radio communications] [J]. IEEE Communications Magazine, 2007, 45(12):52-58.
[3] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4):1289-1306.
[4] BAJWA W U, HAUPT J, RAZ G, et al. Compressed channel sensing[C]//200842nd Annual Conference on Information Sciences and Systems. New Jersey:IEEE, 2008:5-10.
[5] BAJWA W U, HAUPT J, SAYEED A M, et al. Compressed channel sensing:a new approach to estimating sparse multipath channels[J]. Proceedings of the IEEE, 2010, 98(6):1058-1076.
[6] TAUBOCK G, HLAWATSCH F, EIWEN D, et al. Compressive estimation of doubly selective channels in multicarrier systems:leakage effects and sparsity-enhancing processing[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2):255-271.
[7] EIWEN D, TAUBÖCK G, HLAWATSCH F, et al. Group sparsity methods for compressive channel estimation in doubly dispersive multicarrier systems[C]//2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Marrakech:IEEE, 2010:1-5.
[8] TROPP J A. Greed is good:algorithmic results for sparse approximation[J]. IEEE Transactions on Information Theory, 2004, 50(10):2231-2242.
[9] HE X, SONG R. Pilot pattern optimization for compressed sensing based sparse channel estimation in OFDM systems[C]//2010 International Conference on Wireless Communications & Signal Processing (WCSP). Suzhou:IEEE, 2010:1-5.
[10] QI C, WU L. Optimized pilot placement for sparse channel estimation in OFDM systems[J]. IEEE Signal Processing Letters, 2011, 18(12):749-752.
[11] QI C, WU L. A study of deterministic pilot allocation for sparse channel estimation in OFDM systems[J]. IEEE Communications Letters, 2012, 16(5):742-744.
[12] QI C, YUE G, WU L, et al. Pilot design schemes for sparse channel estimation in OFDM systems[J]. IEEE Transactions on Vehicular Technology, 2015, 64(4):1493-1505.
[13] QI C, YUE G, WU L, et al. Pilot design for sparse channel estimation in OFDM-based cognitive radio systems[J]. IEEE Transactions on Vehicular Technology, 2014, 63(2):982-987.
[14] NAJJAR L. Pilot allocation by Genetic Algorithms for sparse channel estimation in OFDM systems[C]//21st European Signal Processing Conference (EUSIPCO 2013). Marrakech:IEEE, 2013:1-5.
[15] HE X, SONG R, ZHU W P. Pilot allocation for sparse channel estimation in MIMO-OFDM systems[J]. IEEE Transactions on Circuits and Systems Ⅱ:Express Briefs, 2013, 60(9):612-616.
[16] LARRAÑAGA P., LOZANO J. A. Estimation of distribution algorithms:a new tool for evolutionary computation[M]. New York:Springer Science & Business Media, 2001:58-59
[17] FLANDRIN P. Time-frequency/time-scale analysis[M]. Orlando:Academic Press, 1998:237-240.
[18] FORNASIER M. Theoretical foundations and numerical methods for sparse recovery[M]. New York:Walter de Gruyter, 2010:108-111.
[1] WU Chao, LIU Yuan-an, WU Fan, FAN Wen-hao, TANG Bi-hua. Efficient data gathering scheme in mobility-constrained internet of things with graph theory[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(8): 1444-1451.
[2] ZHOU Yu, WANG Hong-jun, SHAO Fu-cai, SHA Wen-hao. Signal coverage detection algorithm for electromagnetic situation generation in wireless communication networks[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(6): 1088-1096.