大规模MIMO系统稀疏度自适应信道估计算法

doi:10.11959/j.issn.1000-436x.2017291

通信学报 ›› 2017, Vol. 38 ›› Issue (12): 57-62.doi: 10.11959/j.issn.1000-436x.2017291

大规模MIMO系统稀疏度自适应信道估计算法

戈立军^1,²,郭徽^1,²(),李月^1,²,赵澜^1,²

¹ 天津工业大学电子与信息工程学院，天津 300387
² 天津市光电检测技术与系统重点实验室，天津 300387

修回日期:2017-11-28 出版日期:2017-12-01 发布日期:2018-01-19
作者简介:戈立军（1984-），男，天津人，博士，天津工业大学副教授，主要研究方向为MIMO-OFDM无线通信技术、FPGA技术及应用、通信与信息系统开发。|郭徽（1992-），女，山东潍坊人，天津工业大学硕士生，主要研究方向为MIMO-OFDM无线通信技术。|李月（1991-），女，河南安阳人，天津工业大学硕士生，主要研究方向为OFDM无线通信技术。|赵澜（1993-），女，天津人，天津工业大学硕士生，主要研究方向为OFDM无线通信技术。
基金资助:
国家自然科学基金资助项目(61302062);天津市应用基础及前沿技术研究计划青年基金资助项目(13JCQNJC00900)

Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems

Li-jun GE^1,²,Hui GUO^1,²(),Yue LI^1,²,Lan ZHAO^1,²

¹ School of Electronics and Information Engineering，Tianjin Polytechnic University，Tianjin 300387，China
² Tianjin Key Laboratory of Optoelectronic Detection Technology and System，Tianjin 300387，China

Revised:2017-11-28 Online:2017-12-01 Published:2018-01-19
Supported by:
The National Natural Science Foundation of China(61302062);The Research Program of Application Foundation and Advanced Technology of Tianjin for Young Scientist(13JCQNJC00900)

摘要/Abstract

摘要：

针对信道路径数未知的大规模多输入多输出（MIMO，multi-input multi-output）系统，提出一种稀疏度自适应的压缩感知信道估计方法——块稀疏自适应匹配追踪（BSAMP，block sparsity adaptive matching pursuit）算法。利用大规模MIMO系统子信道的联合稀疏性，通过设置阈值及寻找最大后向差分位置对支撑集原子进行快速初步选择，同时考虑了观测矩阵非正交性造成的能量弥散，提高算法的估计性能；通过正则化对原子进行二次筛选，以提高算法的稳定性。仿真表明，该算法能快速、准确地恢复稀疏度未知的大规模MIMO信道信息。

关键词: 大规模MIMO, 压缩感知, 信道估计, 稀疏度自适应

Abstract:

A sparsity-adaptive channel estimation algorithm based on compressive sensing was proposed for massive MIMO systems when the number of channel multi-paths was unknown.By exploiting the joint sparsity characteristics of the sub-channels，the proposed block sparsity adaptive matching pursuit (BSAMP) algorithm first selected atoms by setting a threshold and finding the position of the maximum backward difference，which reduces the energy dispersion caused by the non-orthogonality of the observation matrix and improves the performance of the algorithm.Then a regularization method was utilized to improve the stability of the algorithm.Simulation results demonstrate that the proposed algorithm recovers the channel state information accurately and shows a high computational efficiency.

Key words: massive MIMO, compressive sensing, channel estimation, sparsity adaptive

中图分类号:

TN914.4

戈立军,郭徽,李月,赵澜. 大规模MIMO系统稀疏度自适应信道估计算法[J]. 通信学报, 2017, 38(12): 57-62.

Li-jun GE,Hui GUO,Yue LI,Lan ZHAO. Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems[J]. Journal on Communications, 2017, 38(12): 57-62.

图/表 5

参考文献 17

[1]	MARZETTA T L . Non-cooperative cellular wireless with unlimited numbers of base station antennas[J]. IEEE Transaction on Wireless Communications, 2010,9(11): 3590-3600.
[2]	ZHENG Z , HAO C Y , YANG X M . Least squares channel estimation with noise suppression for OFDM systems[J]. Electronics Letters, 2016,52(1): 37-39.
[3]	LIN B J , TANG X , GHASSEMLOOY Z ,et al. Efficient frequency-domain channel equalisation methods for OFDM visible light communications[J]. IET Communications, 2017,11(1): 25-29.
[4]	KALAKECH A , BERBINEAU M , DAYOUB I ,et al. Time-domain LMMSE channel estimator based on sliding window for OFDM systems in high-mobility situations[J]. IEEE Transactions on Vehicular Technology, 2015,64(12): 5728-5740.
[5]	CRAMER R J M , SCHOLTZ R A , WIN M Z . Evaluation of an ultra-wide-band propagation channel[J]. IEEE Transactions on Antennas and Propagation, 2002,50(5): 561-570.
[6]	PAREDES J L , ARCE G R , WANG Z M . Ultra-wideband compressed sensing:channel estimation[J]. IEEE Journal of Selected Topics Signal Process, 2007,1(3): 383-395.
[7]	SEO J , JANG S , YANG J ,et al. Analysis of pilot-aided channel estimation with optimal leakage suppression for OFDM systems[J]. IEEE Communications Letter, 2010,14(9): 809-811.
[8]	CAI T T , WANG L . Orthogonal matching pursuit for sparse signal recovery with noise[J]. IEEE Transactions on Information Theory, 2011,57(7): 4680-4688.
[9]	HUI L N , JIANG H , YANG D . Study on channel estimation reconstruction algorithm for OFDM systems based on compressed sensing[J]. Advances in Information Sciences and Service Sciences, 2012,4(3): 270-276.
[10]	ZU B K , XIA X Y , XIA K W ,et al. Channel estimation on 60 GHz wireless communication system based on subspace pursuit[J]. Journal of Computational Information Systems, 2014,10(24): 10565-10572.
[11]	ZHANG Y , VENKATESAN R , DOBRE O A ,et al. An adaptive matching pursuit algorithm for sparse channel estimation[C]// 2015 IEEE Wireless Communications and Networking Conference(WCNC 2015). 2015: 626-630.
[12]	BARBOTIN Y , HORMATI A , RANGAN S ,et al. Estimation of sparse MIMO channels with common support[J]. IEEE Transactions on Communications, 2012,60(12): 3705-3716.
[13]	GAO Z , DAI L L , DAI W ,et al. Structured compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO[J]. IEEE Transactions on Communications, 2016,64(2): 601-617.
[14]	DAVID L D , MICHAEL E . Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization[J]. Proceedings of the National Academy of Sciences of the United States of America, 2003,100(5): 2197-2202.
[15]	CANDES E , TAO T . Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005,51(12): 4203-4215.
[16]	CANDES E , ROMBERG J , TAO T . Robust uncertainty principles:exact signal reconstruction from highly incomplete Fourier information[J]. IEEE Transactions on Information Theory, 2006,52(2): 489-509.
[17]	DUARTE M , ELDAR Y . Structured compressed sensing:from theory to applications[J]. IEEE Transactions on Signal Processing, 2009,59(9): 4053-4085.

稀疏信道恢复算法	平均运行时间/s
LS算法	0.023 28
OMP算法	2.288 70
SP算法	1.106 90
SAMP算法	93.393 0
ASSP算法	15.361 0
BSAMP算法	0.012 84

大规模MIMO系统稀疏度自适应信道估计算法

Sparsity adaptive channel estimation algorithm based on compressive sensing for massive MIMO systems

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