基于压缩感知的大规模多输入多输出空间共稀疏信道估计

doi:10.11772/j.issn.1001-9081.2017082027

计算机应用 ›› 2018, Vol. 38 ›› Issue (4): 1106-1110.DOI: 10.11772/j.issn.1001-9081.2017082027

基于压缩感知的大规模多输入多输出空间共稀疏信道估计

唐虎¹, 刘紫燕¹, 刘世美¹, 冯丽²

1. 贵州大学大数据与信息工程学院, 贵阳 550025;
2. 国家电网重庆市电力公司, 重庆 400014

收稿日期:2017-08-18 修回日期:2017-11-06 出版日期:2018-04-10 发布日期:2018-04-09
通讯作者: 刘紫燕
作者简介:唐虎(1992-),男,湖北荆州人,硕士研究生,主要研究方向:移动通信系统;刘紫燕(1974-),女,贵州都匀人,副教授,硕士,主要研究方向:无线通信、嵌入式通信、大数据挖掘分析;刘世美(1992-),女,贵州毕节人,硕士研究生,主要研究方向:移动通信系统;冯丽(1977-),女,贵州贵阳人,高级工程师,博士,主要研究方向:智能控制、电力系统稳定性。
基金资助:
贵州省科学技术基金资助项目（黔科合基础[2016]1054）；贵州大学研究生创新基金资助项目（研理工2017015）；贵州省联合资金资助项目（黔科合LH字[2017]7226号）。

Spatially common sparsity channel estimation based on compressive sensing for massive multi-input multi-output system

TANG Hu¹, LIU Ziyan¹, LIU Shimei¹, FENG Li²

1. College of Big Data and Information Engineering, Guizhou University, Guiyang Guizhou 550025, China;
2. State Grid Chongqing Electric Power Company, Chongqing 400014, China

Received:2017-08-18 Revised:2017-11-06 Online:2018-04-10 Published:2018-04-09
Supported by:
This work is partially supported by the Science and Technology Fund Project of Guizhou Province ([2016]1054), the Guizhou University Graduate Innovation Fund (Manager 2017015), the Joint Fund Project of Guizhou Province ([2017]7226).

摘要/Abstract

摘要： 针对频分复用双工方式的大规模多输入多输出（MASSIVE MIMO）系统在虚拟角域信道中估计精度较差的问题，提出一种基于门限的稀疏度自适应匹配追踪（BT-SAMP）算法。该算法融合了回溯正交匹配追踪（BAOMP）算法的原子选择特性和稀疏度自适应匹配追踪（SAMP）算法的自适应特性，将BAOMP算法的"添加原子"规则作为SAMP算法的原子选择预处理，通过合理的阈值添加固定的原子，然后延续SAMP算法的步长迭代自适应特性，寻找到信道矩阵近似系数最大，达到了提高SAMP算法估计精度、加快算法收敛的目的。仿真结果表明，在低信噪比（SNR）情况下，与SAMP算法相比，信道估计精度均有提高，特别是信噪比在0~10 dB时，其估计精度提升4 dB，算法的运行时间减少约61%。

关键词: 大规模多输入输出, 信道估计, 压缩感知, 稀疏度自适应匹配追踪, 虚拟角域

Abstract: Focusing on low the channel estimation accuracy is in virtual angular domain channel for Frequency Division Duplex based MASSIVE Multi-Input Multi-Output (MASSIVE MIMO) systems, a new algorithm Based on Threshold Sparsity Adaptive Matching Pursuit (BT-SAMP) was proposed. The algorithm combined the atomic selection characteristics of BAOMP algorithm and the adaptive characteristics of Sparsity Adaptive Matching Pursuit (SAMP) algorithm. The Backtracking-based Adaptive Orthogonal Matching Pursuit (BAOMP) rule of the "adding atom" algorithm was used as the atomic selection preprocessing of the SAMP algorithm, the fixed atom was added by reasonable threshold, and then the step size of the SAMP algorithm was extended to find the maximum approximation coefficient of the channel matrix, which can improve the accuracy of SAMP algorithm and accelerate the convergence speed of the algorithm. The simulation results show that the channel estimation accuracy is improved compared with the SAMP algorithm in the case of low Signal-to-Noise Ratio (SNR), especially when the SNR is 0 to 10 dB, the estimation accuracy is improved by 4 dB, and the running time of the algorithm is reduced by about 61%.

Key words: MASSIVE Multi-input Multi-output (MASSIVE MIMO), channel estimation, Compression Sensing (CS), Sparsity Adaptive Matching Pursuit (SAMP), virtual angular domain

中图分类号:

TN929.5

唐虎, 刘紫燕, 刘世美, 冯丽. 基于压缩感知的大规模多输入多输出空间共稀疏信道估计[J]. 计算机应用, 2018, 38(4): 1106-1110.

TANG Hu, LIU Ziyan, LIU Shimei, FENG Li. Spatially common sparsity channel estimation based on compressive sensing for massive multi-input multi-output system[J]. Journal of Computer Applications, 2018, 38(4): 1106-1110.

参考文献

[1] LU L, LI G Y, SWINDLEHURST A L, et al. An overview of massive MIMO:benefits and challenges[J]. IEEE Journal of Selected Topics in Signal Processing, 2014, 8(5):742-758.
[2] RUSEK F, PERSSON D, LAU B K, et al. Scaling up MIMO:opportunities and challenges with very large arrays[J]. IEEE Signal Processing Magazine, 2012, 30(1):40-60.
[3] CHOI J, LOVE D J, BIDIGARE P. Downlink training techniques for FDD massive MIMO systems:open-loop and closed-loop training with memory[J]. IEEE Journal of Selected Topics in Signal Processing, 2014, 8(5):802-814.
[4] DAI L, WANG Z, YANG Z. Spectrally efficient time-frequency training OFDM for mobile large-scale MIMO systems[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(2):251-263.
[5] GAO Z, DAI L, WANG Z, et al. Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO[J]. IEEE Transactions on Signal Processing, 2015, 63(23):6169-6183.
[6] GAO Z, DAI L, WANG Z. Structured compressive sensing based superimposed pilot design in downlink large-scale MIMO systems[J]. Electronics Letters, 2014, 50(12):896-898.
[7] DAI L, WANG Z, YANG Z. Spectrally efficient time-frequency training OFDM for mobile large-scale MIMO systems[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(2):251-263.
[8] 谢建超.Massive MIMO通信系统中信道估计技术研究[D]. 南京:南京邮电大学, 2016:44-54.(XIE J C. Research on channel estimation technology for massive MIMO communication system[D]. Nanjing:Nanjing University of Posts and Telecommunications, 2016:44-54.)
[9] QI C, HUANG Y, JIN S, et al. Sparse channel estimation based on compressed sensing for massive MIMO systems[C]//Proceedings of the 2015 IEEE International Conference on Communications. Piscataway, NJ:IEEE, 2015:4558-4563.
[10] RAO X, LAU V K N, KONG X. CSIT estimation and feedback for FDD multi-user massive MIMO systems[C]//Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway, NJ:IEEE, 2014:3157-3161.
[11] NAM J, ADHIKARY A, AHN J Y, et al. Joint spatial division and multiplexing:opportunistic beamforming, user grouping and simplified downlink scheduling[J]. IEEE Journal of Selected Topics in Signal Processing, 2014, 8(5):876-890.
[12] HU A, LV T, GAO H, et al. An ESPRIT-based approach for 2-D localization of incoherently distributed sources in massive MIMO systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2014, 8(5):996-1011.
[13] 刘紫燕, 唐虎, 刘世美.基于压缩感知的多小区MASSIVE MIMO信道估计[J]. 计算机应用, 2017, 37(9):2474-2478.(LIU Z Y, TANG H, LIU S M. Multi-cell channel estimation on compressive sensing in massive MIMO system[J]. Journal of Computer Applications, 2017, 37(9):2474-2478.)
[14] YIN H, GESBERT D, FILIPPOU M, et al. A coordinated approach to channel estimation in large-scale multiple-antenna systems[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(2):264-273.
[15] 徐立军.压缩感知重构算法及其应用研究[D]. 太原:中北大学, 2016:24-25.(XIU L J. Research on compressed sensing algorithms and applications[D]. Taiyuan:North University of China, 2016:24-25.)
[16] ZHANG Y, QI R, ZENG Y. Backtracking-based matching pursuit method for distributed compressed sensing[J]. Multimedia Tools & Applications, 2017, 76(13):14691-14710.
[17] WANG A, WANG Y, JIANG L. Improved sparse channel estimation for multi-user massive MIMO systems with compressive sensing[C]//Proceedings of the 2015 International Conference on Wireless Communications & Signal Processing. Piscataway, NJ:IEEE, 2015:1-5.

基于压缩感知的大规模多输入多输出空间共稀疏信道估计

Spatially common sparsity channel estimation based on compressive sensing for massive multi-input multi-output system

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