一种基于改进ROMP的MIMO-OFDM信道估计方法

doi:10.3969/j.issn.0372-2112.2017.12.004

电子学报 ›› 2017, Vol. 45 ›› Issue (12): 2848-2854.DOI: 10.3969/j.issn.0372-2112.2017.12.004

一种基于改进ROMP的MIMO-OFDM信道估计方法

廖勇^1,2, 周昕¹, 沈轩帆¹, 洪观³

1. 重庆大学飞行器测控与通信教育部重点实验室, 重庆 400044;
2. 西安电子科技大学综合业务网理论及关键技术国家重点实验室, 陕西西安 710071;
3. 重庆大学通信工程学院, 重庆 400044

收稿日期:2016-08-26 修回日期:2016-11-02 出版日期:2017-12-25
- 通讯作者:
- 廖勇
- 作者简介:
- 周昕,女,1993年出生于重庆市渝北区.现为重庆大学通信工程学院硕士研究生.主要研究方向为宽带无线通信、压缩感知、信道估计.E-mail:Mrs-zhouxin@163.com;沈轩帆,男,1994年出生于云南省昆明市.现为重庆大学通信工程学院硕士研究生.主要研究方向为无线通信信道估计.E-mail:shenxuanfan@foxmail.com;洪观,男,1992年出生于辽宁省丹东市.现为重庆大学通信工程学院硕士研究生.主要研究方向为无线通信、频谱感知.E-mail:18696603949@163.com
- 基金资助:
- 国家自然科学基金 (No.61501066）; 重庆市基础与前沿研究计划项目 (No.cstc2015jcyjA40003）; 西安电子科技大学综合业务网理论及关键技术国家重点实验室开放基金 (No.ISN16-03）; 中央高校基本科研业务费重点基金 (No.CDJZR165505）

A Channel Estimation Method Based on Improved Regularized Orthogonal Matching Pursuit for MIMO-OFDM Systems

LIAO Yong^1,2, ZHOU Xin¹, SHEN Xuan-fan¹, HONG Guan³

1. Key Laboratory of Aerocraft TT & C and Communication, Ministry of Education, Chongqing University, Chongqing 400044, China;
2. The State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an, Shaanxi 710071, China;
3. College of Communication Engineering, Chongqing University, Chongqing 400044, China

Received:2016-08-26 Revised:2016-11-02 Online:2017-12-25 Published:2017-12-25

摘要/Abstract

摘要： 本文根据信道响应的时域稀疏性，引入压缩感知理论，针对正则化正交匹配追踪（ROMP）需已知稀疏度和原子一旦选入无法删除两大缺点，提出一种基于改进ROMP的信道估计方法.该方法结合压缩采样匹配追踪（CoSaMP）、稀疏度自适应匹配追踪（SAMP）和变步长的优点，实现稀疏信号快速准确的重建.仿真结果表明，与基于OMP、ROMP、CoSaMP、SAMP的信道估计方法相比，所提方法有效提高了MIMO-OFDM系统的归一化均方误差（NMSE）和误码率（BER）性能.

关键词: 多入多出, 正交频分复用, 压缩感知, 贪婪方法, 信道估计

Abstract: The regularized orthogonal matching pursuit (ROMP) has two drawbacks,ie,the sparsity should be known beforehand and once the atom is determined it can not be deleted.According to these two problems,this paper presents an improved ROMP channel estimation method based on compressive sensing by exploiting the sparse structure of channel impulse response.The proposed method combines the advantages of compressive sampling matching pursuit (CoSaMP),sparsity adaptive matching pursuit (SAMP) and variable step size to achieve the reconstruction of sparse signal quickly and accurately.Simulation results demonstrate that compared with the channel estimations respectively based on OMP,ROMP,CoSaMP and SAMP,the proposed method effectively improve the performance of the MIMO-OFDM systems in the normalized mean square error (NMSE) and bit error rate (BER).

Key words: multiple input multiple output (MIMO), orthogonal frequency division multiplexing (OFDM), compressed sensing, greedy method, channel estimation

中图分类号:

TN911.72

廖勇, 周昕, 沈轩帆, 洪观. 一种基于改进ROMP的MIMO-OFDM信道估计方法[J]. 电子学报, 2017, 45(12): 2848-2854.

LIAO Yong, ZHOU Xin, SHEN Xuan-fan, HONG Guan. A Channel Estimation Method Based on Improved Regularized Orthogonal Matching Pursuit for MIMO-OFDM Systems[J]. Acta Electronica Sinica, 2017, 45(12): 2848-2854.

参考文献

[1] CHEEMA S A,NASKOVSKA K,ATTAR M,et al.Performance comparison of space time block codes for different 5G air interface proposals[A].Proceedings of the 20th International ITG Workshop on Smart Antennas (WSA 2016)[C].Germany:VDE VERLAG GmbH,2016.9-11.
[2] GAO Z,DAI L,LU Z,et al.Super-resolution sparse MIMO-OFDM channel estimation based on spatial and temporal correlations[J].IEEE Communications Letters,2015,18(7):1266-1269.
[3] BERGER C R,ZHOU Z,PREISIG J C,et al.Sparse channel estimation for multicarrier underwater acoustic communication:from subspace methods to compressed sensing[J].IEEE Transactions on Signal Processing,2010,58(3):1708-1721.
[4] KHOJASTEPOUR M A,GOMADAM K S.Sparse Channel Estimation for MIMO OFDM Systems[P].US:US8619918,2013.
[5] RANGAN S,RAPPAPORT T S,E Erkip.Millimeter-wave cellular wireless networks:potentials and challenges[J].Proceedings of the IEEE,2014,102(3):366-385.
[6] QI C,HUANG Y,JIN S,et al.Sparse channel estimation based on compressed sensing for massive MIMO systems[A].IEEE International Conference on Communications[C].London UK:IEEE,2015.4558-4563.
[7] REN X,CHEN W,TAO M.Position-based compressed channel estimation and pilot design for high-mobility OFDM systems[J].IEEE Transactions on Vehicular Technology,2015,64(5):1918-1929.
[8] LEE D.MIMO OFDM channel estimation via block stagewise orthogonal matching pursuit[J].IEEE Communications Letters,2016,20(10):2115-2118.
[9] HE Y,SONG R,ZHU W.Pilot allocation for distributed compressed sensing based sparse channel estimation in MIMO-OFDM systems[J].IEEE Transactions on Vehicular Technology,2016,65(5):2990-3004.
[10] 田文飚,康健,张洋,等.基于卡尔曼滤波的压缩感知弱匹配去噪重构[J].电子学报,2014,42(6):1061-1067. TIAN Wen-biao,KANG Jian,ZHANG Yang,et al.Weakly matching pursuit denoising recovery for compressed sensing based on Kalman filtering[J].Acta Electronica Sinica,2014,42(6):1061-1067.(in Chinese)
[11] 石光明,刘丹华,高大化,等.压缩感知理论及其研究进展[J].电子学报,2009,37(5):1070-1081. SHI Guang-ming,LIU Dan-hua,GAO Da-hua,et al.Advances in theory and application of compressed sensing[J].Acta Electronica Sinica,2009,37(5):1070-1081.(in Chinese)
[12] PATI Y C,REZAⅡFAR R,KRISHNAPRASAD P S.Orthogonal matching pursuit:recursive function approximation with applications to wavelet decomposition[A].Conference Record of the Twenty-Seventh Asilomar Conference on Signals,Systems and Computers[C].Pacific Grove CA:IEEE,1993.1-3.
[13] MALLAT S G,ZHANG Z.Matching pursuits with time-frequency dictionaries[J].IEEE Transactions on Signal Processing,1993,41(12):3397-3415.
[14] NEEDELL D,VERSHYNIN R.Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit[J].Foundations of Computational Mathematics,2009,9(3):317-334.
[15] NEEDELL D,TROPP J A.CoSaMP:iterative signal recovery from incomplete and inaccurate samples[J].Applied & Computational Harmonic Analysis,2008,26(12):93-100.
[16] WU H L,WANG S.Adaptive sparsity matching pursuit algorithm for sparse reconstruction[J].IEEE Signal Processing Letters,2012,19(8):471-474.
[17] CANDèS E J.The restricted isometry property and its implications for compressed sensing[J].Comptes Rendus Mathematique,2008,346(9-10):589-592.

一种基于改进ROMP的MIMO-OFDM信道估计方法

A Channel Estimation Method Based on Improved Regularized Orthogonal Matching Pursuit for MIMO-OFDM Systems

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	吕明久, 马建朝, 韦旭, 陈文峰, 杨军, 马晓岩. 基于矩阵化原子范数的高频雷达距离-多普勒二维估计方法[J]. 电子学报, 2022, 50(5): 1150-1158.
[2]	苏林林, 陈亮, 陈菲菲, 周鑫, 焦振航, 刘钊良. 面向B5G/6G的GFDM信号高精度测距与定位研究[J]. 电子学报, 2022, 50(4): 849-859.
[3]	姜斌, 唐禹, 包建荣, 唐向宏, 刘超. 水声信道频域Turbo均衡的期望传播改进算法[J]. 电子学报, 2022, 50(3): 652-662.
[4]	魏志超, 杨春玲. 时域注意力特征对齐的视频压缩感知重构网络[J]. 电子学报, 2022, 50(11): 2584-2592.
[5]	陈文俊, 杨春玲. 图像压缩感知的特征域优化及自注意力增强神经网络重构算法[J]. 电子学报, 2022, 50(11): 2629-2637.
[6]	左婷, 王法松, 张建康, 李睿. 室内可见光通信系统中基于压缩感知的空移键控信号检测方法[J]. 电子学报, 2022, 50(1): 36-44.
[7]	冯西安, 寇思玮, 谭伟杰, 毕杨. 水声信号处理中的稀疏表示理论及应用[J]. 电子学报, 2021, 49(9): 1840-1851.
[8]	王孟依, 陈振兴, 陈智慧. 基于三级LLR检测的广义零填充三模OFDM索引调制系统研究[J]. 电子学报, 2021, 49(7): 1291-1297.
[9]	裴翰奇, 杨春玲, 魏志超, 曹燕. 基于SPL迭代思想的图像压缩感知重构神经网络[J]. 电子学报, 2021, 49(6): 1195-1203.
[10]	廖勇, 田肖懿, 蔡志镕, 花远肖, 韩庆文. 面向C-V2I的基于边缘计算的智能信道估计[J]. 电子学报, 2021, 49(5): 833-842.
[11]	禤韵怡, 杨春玲. 基于帧间组稀疏的两阶段递归增强视频压缩感知重构网络[J]. 电子学报, 2021, 49(3): 435-442.
[12]	李峰, 詹邦成, 辛蕾, 刘洋, 刘志佳, 肖化超. 基于新型联合感知矩阵的压缩学习目标识别技术[J]. 电子学报, 2021, 49(11): 2108-2116.
[13]	郑恩明, 陈新华, 周权斌, 李嶷, 杨鹤, 孟浩. 一种复域压缩感知目标方位估计方法[J]. 电子学报, 2021, 49(11): 2117-2123.
[14]	张祥莉, 王勇, 王典洪, 陈振兴. 四维信号星座图改进及相应OFDM系统模型设计[J]. 电子学报, 2020, 48(8): 1486-1492.
[15]	廖勇, 姚海梅, 花远肖, 赵砚. 一种基于深度学习的FDD大规模MIMO系统CSI反馈方法[J]. 电子学报, 2020, 48(6): 1182-1189.