基于RSAMP算法的OFDM稀疏信道估计

doi:10.12305/j.issn.1001-506X.2021.08.31

系统工程与电子技术 ›› 2021, Vol. 43 ›› Issue (8): 2290-2296.doi: 10.12305/j.issn.1001-506X.2021.08.31

基于RSAMP算法的OFDM稀疏信道估计

季策^1,², 王金芝^1,², 李伯群^3,*

1. 东北大学计算机科学与工程学院, 辽宁沈阳 110169
2. 东北大学医学影像智能计算教育部重点实验室, 辽宁沈阳 110169
3. 辽宁科技大学电子与信息工程学院, 辽宁鞍山 114051

收稿日期:2020-07-23 出版日期:2021-07-23 发布日期:2021-08-05
通讯作者: 李伯群
作者简介:季策(1969—), 女, 副教授, 博士, 主要研究方向为OFDM关键技术研究、盲信号处理|王金芝(1995—), 女, 硕士研究生, 主要研究方向为OFDM系统信道估计技术研究|李伯群(1970—), 男, 教授, 博士, 主要研究方向为复杂工业过程建模、智能控制及深度学习
基金资助:
国家自然科学基金(61671141);国家自然科学基金(61701100);国家自然科学基金(61673093)

OFDM sparse channel estimation based on RSAMP algorithm

Ce JI^1,², Jinzhi WANG^1,², Boqun LI^3,*

1. School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
2. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang 110169, China
3. School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051, China

Received:2020-07-23 Online:2021-07-23 Published:2021-08-05
Contact: Boqun LI

摘要/Abstract

摘要：

为了提高稀疏度自适应贪婪迭代(sparsity adaptive greedy iterative, SAGI)算法的重构性能, 缩短重构时间, 提出了一种基于有限等距性质(restricted isometry property, RIP)的稀疏度预测自适应匹配追踪(RIP based prediction-sparsity adaptive matching pursuit, RSAMP)算法, 并成功将其应用于正交频分复用(orthogonal frequency division multiplexing, OFDM)系统信道估计。首先, 提出一种基于RIP的稀疏度预测方法, 可以在稀疏度未知的情况下快速精确地逼近真实稀疏度, 大大缩短了算法的运行时间。其次, 利用主成分分析法对观测矩阵采取了优化处理, 提高了算法的重构性能。仿真实验显示, 相较于SAMP、SAGI算法, 本文提出的RSAMP算法可以获取更好的估计性能和更短的运行时间。

关键词: 正交频分复用系统, 信道估计, 有限等距性质准则, 稀疏度预测, 观测矩阵, 重构算法

Abstract:

In order to improve the reconstruction performance of the sparsity adaptive greedy iteration (SAGI) algorithm and shorten the reconstruction time, a restricted isometry property (RIP) based prediction-sparsity adaptive matching pursuit (RSAMP) algorithm is proposed and successfully applied to channel estimation of orthogonal frequency division multiplexing (OFDM) system. Firstly, a RIP based sparsity prediction method is proposed, which can approximate the real sparsity quickly and accurately in the case of unknown sparsity, greatly reducing the running time of the algorithm. Secondly, the observation matrix is optimized by principal component analysis, which improves the reconstruction performance of the algorithm. Simulation experiments show that the proposed RSAMP algorithm in this paper can achieve better channel estimation performance and shorter running time compared with SAMP algorithm and SAGI algorithm.

Key words: orthogonal frequency division multiplexing (OFDM) system, channel estimation, restricted isometry property (RIP) criterion, prediction of sparsity, observation matrix, reconstruction algorithm

中图分类号:

TN911.7

季策, 王金芝, 李伯群. 基于RSAMP算法的OFDM稀疏信道估计[J]. 系统工程与电子技术, 2021, 43(8): 2290-2296.

Ce JI, Jinzhi WANG, Boqun LI. OFDM sparse channel estimation based on RSAMP algorithm[J]. Systems Engineering and Electronics, 2021, 43(8): 2290-2296.

图/表 7

图1

表1

图2

表2

图3

图4

图5

参考文献 24

1	张继东, 郑宝玉. 基于导频的OFDM信道估计及其研究进展[J]. 通信学报, 2003, 24 (11): 116- 124. doi: 10.3321/j.issn:1000-436X.2003.11.017
	ZHANG J D , DENG B Y . Overview of pilot-aided channel estimation in OFDM[J]. Journal on Communications, 2003, 24 (11): 116- 124. doi: 10.3321/j.issn:1000-436X.2003.11.017
2	FARZAMNIA A, HLAING A W, HALDAR M K, et al. Channel estimation for sparse channel OFDM systems using least square and minimum mean square error techniques[C]//Proc. of the IEEE International Conference on Engineering and Technology (ICET), 2017.
3	SUTAR M B, PATIL V S. LS and MMSE estimation with different fading channels for OFDM system[C]//Proc. of the IEEE Information Conference on Electronics, Communication and Aerospace Technology(ICECA), 2017: 740-745.
4	DONOHO D L . Compressed sensing[J]. IEEE Trans.on Information Theory, 2006, 52 (4): 1289- 1306. doi: 10.1109/TIT.2006.871582
5	CANDES E J , ROMBERG J , TAO T . Robust uncertainty principles: exact signal recognition from highly incomplete frequency information[J]. IEEE Trans.on Information Theory, 2006, 52 (2): 489- 509. doi: 10.1109/TIT.2005.862083
6	BARANIUK R G . Compressive sensing[J]. IEEE Signal Processing Magazine, 2007, 24 (4): 118- 121. doi: 10.1109/MSP.2007.4286571
7	LACRUZ J, RAMIREZ J, PAREDES J. Robust sparse channel estimation for OFDM system using an iterative algorithm based on complex median[C]//Proc. of the IEEE International Confe-rence on Acoustics, Speech and Signal Processing (ICASSP), 2014: 6429-6433.
8	TROPP J A , GILBERT A C . Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Trans.on Information Theory, 2007, 53 (12): 4655- 4666. doi: 10.1109/TIT.2007.909108
9	TAN G P , WU B Y , HERFET T . Performance analysis of OMP-based channel estimation in mobile OFDM system[J]. IEEE Trans.on Wireless Communication, 2018, 17 (5): 3459- 3473. doi: 10.1109/TWC.2018.2813380
10	DAI W , MILENKOVIC O . Subspace pursuit for compressive sensing signal reconstruction[J]. IEEE Trans.on Information Theory, 2009, 55 (5): 2230- 2249. doi: 10.1109/TIT.2009.2016006
11	BECERRA J A, HERRERA D, MADERO-AYORA M J, et al. Sparse model selection of digital predistorters using subspace pursuit[C]//Proc. of the 13th European Microwave Integrated Circuits Conference(EuMIC), 2018: 190-193.
12	DONOHO D L , TSAIG Y , DRORI I , et al. Sparse solution of underdetermined systems of linear equations by stage wise orthogonal matching pursuit[J]. IEEE Trans.on Information Theory, 2011, 58 (2): 1094- 1121.
13	张国平, 牟忠德. 基于压缩感知的医学图像重建方法[J]. 生物医学工程学进展, 2019, 40 (4): 187- 189.
	ZHANG G P , MOU Z D . Compressed sensing-based methods for medical image reconstruction[J]. Progress in Biomedical Engineering, 2019, 40 (4): 187- 189.
14	WANG J , KWON S , SHIM B . Generalized orthogonal matching pursuit[J]. IEEE Trans.on Signal Processing, 2012, 60 (12): 6202- 6212. doi: 10.1109/TSP.2012.2218810
15	肖沈阳, 金志刚, 苏毅珊, 等. 一种优化的gOMP稀疏OFDM信道估计方法[J]. 工程科学与技术, 2017, 49 (5): 149- 155.
	XIAO S Y , JIN Z G , SU Y S , et al. An optimized gOMP algorithm for spare OFDM channel estimation[J]. Advanced Engineering Science, 2017, 49 (5): 149- 155.
16	WU H L , WANG S . Adaptive sparsity matching pursuit algorithm for sparse reconstruction[J]. IEEE Signal Processing Letters, 2012, 19 (8): 471- 474. doi: 10.1109/LSP.2012.2188793
17	WANG L, XUN L N, ZHANG D X, et al. A sparsity adaptive greedy iterative algorithm for compressed sensing[C]//Proc. of the Chinese Control Conference, 2018: 4033-4038.
18	费洪涛, 何雪云, 梁彦. 基于自适应压缩感知的OFDM稀疏信道估计研究[J]. 南京邮电大学学报, 2019, 39 (2): 49- 54.
	FEI H T , HE X Y , LIANG Y . OFDM sparse channel estimation based on adaptive compressed sensing[J]. Journal of Nanjing University of Posts and Telecommunications, 2019, 39 (2): 49- 54.
19	刘浩强, 赵洪博, 冯文全. 基于CS的正则化稀疏度变步长自适应匹配追踪算法[J]. 北京航空航天大学学报, 2017, 43 (10): 2109- 2117.
	LIU H Q , ZHAO H B , FENG W Q . Regularized sparsity variable step-size adaptive matching pursuit algorithm for compressed sensing[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43 (10): 2109- 2117.
20	李超, 王沛, 王传正. 一种改进的变步长自适应匹配追踪算法[J]. 上海师范大学学报, 2017, 46 (2): 213- 217.
	LI C , WANG P , WANG C Z . An improved step size adaptive matching pursuit algorithm[J]. Journal of Shanghai Normal University, 2017, 46 (2): 213- 217.
21	ALIHAN K, VOLKER P, HOLGER B. Deterministic matrices with a restricted isometry property for partially structured sparse signal[C]//Proc. of the International Conference on Sampling Theory and Applications, 2019.
22	LU W. Compressed sensing and sparse signal processing[D]. Victoria: University of Victoria, 2010.
23	JIE Y M , LI M C , GUO C , et al. A new construction of compressed sensing matrices for signal processing via vector spaces over finite fields[J]. Multimedia Tools and Applications, 2019, 78, 31137- 31161. doi: 10.1007/s11042-019-07947-w
24	DENG H G, HE G Z, LIU G. An improved channel estimation scheme based on compressed sensing for an orthogonal frequency division multiplexing visible light communication system[C]//Proc. of the IEEE International Conference on Computer and Communication Engineering Technology (CCET), 2018.

算法名称	运行时间/s
SAMP算法	0.297 372
SAGI算法	0.219 098
RSAMP算法	0.095 933

参数名称	取值
中心频率/GHz	2
子载波数	512
导频数	36
信道长度	50
调制方式	16QAM
多普勒频移/Hz	10
多径数	6
导频放置方式	随机
信噪比/dB	0~30

[1]	党建, 李业伟, 朱永东, 郭荣斌, 张在琛, 吴亮. 可重构智能表面通信系统的渐进信道估计方法[J]. 系统工程与电子技术, 2022, 44(3): 998-1006.
[2]	吴灏, 康颖, 葛松虎, 李亚星, 孟进. VHF/UHF通信电台接收链路中的干扰抑制合并方法[J]. 系统工程与电子技术, 2022, 44(3): 1014-1021.
[3]	刘步花, 丁丹, 杨柳, 薛乃阳, 刘仲谦. 基于DNN的无人机数据OFDM传输技术[J]. 系统工程与电子技术, 2022, 44(2): 696-702.
[4]	齐永磊, 陈西宏, 袁迪喆. SC-FDE系统中基于UW的联合信道估计均衡算法[J]. 系统工程与电子技术, 2022, 44(10): 3258-3265.
[5]	刘紫燕, 马珊珊, 梁静, 朱明成, 袁磊. 注意力机制CNN的毫米波大规模MIMO系统信道估计算法[J]. 系统工程与电子技术, 2022, 44(1): 307-312.
[6]	马啸宇, 张金生, 李婷. 基于蔡氏电路和压缩感知的图像压缩加密方法[J]. 系统工程与电子技术, 2021, 43(9): 2407-2412.
[7]	郭文博, 宋长庆, 张译丹, 赵宏志, 邵士海, 唐友喜. 电磁频谱伞罩自干扰抑制实验验证[J]. 系统工程与电子技术, 2021, 43(9): 2637-2641.
[8]	邵凯, 陈连成, 刘胤. 高移动性Jakes信道的学习与估计[J]. 系统工程与电子技术, 2021, 43(4): 1119-1125.
[9]	赵迎新, 王长峰, 吴虹, 张铭, 黄英杰, 王乐耕, 刘之洋. 基于负熵最大化的压缩感知信道估计算法[J]. 系统工程与电子技术, 2021, 43(4): 1126-1132.
[10]	李贵勇, 于敏, 余永坤. 大规模MIMO系统中分布式压缩感知LMMSE信道估计[J]. 系统工程与电子技术, 2021, 43(3): 823-831.
[11]	李彬睿, 张忠培. 可重构智能面辅助的低精度量化大规模MIMO系统的信道估计[J]. 系统工程与电子技术, 2021, 43(10): 2984-2991.
[12]	刘永进, 陈西宏. OQAM/OFDM系统中基于信道估计的原型滤波器设计[J]. 系统工程与电子技术, 2020, 42(1): 191-197.
[13]	吴鹏, 陈西宏, 邱上飞, 张凯. OFDM/OQAM系统限幅补偿IAM信道估计方法[J]. 系统工程与电子技术, 2018, 40(9): 2092-2099.
[14]	薛伦生, 张凯, 陈航, 邱上飞. OQAM/OFDM系统基于迭代的辅助导频信道估计[J]. 系统工程与电子技术, 2018, 40(6): 1358-1362.
[15]	邱上飞, 薛伦生, 吴鹏. 改进的OFDM/OQAM预编码信道估计方法[J]. 系统工程与电子技术, 2018, 40(5): 1129-1134.

基于RSAMP算法的OFDM稀疏信道估计

OFDM sparse channel estimation based on RSAMP algorithm

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 24

相关文章 15

编辑推荐

Metrics

本文评价