基于RSAMP算法的OFDM稀疏信道估计

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

Abstract

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

CLC Number:

TN911.7

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

Figures/Tables 7

Fig.1

Table 1

Fig.2

Table 2

Fig.3

Fig.4

Fig.5

References 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]	Jian DANG, Yewei LI, Yongdong ZHU, Rongbin GUO, Zaichen ZHANG, Liang WU. Progressive channel estimation method for RIS-assisted communication system [J]. Systems Engineering and Electronics, 2022, 44(3): 998-1006.
[2]	Hao WU, Ying KANG, Songhu GE, Yaxing LI, Jin MENG. Interference rejection combining method in receive chain of VHF/UHF communication radio [J]. Systems Engineering and Electronics, 2022, 44(3): 1014-1021.
[3]	Buhua LIU, Dan DING, Liu YANG, Naiyang XUE, Zhongqian LIU. OFDM data transmission technology of UAV based on deep neural network [J]. Systems Engineering and Electronics, 2022, 44(2): 696-702.
[4]	Yonglei QI, Xihong CHEN, Dizhe YUAN. Joint channel estimation and equalization algorithm based on UW for SC-FDE system [J]. Systems Engineering and Electronics, 2022, 44(10): 3258-3265.
[5]	Ziyan LIU, Shanshan MA, Jing LIANG, Mingcheng ZHU, Lei YUAN. Attention mechanism based CNN channel estimation algorithm in millimeter-wave massive MIMO system [J]. Systems Engineering and Electronics, 2022, 44(1): 307-312.
[6]	Xiaoyu MA, Jinsheng ZHANG, Ting LI. Image compress and encryption method based on Chua's circuit and compressed sensing [J]. Systems Engineering and Electronics, 2021, 43(9): 2407-2412.
[7]	Wenbo GUO, Changqing SONG, Yidan ZHANG, Hongzhi ZHAO, Shihai SHAO, Youxi TANG. Experimental testbed of self-interference suppression in electromagnetic spectrum umbrella [J]. Systems Engineering and Electronics, 2021, 43(9): 2637-2641.
[8]	Kai SHAO, Liancheng CHEN, Yin LIU. Learning and estimation of high mobility Jakes channel [J]. Systems Engineering and Electronics, 2021, 43(4): 1119-1125.
[9]	Yingxin ZHAO, Changfeng WANG, Hong WU, Ming ZHANG, Yingjie HUANG, Legeng WANG, Zhiyang LIU. Channel estimation algorithm based on compressed sensing with maximizing negative entropy [J]. Systems Engineering and Electronics, 2021, 43(4): 1126-1132.
[10]	Guiyong LI, Min YU, Yongkun YU. Distributed compressed sensing LMMSE channel estimation in massive MIMO systems [J]. Systems Engineering and Electronics, 2021, 43(3): 823-831.
[11]	Binrui LI, Zhongpei ZHANG. Channel estimation for reconfigurable intelligent surface assisted low-resolution quantized massive MIMO [J]. Systems Engineering and Electronics, 2021, 43(10): 2984-2991.
[12]	Yongjin LIU, Xihong CHEN. Prototype filter design based on channel estimation for OQAM/OFDM systems [J]. Systems Engineering and Electronics, 2020, 42(1): 191-197.
[13]	WU Peng, CHEN Xihong, QIU Shangfei, ZHANG Kai. IAM channel estimation with clipping compensation for OFDM/OQAM system [J]. Systems Engineering and Electronics, 2018, 40(9): 2092-2099.
[14]	XUE Lunsheng, ZHANG Kai, CHEN Hang, QIU Shangfei. Iterative scatter pilot channel estimation in OQAM/OFDM [J]. Systems Engineering and Electronics, 2018, 40(6): 1358-1362.
[15]	QIU Shangfei, XUE Lunsheng, WU Peng. Improved precoding channel estimation method for OFDM/OQAM system [J]. Systems Engineering and Electronics, 2018, 40(5): 1129-1134.

OFDM sparse channel estimation based on RSAMP algorithm

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 24

Related Articles 15

Recommended Articles

Metrics

Comments