OMPL-SBL Algorithm for Intelligent Reflecting Surface-Aided mmWave Channel Estimation

被引：5

作者：

Zhao, Wuqiong ^{[1
,2
,3
]}

You, You ^{[1
,2
,3
]}

Zhang, Li ^{[4
]}

You, Xiaohu ^{[1
,2
,3
]}

Zhang, Chuan ^{[1
,2
,3
]}

机构：

[1] Southeast Univ, Natl Mobile Commun Res Lab, LEADS, Nanjing 211189, Peoples R China

[2] Southeast Univ, Frontiers Sci Ctr Mobile Informat Commun & Secur, Nanjing 211189, Peoples R China

[3] Purple Mt Labs, Nanjing 211100, Peoples R China

[4] Univ Leeds, Sch Elect & Elect Engn, Leeds LS2 9JT, England

来源：

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2023年 / 72卷 / 11期

关键词：

Channel estimation; Millimeter wave communication; Estimation; Matching pursuit algorithms; MIMO communication; Bayes methods; Sparse matrices; Sparse Bayesian learning (SBL); intelligent reflecting surface (IRS); channel estimation (CE); compressed sensing (CS);

D O I：

10.1109/TVT.2023.3287400

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Channel estimation (CE) is critical for intelligent reflecting surface (IRS) aided millimeter wave (mmWave) multiple input multiple output (MIMO) systems. In this paper, we propose the orthogonal matching pursuit list-sparseBayesian learning (OMPL-SBL) algorithm which divides the cascaded channel estimation into two stages. The first stage calculates the prior for sparse Bayesian learning (SBL) using orthogonal matching pursuit list (OMPL) exploiting the grid sparsity of the cascaded channel, and the second stage employs the prior to obtain the accurate estimation using SBL. The proposed algorithm is able to achieve high estimation accuracy with low computational complexity compared to l(1)-minimization and Bayesian algorithms. In simulation, we show the proposed algorithm not only cuts down time complexity bymore than95% of the SBLalgorithm, but also achieves a higher estimation accuracy.

引用

页码：15121 / 15126

页数：6

共 17 条

[1] [Anonymous], 2006, Ph.D. dissertation
[2] TRICE: A Channel Estimation Framework for RIS-Aided Millimeter-Wave MIMO Systems
Ardah, Khaled
Gherekhloo, Sepideh
de Almeida, Andre L. F.
Haardt, Martin
[J]. IEEE SIGNAL PROCESSING LETTERS, 2021, 28 : 513 - 517
[3] Bayesian Compressive Sensing Using Laplace Priors
Babacan, S. Derin
Molina, Rafael
Katsaggelos, Aggelos K.
[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING, 2010, 19 (01) : 53 - 63
[4] Distributed optimization and statistical learning via the alternating direction method of multipliers
Boyd S.
Parikh N.
Chu E.
Peleato B.
Eckstein J.
[J]. Foundations and Trends in Machine Learning, 2010, 3 (01): : 1 - 122
[5] Atomic decomposition by basis pursuit
Chen, SSB
Donoho, DL
Saunders, MA
[J]. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 1998, 20 (01) : 33 - 61
[6] Fang J, 2015, INT CONF ACOUST SPEE, P3786, DOI 10.1109/ICASSP.2015.7178679
[7] Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication
Huang, Chongwen
Zappone, Alessio
Alexandropoulos, George C.
Debbah, Merouane
Yuen, Chau
[J]. IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2019, 18 (08) : 4157 - 4170
[8] Rodriguez -Fernandez J., 2018, P IEEE GLOB COMM C, P1
[9] Sanderson C., 2016, J OPEN SOURCE SOFTWA, V1, P26, DOI [DOI 10.21105/JOSS.000, 10.21105/joss.00026]
[10] Quasi-Static and Time-Selective Channel Estimation for Block-Sparse Millimeter Wave Hybrid MIMO Systems: Sparse Bayesian Learning (SBL) Based Approaches
Srivastava, Suraj
Mishra, Amrita
Rajoriya, Anupama
Jagannatham, Aditya K.
Ascheid, Gerd
[J]. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2019, 67 (05) : 1251 - 1266

← 1 2 →