Compressed Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems

被引：347

作者：

Wang, Peilan ^{[1
]}

Fang, Jun ^{[1
]}

Duan, Huiping ^{[2
]}

Li, Hongbin ^{[3
]}

机构：

[1] Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China

[3] Stevens Inst Technol, Dept Elect & Comp Engn, Hoboken, NJ 07030 USA

来源：

IEEE SIGNAL PROCESSING LETTERS | 2020年 / 27卷

基金：

美国国家科学基金会;

关键词：

Channel estimation; Sparse matrices; Training; Array signal processing; Receivers; Scattering; Surface waves; Intelligent reflecting surface; millimeter wave communications; channel estimation;

D O I：

10.1109/LSP.2020.2998357

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：

In this letter, we consider channel estimation for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) systems, where an IRS is deployed to assist the data transmission from the base station (BS) to a user. It is shown that for the purpose of joint active and passive beamforming, the knowledge of a large-size cascade channel matrix needs to be acquired. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing properties of Katri-Rao and Kronecker products, we find a sparse representation of the cascade channel and convert cascade channel estimation into a sparse signal recovery problem. Simulation results show that our proposed method can provide an accurate channel estimate and achieve a substantial training overhead reduction.

引用

页码：905 / 909

页数：5

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