Work

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

Year: 2020

Type: article

Abstract: 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.

Source: IEEE Signal Processing Letters

DOI: 10.1109/lsp.2020.2998357

Authors: Peilan Wang, Jun Fang, Huiping Duan, Hongbin Li

Institutions: University of Electronic Science and Technology of China, National Key Laboratory of Science and Technology on Communications, Stevens Institute of Technology

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funder: National Natural Science Foundation of China

Awards: G4600516838, 面向宽带信号的新型贝叶斯稀疏重构理论与方法研究