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

• Published in: IEEE signal processing letters Vol. 27; pp. 905 - 909
• Main Authors: Wang, Peilan, Fang, Jun, Duan, Huiping, Li, Hongbin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Array signal processing; Beamforming; Channel estimation; Data transmission; Intelligent reflecting surface; millimeter wave communications; Millimeter waves; Receivers; Reconfigurable intelligent surfaces; Scattering; Signal reconstruction; Sparse matrices; Surface waves; Training
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2020.2998357

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.