TY - JOUR
T1 - Compressed Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems
AU - Wang, Peilan
AU - Fang, Jun
AU - Duan, Huiping
AU - Li, Hongbin
N1 - Publisher Copyright:
© 1994-2012 IEEE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Intelligent reflecting surface
KW - channel estimation
KW - millimeter wave communications
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U2 - 10.1109/LSP.2020.2998357
DO - 10.1109/LSP.2020.2998357
M3 - Article
AN - SCOPUS:85087159054
SN - 1070-9908
VL - 27
SP - 905
EP - 909
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
M1 - 9103231
ER -