TY - JOUR
T1 - Compressed CPD-Based Channel Estimation and Joint Beamforming for RIS-Assisted Millimeter Wave Communications
AU - Zheng, Xi
AU - Fang, Jun
AU - Wang, Hongwei
AU - Wang, Peilan
AU - Li, Hongbin
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2024
Y1 - 2024
N2 - We consider the problem of channel estimation and joint active and passive beamforming for reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We show that, with a well-designed frame-based training protocol, the received pilot signal can be organized into a low-rank third-order tensor that admits a canonical polyadic decomposition (CPD). Based on this observation, we propose a CPD-based method for estimating the cascade channels associated with different subcarriers. The proposed method exploits the intrinsic low-rankness of the CPD formulation, which is a result of the sparse scattering characteristics of mmWave channels, and thus has the potential to achieve a significant training overhead reduction. Specifically, our analysis shows that the proposed method has a sample complexity that scales quadratically with the sparsity of the cascade channel. Also, by utilizing the singular value decomposition-like structure of the effective channel, this paper develops a joint active and passive beamforming method based on the estimated cascade channels. Simulation results show that the proposed CPD-based channel estimation method attains mean square errors that are close to the Cramér-Rao bound (CRB) and present a clear advantage over the compressed sensing-based methods. In addition, the proposed joint beamforming method can effectively utilize the estimated channel parameters to achieve superior beamforming performance.
AB - We consider the problem of channel estimation and joint active and passive beamforming for reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We show that, with a well-designed frame-based training protocol, the received pilot signal can be organized into a low-rank third-order tensor that admits a canonical polyadic decomposition (CPD). Based on this observation, we propose a CPD-based method for estimating the cascade channels associated with different subcarriers. The proposed method exploits the intrinsic low-rankness of the CPD formulation, which is a result of the sparse scattering characteristics of mmWave channels, and thus has the potential to achieve a significant training overhead reduction. Specifically, our analysis shows that the proposed method has a sample complexity that scales quadratically with the sparsity of the cascade channel. Also, by utilizing the singular value decomposition-like structure of the effective channel, this paper develops a joint active and passive beamforming method based on the estimated cascade channels. Simulation results show that the proposed CPD-based channel estimation method attains mean square errors that are close to the Cramér-Rao bound (CRB) and present a clear advantage over the compressed sensing-based methods. In addition, the proposed joint beamforming method can effectively utilize the estimated channel parameters to achieve superior beamforming performance.
KW - Channel estimation
KW - joint active and passive beamforming
KW - millimeter wave communications
KW - reconfigurable intelligent surface
UR - http://www.scopus.com/inward/record.url?scp=85195365543&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85195365543&partnerID=8YFLogxK
U2 - 10.1109/TVT.2024.3411069
DO - 10.1109/TVT.2024.3411069
M3 - Article
AN - SCOPUS:85195365543
SN - 0018-9545
VL - 73
SP - 15214
EP - 15226
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 10
ER -