TY - JOUR
T1 - Bayesian mmWave Channel Estimation via Exploiting Joint Sparse and Low-Rank Structures
AU - Liu, Kaihui
AU - Li, Xingjian
AU - Fang, Jun
AU - Li, Hongbin
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - We consider the problem of channel estimation for millimeter wave (mmWave) systems, where both the base station and the mobile station employ a single radio frequency (RF) chain to reduce the hardware cost and power consumption. Recent real-world channel measurements reveal that the mmWave channels incur a certain amount of spread over the angular domains due to the scattering clusters. The angular spreads give rise to a joint sparse and low-rank channel matrix in the angular domain. To utilize this joint sparse and low-rank structure, we address the channel estimation problem within a Bayesian framework. Specifically, we adopt a matrix factorization formulation and translate the problem of channel estimation into one of searching for two-factor matrices. To encourage a joint sparse and low-rank solution, independent sparsity-promoting priors are placed on entries of the two-factor matrices, which aims to promote sparse factor matrices with only a few non-zero columns. Based on the proposed prior model, we develop a variational Bayesian inference method for the mmWave channel estimation. The simulation results show that our proposed method presents a considerable performance improvement over the state-of-the-art compressed sensing-based channel estimation methods.
AB - We consider the problem of channel estimation for millimeter wave (mmWave) systems, where both the base station and the mobile station employ a single radio frequency (RF) chain to reduce the hardware cost and power consumption. Recent real-world channel measurements reveal that the mmWave channels incur a certain amount of spread over the angular domains due to the scattering clusters. The angular spreads give rise to a joint sparse and low-rank channel matrix in the angular domain. To utilize this joint sparse and low-rank structure, we address the channel estimation problem within a Bayesian framework. Specifically, we adopt a matrix factorization formulation and translate the problem of channel estimation into one of searching for two-factor matrices. To encourage a joint sparse and low-rank solution, independent sparsity-promoting priors are placed on entries of the two-factor matrices, which aims to promote sparse factor matrices with only a few non-zero columns. Based on the proposed prior model, we develop a variational Bayesian inference method for the mmWave channel estimation. The simulation results show that our proposed method presents a considerable performance improvement over the state-of-the-art compressed sensing-based channel estimation methods.
KW - Angular spread
KW - Compressed sensing
KW - Joint sparse and low-rank
KW - MmWave channel estimation
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U2 - 10.1109/ACCESS.2019.2910088
DO - 10.1109/ACCESS.2019.2910088
M3 - Article
AN - SCOPUS:85065096410
VL - 7
SP - 48961
EP - 48970
JO - IEEE Access
JF - IEEE Access
M1 - 8685095
ER -