TY - JOUR
T1 - Uplink Channel Estimation With Reduced Fronthaul Overhead in Cell-Free Massive MIMO Systems
AU - Zhao, Tianyu
AU - Chen, Shuyi
AU - Zhang, Ruoyu
AU - Chen, Hsiao Hwa
AU - Guo, Qing
N1 - Funding Information:
This work was supported in part by the Natural Science Foundation of China under Grant U1764263; in part by the Natural Science Foundation of Jiangsu Province under Grant BK20210335; in part by the Young Elite Scientists Sponsorship Program by CAST under Grant 2021QNRC001; and in part by the Fellowship of China Postdoctoral Science Foundation under Grant 2021TQ0092.
Publisher Copyright:
© 2012 IEEE.
PY - 2022/8/1
Y1 - 2022/8/1
N2 - This letter focuses on the problem of uplink channel estimation with the reduced fronthaul overhead in cell-free massive multiple-input multiple-output (mMIMO) systems. First, we propose a sub-sampling scheme to reduce the dimension of fronthaul. Then, we exploit the inherent channel sparsity and model the underdetermined channel estimation problem as an off-grid sparse signal recovery problem. Finally, an enhanced sparse Bayesian learning (ESBL) channel estimation algorithm is proposed to refine the sampled grid points and recover the sparse channel iteratively. Simulation results demonstrate that the proposed algorithm achieves a significant reduction on the fronthaul overhead and offers a better channel estimation performance.
AB - This letter focuses on the problem of uplink channel estimation with the reduced fronthaul overhead in cell-free massive multiple-input multiple-output (mMIMO) systems. First, we propose a sub-sampling scheme to reduce the dimension of fronthaul. Then, we exploit the inherent channel sparsity and model the underdetermined channel estimation problem as an off-grid sparse signal recovery problem. Finally, an enhanced sparse Bayesian learning (ESBL) channel estimation algorithm is proposed to refine the sampled grid points and recover the sparse channel iteratively. Simulation results demonstrate that the proposed algorithm achieves a significant reduction on the fronthaul overhead and offers a better channel estimation performance.
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U2 - 10.1109/LWC.2022.3177429
DO - 10.1109/LWC.2022.3177429
M3 - Article
AN - SCOPUS:85130789075
SN - 2162-2337
VL - 11
SP - 1718
EP - 1722
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
IS - 8
ER -