@inproceedings{98ac3a76fe9e4095be5326deb742977e,
title = "Channel Estimation for Massive MIMO: A Weighted Nuclear Norm Minimization Approach",
abstract = "Increased matrix dimensionality and shorter channel coherence time pose critical challenge to obtain channel state information (CSI) in millimeter-wave massive multiple-input multiple-output communication systems. The accuracy of CSI derived through beam training is often hampered by codebook design, while the CSI secured by channel estimation typically underutilizes the prior information of the channel matrix. To address these issues, we introduce a novel channel estimation algorithm that incorporates a weighted nuclear norm minimization approach which adopts fast wide-beam training to determine the weight factor. The simulation results demonstrate that the proposed method achieves more accurate estimation performance with reliable convergence when compared with traditional schemes.",
keywords = "Millimeter-wave communications, beam training, channel estimation, convolutional neural network, deep learning, multiple-input multiple-output",
author = "Qi Tan and Yuan Ma and Xingjian Zhang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Global Communications Conference, GLOBECOM 2023 ; Conference date: 04-12-2023 Through 08-12-2023",
year = "2023",
doi = "10.1109/GLOBECOM54140.2023.10437741",
language = "英语",
series = "Proceedings - IEEE Global Communications Conference, GLOBECOM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4509--4515",
booktitle = "GLOBECOM 2023 - 2023 IEEE Global Communications Conference",
address = "美国",
}