@inproceedings{182c16c5479c416abf9ff38ea77278a6,
title = "GroupMixFormer: Another Blessing of DL-Based Massive MIMO Hybrid Beamforming",
abstract = "The hybrid beamforming plays a critical role in massive multiple-input multiple-output (MIMO), addressing the high hardware costs associated with fully-digital beamforming. The design of hybrid beamforming, particularly satisfying the constant modulus constraint for analog beamforming, presents a complex non-convex problem. In this paper, we propose a GroupMixFormer-based hybrid beamforming network (GMF-HBN) for multi-user scenarios using orthogonal frequency di-vision multiplexing (OFDM) with both perfect and imperfect channel state information (CSI). Simulation results show a significant performance improvement of our proposed GMF-HBN with perfect CSI compared to the Transformer-based and also demonstrate robustness with imperfect CSI.",
keywords = "GroupMixFormer, deep learning (DL), hybrid beamforming, multiple-input multiple-output (MIMO)",
author = "Ye Zeng and Li Qiao and Hengwei Zhang and Ziqi Han and Kuiyu Wang and Yang Huang and Zhen Gao",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2024 ; Conference date: 07-08-2024 Through 09-08-2024",
year = "2024",
doi = "10.1109/ICCCWorkshops62562.2024.10693777",
language = "English",
series = "International Conference on Communications in China, ICCC Workshops 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "470--474",
booktitle = "International Conference on Communications in China, ICCC Workshops 2024",
address = "United States",
}