Hybrid beamforming design for massive MIMO system: a deep learning approach

Dankao Chen; Yiming Zhu; Zhouzhao Xu; Xinyu Zhang; Weiyu Dai; Zijun Huang

doi:10.1117/12.2679701

16 June 2023 Hybrid beamforming design for massive MIMO system: a deep learning approach

Dankao Chen, Yiming Zhu, Zhouzhao Xu, Xinyu Zhang, Weiyu Dai, Zijun Huang

Proceedings Volume 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023); 1270229 (2023) https://doi.org/10.1117/12.2679701
Event: International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 2023, Changsha, China

Abstract

High data rates can be achieved with large-scale MIMO systems by using hybrid beamforming (HBF), which also simplifies and lowers MIMO systems' costs, but designing hybrid precoders is an extremely challenging task that requires handling CSI feedback and solving demanding optimization problems. With the RSSI-based unsupervised deep learning approach introduced in this paper, hybrid beamforming is designed for large-scale MIMO systems. In addition, there are two approaches provided: one for designing the codebook for the simulated pre-encoder, and another for designing the synchronization signal (SS) for the initial access (IA), and different scenarios and realistic channel models are used to evaluate the system performance.

Citation Download Citation

Dankao Chen, Yiming Zhu, Zhouzhao Xu, Xinyu Zhang, Weiyu Dai, and Zijun Huang "Hybrid beamforming design for massive MIMO system: a deep learning approach", Proc. SPIE 12702, International Conference on Intelligent Systems, Communications, and Computer Networks (ISCCN 2023), 1270229 (16 June 2023); https://doi.org/10.1117/12.2679701

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