Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming

• URL: http://arxiv.org/abs/2208.05443v1
• Date: Wed, 10 Aug 2022 16:55:00 GMT
• Title: Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming
• Authors: Hamed Hojatian, J\'er\'emy Nadal, Jean-Fran\c{c}ois Frigon, and Fran\c{c}ois Leduc-Primeau
• Abstract summary: Subarray hybrid beamforming is a promising technology to improve the energy efficiency of massive systems. We propose a novel unsupervised learning approach to design the hybrid beamforming while supporting neuralized phase-shifters and CSI.
• Score: 1.181206257787103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems. In particular, subarray hybrid beamforming can further decrease power consumption by reducing the number of phase-shifters. However, designing the hybrid beamforming vectors is a complex task due to the discrete nature of the subarray connections and the phase-shift amounts. Finding the optimal connections between RF chains and antennas requires solving a non-convex problem in a large search space. In addition, conventional solutions assume that perfect CSI is available, which is not the case in practical systems. Therefore, we propose a novel unsupervised learning approach to design the hybrid beamforming for any subarray structure while supporting quantized phase-shifters and noisy CSI. One major feature of the proposed architecture is that no beamforming codebook is required, and the neural network is trained to take into account the phase-shifter quantization. Simulation results show that the proposed deep learning solutions can achieve higher sum-rates than existing methods.

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