Journal
2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022)
Volume -, Issue -, Pages 3833-3838Publisher
IEEE
DOI: 10.1109/GLOBECOM48099.2022.10001155
Keywords
Massive MIMO; subarray hybrid beamforming; unsupervised learning; quantized phase-shifters
Categories
Funding
- Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-2021-04242]
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Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems. Subarray hybrid beamforming, in particular, can further reduce power consumption by reducing the number of phase-shifters. However, designing the hybrid beamforming vectors is complex due to the discrete nature of subarray connections and phase-shift amounts. Therefore, we propose a novel unsupervised learning approach to address this problem.
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 channel state information (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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