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arXiv · 2007.00038

Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming

Hamed Hojatian·Jeremy Nadal·Jean-Francois Frigon·Francois Leduc-Primeau
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Abstract

Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the hybrid precoder design is a challenging task requiring channel state information (CSI) feedback and solving a complex optimization problem. This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming in massive MIMO systems. Furthermore, we propose i) a method to design the synchronization signal (SS) in initial access (IA); and ii) a method to design the codebook for the analog precoder. We also evaluate the system performance through a realistic channel model in various scenarios. We show that the proposed method not only greatly increases the spectral efficiency especially in frequency-division duplex (FDD) communication by using partial CSI feedback, but also has near-optimal sum-rate and outperforms other state-of-the-art full-CSI solutions.

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Hamed Hojatian, Jeremy Nadal, Jean-Francois Frigon, Francois Leduc-Primeau. 2020-06-30. Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming. https://doi.org/10.1109/twc.2021.3080672

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