Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming

• URL: http://arxiv.org/abs/2007.00038v2
• Date: Thu, 2 Jul 2020 21:35:54 GMT
• Title: Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming
• Authors: Hamed Hojatian, Jeremy Nadal, Jean-Francois Frigon, Francois Leduc-Primeau
• Abstract summary: Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems. This paper proposes a novel RSSI-based unsupervised deep learning method to design the hybrid beamforming.
• Score: 1.290382979353427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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