Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming

TL;DR

This paper introduces an unsupervised deep learning approach using RSSI for hybrid beamforming in massive MIMO systems, reducing complexity and feedback needs while achieving high spectral efficiency.

Contribution

It presents a novel RSSI-based unsupervised deep learning method for hybrid beamforming, including new designs for synchronization signals and codebooks, outperforming existing solutions.

Findings

Significantly increases spectral efficiency in FDD systems

Achieves near-optimal sum-rate performance

Outperforms state-of-the-art full-CSI solutions

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.