Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming
This work addresses the problem of reducing complexity and cost in massive MIMO systems for wireless communication, representing a novel method for a known bottleneck.
The paper tackles the challenge of hybrid beamforming design in massive MIMO systems by proposing an RSSI-based unsupervised deep learning method, which increases spectral efficiency in FDD communication and achieves near-optimal sum-rate while outperforming other state-of-the-art full-CSI solutions.
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.