Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming

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.

Submitted to IEEE Transactions on Wireless Communications

References in corpus (2)

• Optimal Multiuser Transmit Beamforming: A Difficult Problem with a Simple Solution Structure
• Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding

Cited by in corpus (6)

• Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming
• Multidimensional Graph Neural Networks for Wireless Communications
• Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning
• Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming
• Massive MIMO CSI Feedback using Channel Prediction: How to Avoid Machine Learning at UE?
• SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming