PrecoderNet: Hybrid Beamforming for Millimeter Wave Systems with Deep Reinforcement Learning

Abstract

In this letter, we investigate the hybrid beamforming for millimeter wave massive multiple-input multiple-output (MIMO) system based on deep reinforcement learning (DRL). Imperfect channel state information (CSI) is assumed to be available at the base station (BS). To achieve high spectral efficiency with low time consumption, we propose a novel DRL-based method called PrecoderNet to design the digital precoder and analog combiner. The DRL agent takes the digital beamformer and analog combiner of the previous learning iteration as state, and these matrices of current learning iteration as action. Simulation results demonstrate that the PrecoderNet performs well in spectral efficiency, bit error rate (BER), as well as time consumption, and is robust to the CSI imperfection.

arXiv comment13 pages, 6 figures

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• Hybrid Beamforming for Terahertz Wireless Communications: Challenges, Architectures, and Open Problems
• Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey
• Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming
• Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning
• Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming
• Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design
• Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO
• Hybrid Beamforming for RIS-Empowered Multi-hop Terahertz Communications: A DRL-based Method
• Intelligent Radio Signal Processing: A Survey
• SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming
• RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN
• Millimeter Wave Communications on Overhead Messenger Wire: Deep Reinforcement Learning-Based Predictive Beam Tracking