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

## TL;DR

This paper introduces PrecoderNet, a deep reinforcement learning approach for hybrid beamforming in millimeter wave MIMO systems, achieving high spectral efficiency and robustness with low latency despite imperfect channel information.

## Contribution

The paper presents a novel DRL-based hybrid beamforming method called PrecoderNet, which jointly designs digital precoders and analog combiners efficiently.

## Key findings

- PrecoderNet outperforms traditional methods in spectral efficiency.
- It demonstrates robustness to CSI imperfections.
- It reduces time consumption in beamforming design.

## 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.