PrecoderNet: Hybrid Beamforming for Millimeter Wave Systems with Deep Reinforcement Learning
This work addresses efficient beamforming in wireless communication systems, but it appears incremental as it applies DRL to a known problem with specific improvements.
The paper tackled hybrid beamforming for millimeter wave MIMO systems by proposing PrecoderNet, a deep reinforcement learning method that achieved high spectral efficiency, low bit error rate, and reduced time consumption in simulations.
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.