Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning
This work addresses beamforming efficiency for wireless communication systems, but it is incremental as it builds on existing DRL methods with specific optimizations.
The paper tackled the problem of hybrid beamforming in mmWave MU-MISO systems by proposing a multi-agent deep reinforcement learning method with prioritized replay buffer and informative rewards, achieving higher spectral efficiency and less time consumption than benchmarks.
In this letter, we investigate the hybrid beamforming based on deep reinforcement learning (DRL) for millimeter Wave (mmWave) multi-user (MU) multiple-input-single-output (MISO) system. A multi-agent DRL method is proposed to solve the exploration efficiency problem in DRL. In the proposed method, prioritized replay buffer and more informative reward are applied to accelerate the convergence. Simulation results show that the proposed architecture achieves higher spectral efficiency and less time consumption than the benchmarks, thus is more suitable for practical applications.