Enhancing User Throughput in Multi-panel mmWave Radio Access Networks for Beam-based MU-MIMO Using a DRL Method

Researchers developed a deep reinforcement learning approach to optimize beam management in millimeter-wave radio access networks, achieving up to 16% throughput improvements and 3-7x latency reduction. The method uses adaptive beam selection based on real-time observations to enhance multi-user MIMO performance in practical network setups.