用深度强化学习优化毫米波多天线网络的波束选择，提升用户吞吐量。
Enhancing User Throughput in Multi-panel mmWave Radio Access Networks for Beam-based MU-MIMO Using a DRL Method
- 基于深度强化学习建模波束管理为马尔可夫决策过程，实时动态调整波束。
- 实测显示吞吐量最高提升16%，端到端延迟降低3-7倍。
- 适合关注毫米波通信、智能波束管理的系统设计与算法研究人员。
毫米波通信系统在采用混合波束成形的多用户多输入多输出（MU-MIMO）场景中，因动态波束选择与管理的高复杂性，面临用户吞吐量与延迟优化难题。本文提出一种基于深度强化学习（DRL）的方法，在实际多面板毫米波无线接入网络中提升用户吞吐量。所提框架将通信代理与环境的交互建模为马尔可夫决策过程（MDP），利用自适应波束管理策略，结合不同天线面板间波束的互相关性、测量的参考信号接收功率（RSRP）及波束使用统计信息，实现基于实时观测的波束选择优化。结果表明，该方法显著提升了频谱效率并降低了端到端延迟。数值实验显示，相比基线（传统波束管理），吞吐量最高提升16%，延迟降低3-7倍。
原文摘要 · Abstract (English)
Millimeter-wave (mmWave) communication systems, particularly those leveraging multi-user multiple-input and multiple-output (MU-MIMO) with hybrid beamforming, face challenges in optimizing user throughput and minimizing latency due to the high complexity of dynamic beam selection and management. This paper introduces a deep reinforcement learning (DRL) approach for enhancing user throughput in multi-panel mmWave radio access networks in a practical network setup. Our DRL-based formulation utilizes an adaptive beam management strategy that models the interaction between the communication agent and its environment as a Markov decision process (MDP), optimizing beam selection based on real-time observations. The proposed framework exploits spatial domain (SD) characteristics by incorporating the cross-correlation between the beams in different antenna panels, the measured reference signal received power (RSRP), and the beam usage statistics to dynamically adjust beamforming decisions. As a result, the spectral efficiency is improved and end-to-end latency is reduced. The numerical results demonstrate an increase in throughput of up to 16% and a reduction in latency by factors 3-7x compared to baseline (legacy beam management).
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