用智能反射面与多智能体强化学习，提升6G工业网络的连接质量与能效。
Multi-Agent DRL for QoS and Energy Optimization in RIS-Enabled Open-RAN Industrial 6G TN/NTN Networks
- 通过多智能体强化学习优化资源分配，解决高维耦合决策难题。
- 数据速率最高提升75%，时延降低25%，能耗减少16%。
- 适合研究6G工业物联网、智能反射面与边缘计算的从业者。
工业6G网络需在动态且易受遮挡的环境中实现超可靠、低延迟和高能效的连接，传统地面部署常难以保障稳定覆盖。本文提出一种基于可重构智能表面（RIS）的Open-RAN框架，集成地面基站、高空平台（HAP）与机载RIS无人机，为密集工业物联网设备增强连通性。由于决策变量维度高且强耦合，传统优化方法计算不可行。为此，将数据速率、时延与能耗联合优化问题建模为分布式部分可观马尔可夫决策过程（Dec-POMDP），并采用多智能体深度强化学习求解。仿真结果表明，相比最先进的学习基与非RIS基准方案，数据速率最高提升75%，时延降低25%，能耗减少16%，验证了RIS辅助的Open-RAN智能在工业6G网络中的有效性。
原文摘要 · Abstract (English)
Industrial 6G networks require ultra-reliable, low-latency, and energy-efficient connectivity in dynamic and blockage-prone environments, where conventional terrestrial deployments often fail to ensure stable coverage. Hence, in this paper, we propose a RIS-enabled Open-RAN framework for integrated terrestrial/non-terrestrial (TN/NTN) industrial 6G networks, in which UAVs-mounted reconfigurable intelligent surfaces (RISs) cooperate with ground radio units and a high-altitude platform (HAP) to enhance connectivity for dense industrial IoT devices. Owing to the high dimensionality and strong coupling among decision variables, conventional optimization techniques become computationally intractable. To overcome this limitation, the joint optimization problem of data rates, latency, and energy consumptions is formulated as a decentralized partially observable Markov decision process (Dec-POMDP) and solved using a multi-agent deep reinforcement learning framework. Simulation results show improvements of up to 75\% in data rate, 25\% latency reduction, and 16\% energy savings compared with state-of-the-art learning-based and non-RIS baselines, demonstrating the effectiveness of RIS-assisted Open-RAN intelligence for industrial 6G networks.
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