用强化学习让工厂6G基站自动学功率和信号控制，省8倍通信开销。
Learning Power Control Protocol for In-Factory 6G Subnetworks
- 用多智能体强化学习自主学习信号与功率控制策略。
- 信号开销降低8倍，缓冲区溢出率仅比理想情况高5%。
- 适合高密度工厂6G场景，无需完美信道信息。
工厂内6G子网需满足短距离通信的严苛需求。在工厂场景中，有效功率控制对缓解高密度子网带来的干扰至关重要。现有方法主要关注数据平面，常忽略信令开销；且多基于网络中心视角，假设中心控制器可获取完整实时的信道状态信息（CSI）。本文提出一种新型多智能体强化学习（MARL）框架，使接入点能在工厂子网环境中自主学习信令与功率控制协议。将问题建模为部分可观测马尔可夫决策过程（POMDP），并采用多智能体近端策略优化（MAPPO），取得显著优势。仿真结果表明，该学习方法将信令开销降低8倍，同时缓冲区溢出率仅比理想‘天眼’方案高出5%。
原文摘要 · Abstract (English)
In-X Subnetworks are envisioned to meet the stringent demands of short-range communication in diverse 6G use cases. In the context of In-Factory scenarios, effective power control is critical to mitigating the impact of interference resulting from potentially high subnetwork density. Existing approaches to power control in this domain have predominantly emphasized the data plane, often overlooking the impact of signaling overhead. Furthermore, prior work has typically adopted a network-centric perspective, relying on the assumption of complete and up-to-date channel state information (CSI) being readily available at the central controller. This paper introduces a novel multi-agent reinforcement learning (MARL) framework designed to enable access points to autonomously learn both signaling and power control protocols in an In-Factory Subnetwork environment. By formulating the problem as a partially observable Markov decision process (POMDP) and leveraging multi-agent proximal policy optimization (MAPPO), the proposed approach achieves significant advantages. The simulation results demonstrate that the learning-based method reduces signaling overhead by a factor of 8 while maintaining a buffer flush rate that lags the ideal "Genie" approach by only 5%.
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