图神经网络多智能体协调用于分布式接入点选择的细胞自由大规模MIMO
Graph-Neural Multi-Agent Coordination for Distributed Access-Point Selection in Cell-Free Massive MIMO
AI总结：
本文提出APS-GNN，一种基于图神经网络的多智能体协调框架，用于优化细胞自由大规模MIMO中的接入点选择，实现高效频谱效率与低能耗。
摘要：
细胞自由大规模MIMO（CFmMIMO）系统需要可扩展和可靠的分布式协调机制，以在严格的通信和时延约束下运行。一个核心挑战是接入点选择（APS）问题，该问题旨在确定每个用户设备（UE）的接入点（AP）子集，以满足UE的频谱效率（SE）要求，同时最小化网络功率消耗。我们引入了APS-GNN，一种可扩展的分布式多智能体学习框架，将APS分解为在单个AP-UE连接粒度上操作的智能体。智能体通过本地观察交换经由一种新的图神经网络（GNN）架构进行协调，并共享参数以重用其知识和经验。APS-GNN采用约束强化学习方法，为智能体提供明确的APS冲突目标的可观察性，将SE满足视为成本，功率减少视为奖励。这两种信号都定义在本地，促进了有效信用分配和大规模网络中的可扩展协调。为进一步提高训练稳定性和探索效率，策略通过监督模仿学习从启发式APS基线初始化。我们开发了一个真实的CFmMIMO仿真器，并证明在不同评估场景中，APS-GNN在满足目标SE的同时，比启发式和集中多智能体强化学习（MARL）基线激活的接入点数量少50-70%。此外，由于其完全并行和分布式执行，APS-GNN的推理延迟比集中MARL方法低一个到两个数量级。这些结果确立了APS-GNN作为大规模CFmMIMO网络中APS的实用且可扩展的解决方案。
英文摘要：
Cell-free massive MIMO (CFmMIMO) systems require scalable and reliable distributed coordination mechanisms to operate under stringent communication and latency constraints. A central challenge is the Access Point Selection (APS) problem, which seeks to determine the subset of serving Access Points (APs) for each User Equipment (UE) that can satisfy UEs' Spectral Efficiency (SE) requirements while minimizing network power consumption. We introduce APS-GNN, a scalable distributed multi-agent learning framework that decomposes APS into agents operating at the granularity of individual AP-UE connections. Agents coordinate via local observation exchange over a novel Graph Neural Network (GNN) architecture and share parameters to reuse their knowledge and experience. APS-GNN adopts a constrained reinforcement learning approach to provide agents with explicit observability of APS' conflicting objectives, treating SE satisfaction as a cost and power reduction as a reward. Both signals are defined locally, facilitating effective credit assignment and scalable coordination in large networks. To further improve training stability and exploration efficiency, the policy is initialized via supervised imitation learning from a heuristic APS baseline. We develop a realistic CFmMIMO simulator and demonstrate that APS-GNN delivers the target SE while activating 50-70% fewer APs than heuristic and centralized Multi-agent Reinforcement Learning (MARL) baselines in different evaluation scenarios. Moreover, APS-GNN achieves one to two orders of magnitude lower inference latency than centralized MARL approaches due to its fully parallel and distributed execution. These results establish APS-GNN as a practical and scalable solution for APS in large-scale CFmMIMO networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
