6G网络下无需中心服务器的智能资源分配方法
FedCritic: Serverless Federated Critic Learning-based Resource Allocation for Multi-Cell OFDMA in 6G
- 基于去中心化强化学习，通过消息传播聚合各小区策略
- 在干扰密集场景中提升平均SINR与边缘用户速率
- 适合大规模分布式通信系统，降低协调开销
在第六代（6G）超密集网络中，频谱复用强度加剧了小区间干扰（ICI），导致多小区正交频分多址（OFDMA）调度与功率控制在邻近小区间强耦合。本文研究分布式下行链路资源管理——联合子载波调度与功率分配，在干扰耦合及长期用户服务质量（QoS）最小速率约束条件下，利用虚拟队列欠额权重实现长期QoS保障。提出FedCritic：一种无服务器的联邦多智能体演员-评论家框架，支持去中心化执行。不同于需中心化评论家学习与联合轨迹聚合的集中训练、去中心化执行（CTDE）方法，FedCritic通过干扰图上的轻量级基于消息传播的参数平均实现评论家联邦化，无需中心协调即可保持稳定值估计，同时保持策略本地性。仿真结果表明，在干扰强烈的重用-1场景下，FedCritic相比非协同和CTDE基线，显著提升平均信干噪比（SINR）与小区边缘速率，提高网络总和速率与公平性，并实现更稳定的训练过程，协调开销更低。
原文摘要 · Abstract (English)
In sixth-generation (6G) ultra-dense networks, aggressive frequency reuse amplifies inter-cell interference (ICI), making multi-cell orthogonal frequency-division multiple access (OFDMA) scheduling and power control strongly coupled across neighboring cells. We study distributed downlink resource management -- joint subcarrier scheduling and power allocation -- under interference coupling and long-term per-user quality-of-service (QoS) minimum-rate constraints. By using virtual-queue deficit weights to enforce long-term QoS, we develop FedCritic, a serverless federated multi-agent actor-critic framework with decentralized execution. Unlike centralized training with decentralized execution (CTDE) approaches that require centralized critic learning and joint trajectory aggregation, FedCritic federates the critic through lightweight gossip-based parameter averaging over the interference graph, enabling stable value estimation without a central coordinator while keeping policies local. Simulations in an interference-rich reuse-1 setting show that FedCritic improves mean signal-to-interference-plus-noise ratio (SINR) and cell-edge rate, increases network-wide average sum-rate and fairness relative to non-coordinated and CTDE baselines, and achieves more stable training with lower coordination overhead.
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