FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANs

核心亮点：这项研究让6G基站像一群能互相“传纸条”的智能体，在不共享全部信息的情况下，用极低的通信成本实现全网信号协调，把网络吞吐量做到最优。通俗解读：想象一个大型商场里几十个Wi-Fi路由器，以前每个路由器只顾自己，信号互相干扰。现在研究人员给每个路由器装了个“小脑”（本地AI），它们不用把所有数据都传到中央服务器，只需偶尔交换一点“心得”（关键的批评参数），就能学会默契配合，避免互相干扰。这就像一群厨师各自做菜，但每隔几分钟只喊一句“我这边火大，你那边少放盐”，就能保证整桌菜味道协调，还省了来回跑腿的力气。实际影响：未来用6G手机打游戏、看8K视频或开全息会议时，网速会更稳定，尤其在人群密集的体育场或地铁站，不会出现“信号满格却刷不动”的尴尬。同时运营商能省下大量基站间通信的能耗和带宽成本，这些节省最终可能转化为更便宜的流量套餐，让普通人用上更高速、更稳定、更便宜的移动网络。

This paper proposes FedCritic-MIMO, a communication-efficient serverless federated multi-agent reinforcement learning framework for AI-native resource control across independently deployable cell-level controllers in open and disaggregated 6G RANs. Controllers share no trainer, retain local actors and personalized critic components, and exchange only compatible shared critic parameters. FedCritic-MIMO targets reuse-$1$ multi-cell massive-MIMO OFDMA deployments, where RAN controllers jointly manage user scheduling, per-stream power allocation, beamforming, interference, and long-term QoS with limited inter-controller signaling. Each base station locally executes its actor without centralized training or actor federation, while critic knowledge is exchanged peer-to-peer over an interference-aware graph. It enables this collaboration through wireless-aware event triggering, adaptive layer-wise top-$k$ sparse critic exchange with error feedback, and balanced interference-aware fusion. We establish conditional finite-time stationarity and consensus guarantees for the balanced, compressed peer-to-peer critic recursion under a fixed-policy, frozen-target critic-regression model. In strongly interference-coupled reuse-$1$ simulations, FedCritic-MIMO achieves the best performance-communication tradeoff among heuristic, independent-learning, centralized-training, and communication-ablation baselines. It achieves the highest held-out throughput, improves user-rate distribution and mean SINR, increases QoS satisfaction, and attains the lowest interference cost per delivered bit among learning baselines. It reduces critic-communication overhead by $76\%$ relative to uncompressed distributed critic exchange. These results demonstrate that serverless exchange of compatible shared critic parameters can coordinate RAN controllers without centralized trajectory collection or parameter-server aggregation.