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

2026-08-04

AI Summary

This work addresses the challenge of communication-efficient, serverless multi-cell massive MIMO coordination in open and disaggregated 6G radio access networks. To jointly optimize user scheduling, power allocation, beamforming, and QoS provisioning without centralized control or global data sharing, the paper proposes a serverless federated multi-agent reinforcement learning framework. Each base station independently executes a local policy network and exchanges only sparse critic parameters via an interference-aware graph using an event-triggered mechanism. The approach innovatively integrates adaptive hierarchical Top-k sparsification, error-feedback compensation, and interference-aware fusion to ensure convergence while drastically reducing communication overhead. Experimental results demonstrate significant improvements over baseline methods in system throughput, per-user rates, average SINR, and QoS satisfaction under strong interference reuse scenarios, achieving a 76% reduction in critic communication cost.

Abstract

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.

Problem

• Massive-MIMO
• Federated Learning
• Resource Control
• 6G RAN
• Communication Efficiency

Innovation

• serverless federated learning
• massive-MIMO resource control
• communication-efficient critic exchange
• interference-aware fusion
• sparse parameter sharing