Low-Overhead Distributed Power Control for Cell-Free Massive MIMO With Limited Fronthaul Capacity
Cell-free massive multiple-input multiple-output (CF mMIMO) requires effective power control, but centralized processing relies on global instantaneous channel state information (CSI) and creates heavy fronthaul load. This letter focuses on low-overhead distributed power control under limited fronthaul capacity. We propose an information bottleneck (IB)-based policy that exchanges compact latent messages instead of raw local observations, and we train it using cluster-based federated learning to keep raw CSI local. The IB penalty provides an explicit information-rate proxy for online coordination, while robustness under imperfect CSI and data locality are evaluated as supporting effects rather than formal guarantees. Simulations show that the proposed method approaches a centralized benchmark with much lower effective overhead and stable behavior under channel estimation errors.