Federated Learning for Precoding Design in Cell-Free Massive MIMO Systems

Cell-free massive MIMO precoding leverages the large number of antennas and dense access point (AP) deployment to concurrently serve multiple users in a wide coverage area, thus promising efficient interference mitigation and significant network capacity enhancement. Most existing centralized precoding methods for cell-free massive MIMO systems, whether optimization-based or learning-based, suffer from high computational complexity or expensive communication overhead, introducing additional latency to the network in practical applications. To address the above issues, in this paper we propose two decentralized precoding methods based on the horizontal federated learning (HFL) and vertical federated learning (VFL) frameworks, respectively. In the HFL-based precoding method, we design a low-cost residual global channel state information (CSI) feature acquisition mechanism called RFAM at each AP to create local datasets. RFAM eliminates the need for point-to-point CSI exchange between APs, resulting in reduced communication overhead. In the VFL-based precoding method, each AP utilizes its own CSI for precoding scheme design, thereby eliminating the communication overhead associated with obtaining CSI from other APs. Furthermore, the computational complexity is considerably reduced due to the low overhead of model inference. Experimental results conducted in various channel environments show that the proposed HFL-based method achieves faster convergence rate and outperforms traditional decentralized methods in terms of sum-rate performance. The results also show that the proposed VFL-based method achieves similar sum-rate performance to centralized schemes.