Federated Learning for Beam Management in 5G and Beyond: A Collaborative AI/ML Approach

Building on the standardization efforts in 5G-Advanced for artificial intelligence (AI) and machine learning (ML)-based beam management (BM), two primary types of beam prediction models have been considered for deployment: the user equipment (UE)-sided model which is trained and deployed on the UE, and the network (NW)-sided model which is trained and deployed at the base station (BS). However, the NW-sided model suffers from significant communication overhead, while the UE-sided model is constrained by its reliance on training with data of limited distribution. To address these challenges, this paper proposes a federated learning (FL)-based approach as an alternative to centralized deployment at either the BS or the UE. The proposed FL-based model utilizes locally collected physical layer reference signal received power (L1-RSRP) data from each UEs to train local models, with only the model parameters transmitted to the BS for aggregation. This approach achieves beam prediction performance comparable to that of the NW-sided model while reducing communication overhead by a factor of 4.3 and outperforming the UE-sided model by 11.2%.