Federated Learning Based Near-Field Channel Estimation for XL-MIMO Communications

Extremely large-scale massive MIMO (XL-MIMO) is foreseen as a promising technology to achieve ultra-low latency, high data transmission speed, and extremely low error rate in future 6 G networks. The increases in antenna apertures and the use of higher frequencies (millimeter-wave and subTHz) in XL-MIMO significantly extend the Rayleigh distance, thereby enhancing the prevalence of near-field (NF) communications. Unfortunately, existing far-field channel models struggle to accurately capture both line-of-sight (LoS) and non-line-ofsight (NLoS) propagation paths in the presence of NF effects. Moreover, the huge number of antenna elements greatly increases computational demands and complicates channel estimation tasks. In this paper, we propose a federated learning (FL)based near-field channel estimation (NFCE) framework for mixed LoS/NLoS environments. In this framework, we employ a federated deep residual learning (FDRL)-based convolutional neural network (CNN) architecture, where only a subset of local devices participates in the distributed training process. By utilizing the complex convolution and a few residual blocks, this framework reduces the effect of signal noise while minimizing communication overhead and computational complexity. Simulation results demonstrate that our proposed framework significantly improves NFCE performance in terms of normalized mean square error and bit error rate compared to selected benchmark schemes.