Federated Learning–Enabled Collaborative Beamforming for NGSO Direct-to-Vehicle Systems

Non-geostationary satellite orbit (NGSO) systems are expected to play a central role in enabling direct satellite-to-vehicle (DS2V) connectivity. However, dense spectrum reuse and the coexistence of multiple NGSO constellations introduce severe co-frequency interference (CFI), which is further exacerbated by fast satellite motion and short channel coherence times. In this paper, we propose a federated learning (FL)–enabled collaborative receive beamforming framework for DS2Vs (FedSatBF). In FedSatBF, a serving satellite aggregates locally trained beamforming models from a subset of vehicular user terminals (VUTs) via federated averaging and broadcasts a global model that can be directly applied by both participating and non-participating VUTs under similar CFI conditions. An attention-based beamforming architecture is adopted as the base model for training local models at each VUT, with estimation-free sample covariance matrices as input, thereby eliminating the need for explicit estimation of the interference channel or the direction of arrival. The local model is trained via a reward-based learning objective in which the loss is the negative measured post-beamforming signal-to-interference-plus-noise ratio (SINR), so that no ground-truth weight, no synthetic geometry-derived label, and no interferer’ channel state information (CSI) is provided or needed during training. The proposed framework is explicitly designed to operate within the limited channel coherence time imposed by NGSO mobility. We investigate the impact of key FL hyperparameters, including per-coherent block communication rounds, per-round training data, per-VUT local epochs, VUT participation, and geographical VUT clustering, on convergence speed, interference nulling capability, and computational efficiency. Simulation results demonstrate that FedSatBF achieves near-optimal signal-to-interference-plus-noise ratio (SINR) performance, within 0.1 [dB] of an optimal zero-forcing beamformer (ZFBF) used here as a perfect-CSI oracle upper bound, and significantly outperforms conventional maximum-ratio combining (MRC), all while using only a moderate FL hyperparameter setting. Moreover, the global model exhibits strong generalization to non-participating VUTs and remains effective under reduced settings, highlighting the practicality of FL-based collaborative beamforming for the next generation of DS2V systems.