Direction-of-arrival (DoA) estimation is a fundamental array processing task that has benefited substantially from deep learning. Deploying such methods across distributed edge devices introduces privacy and communication constraints that federated learning (FL) can address. Yet, standard FL algorithms treat DoA as a generic classification problem, ignoring the underlying physics of the array manifold. To address this, we propose a physics-informed FL framework for DoA estimation that incorporates steering-vector geometry directly into the local training objective via a manifold-aware regularizer. Unlike existing FL baselines, the regularizer in our framework penalizes discrepancies in steering space rather than label space, exploiting the known geometric structure of the array manifold. We provide theoretical convergence guarantees for our framework, showing convergence to a stationary point. Simulation results confirm that our physics informed approach outperforms multiple FL baseline approaches across iid and non-iid data conditions.
Efficient optimization and control of photonic computing and communication systems increasingly rely on accurate surrogate models/digital twins. While data-driven models may achieve faster inference than traditional physics-based methods, they typically suffer from poor training…
Distributed learning systems typically assume that local data is already available at clients with fixed quality, while in practice, data is sensed through imperfect physical processes whose quality depends on modality, resolution, sensing power, and sample size. We model sensing…
Active beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) enables hybrid transmitting and reflecting mode to achieve effective signal amplification and full-space coverage, thus providing a promising solution for blockage-aware uplink offloading in heterogeneous mobile…
The direction-of-arrival (DOA) estimation problem using one-bit quantized magnitude-only measurements is studied, where magnitude-only measurements offer robustness against phase errors, thereby avoiding the need for array calibration, while one-bit quantization significantly red…
Future wireless networks are expected to support networked artificial intelligence (AI) services, where multiple devices transmit learned features to an edge server for distributed inference. This setting calls for task-oriented physical-layer optimization, where wireless transmi…