Wireless Physical-Layer Foundation Models: Architectures, Learning Paradigms, Applications, and Deployment

Abstract

Foundation models, i.e., large neural networks pretrained on broad unlabeled data and adapted to many downstream tasks, have reshaped natural language processing and computer vision and are now being explored for the wireless physical layer. Wireless Physical-Layer Foundation Models (WPFMs) aim to learn transferable representations of signals such as channel state information (CSI), in-phase and quadrature (IQ) samples, and spectrograms so that a single pretrained backbone can support tasks ranging from channel estimation and prediction to localization and sensing while using limited task-specific data. This paper provides a dedicated review of WPFMs from learning design to practical deployment. We first establish the theoretical background, covering the neural architectures used for wireless signals, the self-supervised pretraining paradigms of masked modeling, contrastive learning, and generative pretraining, and the fine-tuning strategies that adapt pretrained models to downstream tasks. We then introduce a taxonomy that organizes existing models along five dimensions: architecture family, input modality and tokenization, pretraining objective, model scale and deployment target, and generalization capability. Building on this basis, we review applications across telecommunications, localization, and sensing, and, for each domain, analyze deployment feasibility by mapping model size to the memory, compute, and latency budgets of representative wireless hardware. Finally, we discuss model compression and efficient deployment, summarize the cross-cutting challenges, and outline open research directions. Our goal is to provide a reference that connects pretraining, architecture, and fine-tuning with the practical constraints of wireless systems.