AI 中文总结
本文综述无线物理层基础模型（WPFMs）的架构、学习范式等，提出五维度分类体系，分析其在多领域的应用部署可行性，探讨模型压缩与挑战，为相关研究提供参考。
AI 中文摘要
基础模型是指在大量未标注数据上预训练并可适配多种下游任务的大型神经网络，已重塑自然语言处理与计算机视觉领域，目前正被探索应用于无线物理层。无线物理层基础模型（Wireless Physical-Layer Foundation Models, WPFMs）旨在学习信道状态信息（CSI）、同相正交（IQ）采样、频谱图等信号的可迁移表示，使单一预训练主干网络能在使用有限任务特定数据的情况下，支持从信道估计、预测到定位与感知等各类任务。本文对WPFMs开展专项综述，覆盖从学习设计到实际部署的全流程：首先确立理论背景，包括适用于无线信号的神经架构、掩码建模、对比学习与生成式预训练三类自监督预训练范式，以及将预训练模型适配到下游任务的微调策略；接着提出一个分类体系，从架构家族、输入模态与分词、预训练目标、模型规模与部署目标、泛化能力五个维度对现有模型进行组织；在此基础上，综述其在电信、定位、感知领域的应用，并针对每个领域，通过映射模型规模到代表性无线硬件的内存、计算与延迟预算，分析部署可行性；最后讨论模型压缩与高效部署，总结跨领域挑战，梳理开放研究方向。本文旨在提供一份将预训练、架构、微调与无线系统实际约束相连接的参考资料。
英文摘要
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.
