发表机构
University of Calgary; University of Campinas（卡尔加里大学; 坎皮纳斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述针对物理层应用系统分析无线基础模型（WFMs），梳理其设计组件、任务分类及现有研究的预训练、适配与评估情况，指出当前研究的碎片化问题，明确了未来的开放研究方向。
AI 中文摘要
无线基础模型（Wireless Foundation Models, WFMs）已成为一种有前景的方法，可从大规模无线数据中学习可复用的表示并将其适配到下游任务。然而，快速增长的文献在模态、预训练目标、架构、适配策略和评估协议方面仍呈碎片化，难以评估向广泛可迁移模型推进的进展。本综述针对物理层应用对WFMs进行系统分析：首先介绍WFMs的主要设计组件，包括预训练、骨干架构和下游适配；随后将文献划分为五大物理层任务族：信号识别与解调、信道表示学习、射频（RF）感知与定位、波束管理、频谱感知与监测，同时单独分析多任务物理层（PHY）模型。在上述类别中，分析现有模型的预训练、适配与评估方式，重点关注下游任务多样性，以及分布内、部分分布偏移和分布外迁移的区别。分析表明，当前WFMs为可复用无线表示提供了越来越多的证据，但该证据在不同任务族和评估设置间差异显著。数据集、模态、架构、预训练目标、适配协议和分布偏移的差异，使得难以确定哪些设计选择驱动了迁移与泛化。最后，确定了提升数据可用性、评估严谨性、泛化能力、高效适配及实际部署的开放方向，为理解当前WFM格局及开发未来物理层无线系统更具可复用性的基础模型所需的要求提供了统一框架。
英文摘要
Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations from large-scale wireless data and adapting them to downstream tasks. However, the rapidly growing literature remains fragmented across modalities, pretraining objectives, architectures, adaptation strategies, and evaluation protocols, making it difficult to assess progress toward broadly transferable models. This survey provides a systematic analysis of WFMs for physical-layer applications. We first introduce the main WFM design components, including pretraining, backbone architectures, and downstream adaptation. We then organize the literature into five physical-layer task families: signal recognition and demodulation, channel representation learning, RF sensing and localization, beam management, and spectrum sensing and monitoring, while separately examining multi-task PHY models. Across these categories, we analyze how existing models are pretrained, adapted, and evaluated, with particular attention to downstream task diversity and the distinction between in-distribution, partial-shift, and out-of-distribution transfer. Our analysis shows that current WFMs provide increasing evidence of reusable wireless representations, but this evidence varies considerably across task families and evaluation settings. Differences in datasets, modalities, architectures, pretraining objectives, adaptation protocols, and distribution shifts make it difficult to determine which design choices drive transfer and generalization. We conclude by identifying open directions for improving data availability, evaluation rigor, generalization, efficient adaptation, and real-world deployment, providing a unified framework for understanding the current WFM landscape and the requirements for developing more reusable foundation models for future physical-layer wireless systems.
