Recent advances in Wireless Physical Layer Foundation Models (WPFMs) promise a new paradigm of universal Radio Frequency (RF) representations. However, these models inherit critical limitations found in deep learning such as the lack of explainability, robustness, adaptability, and verifiable compliance with physical and regulatory constraints. In addition, the vision for an AI-native 6G network demands a level of intelligence that is deeply embedded into the systems and is trustworthy. In this vision paper, we argue that the neuro-symbolic paradigm, which integrates data-driven neural networks with rule- and logic-based symbolic reasoning, is essential for bridging this gap. We envision a novel Neuro-Symbolic framework that integrates universal RF embeddings with symbolic knowledge graphs and differentiable logic layers. This hybrid approach enables models to learn from large datasets while reasoning over explicit domain knowledge, enabling trustworthy, generalizable, and efficient wireless AI that can meet the demands of future networks.

翻译：无线物理层基础模型（WPFMs）的最新进展预示着通用射频（RF）表征的新范式。然而，这些模型继承了深度学习的若干关键局限，如缺乏可解释性、鲁棒性、适应性，以及难以验证是否符合物理与监管约束。此外，面向AI原生的6G网络愿景要求一种深度嵌入系统且可信赖的智能水平。在本愿景论文中，我们主张神经符号范式——即整合数据驱动的神经网络与基于规则和逻辑的符号推理——对于弥合这一差距至关重要。我们构想一种新型神经符号框架，该框架将通用射频嵌入与符号知识图谱及可微逻辑层相结合。这种混合方法使模型能够从大规模数据集中学习，同时基于显式领域知识进行推理，从而实现可信、可泛化且高效的无线人工智能，以满足未来网络的需求。