Reasoning Meets Representation: Envisioning Neuro-Symbolic Wireless Foundation Models

TL;DR

This paper proposes a neuro-symbolic framework for Wireless Physical Layer Foundation Models, combining neural networks with symbolic reasoning to enhance explainability, robustness, and trustworthiness for future AI-native wireless networks.

Contribution

It introduces a novel hybrid neuro-symbolic framework that integrates RF embeddings with symbolic knowledge graphs and differentiable logic layers for wireless AI.

Findings

Enables models to learn from data and reason over domain knowledge.

Improves explainability and robustness of wireless AI models.

Supports trustworthy and generalizable wireless network applications.

Abstract

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.