Large Wireless Foundation Models: Stronger over Bigger

TL;DR

This paper introduces large wireless foundation models (LWFMs) to enhance the physical layer of wireless systems, emphasizing generalization and robustness across diverse scenarios, and proposes frameworks and principles for their development.

Contribution

It presents a novel framework for large wireless foundation models, including two paradigms and design principles tailored to wireless constraints, advancing beyond traditional task-specific models.

Findings

Case studies demonstrate LWFM advantages in wireless systems

Roadmaps for implementing LWFMs are proposed

Multidimensional analysis clarifies the scope of 'large' in LWFMs

Abstract

AI-communication integration is widely regarded as a core enabling technology for 6G. Most existing AI-based physical-layer designs rely on task-specific models that are separately tailored to individual modules, resulting in poor generalization. In contrast, communication systems are inherently general-purpose and should support broad applicability and robustness across diverse scenarios. Foundation models offer a promising solution through strong reasoning and generalization, yet wireless-system constraints hinder a direct transfer of large language model (LLM)-style success to the wireless domain. Therefore, we introduce the concept of large wireless foundation models (LWFMs) and present a novel framework for empowering the physical layer with foundation models under wireless constraints. Specifically, we propose two paradigms for realizing LWFMs, including leveraging existing general-purpose foundation models and building novel wireless foundation models. Based on recent progress, we distill two roadmaps for each paradigm and formulate design principles under wireless constraints. We further provide case studies of LWFM-empowered wireless systems to intuitively validate their advantages. Finally, we characterize the notion of "large" in LWFMs through a multidimensional analysis of existing work and outline promising directions for future research.