Large AI Models for Wireless Physical Layer

TL;DR

This paper reviews how large AI models are revolutionizing wireless physical layer technologies by enhancing performance, adaptability, and multitask capabilities, and discusses future research directions for their development.

Contribution

It classifies LAM-based approaches into leveraging pre-trained models and developing native models, providing a comprehensive analysis of their frameworks and applications in wireless communications.

Findings

LAMs improve performance across wireless scenarios

Pre-trained and native LAM strategies are effective

Future directions include efficient architectures and interpretability

Abstract

Large artificial intelligence models (LAMs) are transforming wireless physical layer technologies through their robust generalization, multitask processing, and multimodal capabilities. This article reviews recent advancements in applying LAMs to physical layer communications, addressing obstacles of conventional AI-based approaches. LAM-based solutions are classified into two strategies: leveraging pre-trained LAMs and developing native LAMs designed specifically for physical layer tasks. The motivations and key frameworks of these approaches are comprehensively examined through multiple use cases. Both strategies significantly improve performance and adaptability across diverse wireless scenarios. Future research directions, including efficient architectures, interpretability, standardized datasets, and collaboration between large and small models, are proposed to advance LAM-based physical layer solutions for next-generation communication systems.