Artificial intelligence (AI) plays an important role in the dynamic landscape of wireless communications, solving challenges unattainable by traditional approaches. This paper discusses the evolution of wireless AI, emphasizing the transition from isolated task-specific models to more generalizable and adaptable AI models inspired by recent successes in large language models (LLMs) and computer vision. To overcome task-specific AI strategies in wireless networks, we propose a unified wireless physical-layer foundation model (WPFM). Challenges include the design of effective pre-training tasks, support for embedding heterogeneous time series and human-understandable interaction. The paper presents a strategic framework, focusing on embedding wireless time series, self-supervised pre-training, and semantic representation learning. The proposed WPFM aims to understand and describe diverse wireless signals, allowing human interactivity with wireless networks. The paper concludes by outlining next research steps for WPFMs, including the integration with LLMs.
@article{arxiv.2403.12065,
title = {Towards a Wireless Physical-Layer Foundation Model: Challenges and Strategies},
author = {Jaron Fontaine and Adnan Shahid and Eli De Poorter},
journal= {arXiv preprint arXiv:2403.12065},
year = {2025}
}
Comments
This paper is accepted and part of the WS33 IEEE ICC 2024 1st Workshop on The Impact of Large Language Models on 6G Networks proceedings