Towards a Wireless Physical-Layer Foundation Model: Challenges and Strategies
arXiv:2403.12065 · doi:10.1109/ICCWorkshops59551.2024.10615509
Abstract
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.
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
References in corpus (8)
- VisualBERT: A Simple and Performant Baseline for Vision and Language
- Five Facets of 6G: Research Challenges and Opportunities
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models
- Artificial Intelligence for 6G Networks: Technology Advancement and Standardization
- Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
- Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly
- A K-variate Time Series Is Worth K Words: Evolution of the Vanilla Transformer Architecture for Long-term Multivariate Time Series Forecasting
- Large Language Models for Telecom: Forthcoming Impact on the Industry