首个无线通信多任务基础模型，用海量信道数据预训练提升性能
WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication
- 基于大规模信道数据无监督预训练，提取通用时空特征表示
- 参数量约8000万，在多任务上表现优于传统方法和小型模型
- 适合需要集成通信与感知的智能无线系统研发人员使用
本文提出WirelessGPT，首个专为无线通信与感知设计的多任务基础模型。该模型利用大规模无线信道数据进行无监督预训练，捕捉复杂的时空依赖关系，获得具有任务无关性的通用信道表征。凭借统一的表示架构，WirelessGPT可无缝适配多种下游任务，仅需少量微调即可实现高性能。通过融合通信与感知功能，克服了传统专用模型的局限性，为集成传感与通信（ISAC）提供高效可扩展的解决方案。初始参数规模约为8000万，在无需大量标注数据的情况下显著优于传统方法和小型AI模型，是首个支持跨领域多样化任务的基础模型，确立了新一代多任务无线系统的新基准。
原文摘要 · Abstract (English)
This paper introduces WirelessGPT, a pioneering foundation model specifically designed for multi-task learning in wireless communication and sensing. Specifically, WirelessGPT leverages large-scale wireless channel datasets for unsupervised pretraining and extracting universal channel representations, which captures complex spatiotemporal dependencies. In fact,this task-agnostic design adapts WirelessGPT seamlessly to a wide range of downstream tasks, using a unified representation with minimal fine-tuning. By unifying communication and sensing functionalities, WirelessGPT addresses the limitations of task-specific models, offering a scalable and efficient solution for integrated sensing and communication (ISAC). With an initial parameter size of around 80 million, WirelessGPT demonstrates significant improvements over conventional methods and smaller AI models, reducing reliance on large-scale labeled data. As the first foundation model capable of supporting diverse tasks across different domains, WirelessGPT establishes a new benchmark, paving the way for future advancements in multi-task wireless systems.
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