WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication
It addresses the limitations of task-specific models for integrated sensing and communication systems, offering a scalable solution, though it appears incremental as it builds on existing foundation model concepts applied to a new domain.
This paper tackles the problem of task-specific models in wireless communication and sensing by introducing WirelessGPT, a foundation model for multi-task learning, which demonstrates significant improvements over conventional methods and reduces reliance on labeled data with an 80 million parameter size.
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.