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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.06877 |
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| _version_ | 1866917918475812864 |
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| author | Yang, Tingting Zhang, Ping Zheng, Mengfan Shi, Yuxuan Jing, Liwen Huang, Jianbo Li, Nan |
| author_facet | Yang, Tingting Zhang, Ping Zheng, Mengfan Shi, Yuxuan Jing, Liwen Huang, Jianbo Li, Nan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_06877 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication Yang, Tingting Zhang, Ping Zheng, Mengfan Shi, Yuxuan Jing, Liwen Huang, Jianbo Li, Nan Machine Learning 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. |
| title | WirelessGPT: A Generative Pre-trained Multi-task Learning Framework for Wireless Communication |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.06877 |