Addressing the Curse of Scenario and Task Generalization in AI-6G: A Multi-Modal Paradigm
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909568820314112 |
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| author | Jiao, Tianyu Xiao, Zhuoran Xu, Yin Ye, Chenhui Huang, Yihang Chen, Zhiyong Cai, Liyu Chang, Jiang He, Dazhi Guan, Yunfeng Liu, Guangyi Zhang, Wenjun |
| author_facet | Jiao, Tianyu Xiao, Zhuoran Xu, Yin Ye, Chenhui Huang, Yihang Chen, Zhiyong Cai, Liyu Chang, Jiang He, Dazhi Guan, Yunfeng Liu, Guangyi Zhang, Wenjun |
| contents | Existing works on machine learning (ML)-empowered wireless communication primarily focus on monolithic scenarios and single tasks. However, with the blooming growth of communication task classes coupled with various task requirements in future 6G systems, this working pattern is obviously unsustainable. Therefore, identifying a groundbreaking paradigm that enables a universal model to solve multiple tasks in the physical layer within diverse scenarios is crucial for future system evolution. This paper aims to fundamentally address the curse of ML model generalization across diverse scenarios and tasks by unleashing multi-modal feature integration capabilities in future systems. Given the universality of electromagnetic propagation theory, the communication process is determined by the scattering environment, which can be more comprehensively characterized by cross-modal perception, thus providing sufficient information for all communication tasks across varied environments. This fact motivates us to propose a transformative two-stage multi-modal pre-training and downstream task adaptation paradigm... |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Addressing the Curse of Scenario and Task Generalization in AI-6G: A Multi-Modal Paradigm Jiao, Tianyu Xiao, Zhuoran Xu, Yin Ye, Chenhui Huang, Yihang Chen, Zhiyong Cai, Liyu Chang, Jiang He, Dazhi Guan, Yunfeng Liu, Guangyi Zhang, Wenjun Signal Processing Existing works on machine learning (ML)-empowered wireless communication primarily focus on monolithic scenarios and single tasks. However, with the blooming growth of communication task classes coupled with various task requirements in future 6G systems, this working pattern is obviously unsustainable. Therefore, identifying a groundbreaking paradigm that enables a universal model to solve multiple tasks in the physical layer within diverse scenarios is crucial for future system evolution. This paper aims to fundamentally address the curse of ML model generalization across diverse scenarios and tasks by unleashing multi-modal feature integration capabilities in future systems. Given the universality of electromagnetic propagation theory, the communication process is determined by the scattering environment, which can be more comprehensively characterized by cross-modal perception, thus providing sufficient information for all communication tasks across varied environments. This fact motivates us to propose a transformative two-stage multi-modal pre-training and downstream task adaptation paradigm... |
| title | Addressing the Curse of Scenario and Task Generalization in AI-6G: A Multi-Modal Paradigm |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.04797 |