AI2MMUM: AI-AI Oriented Multi-Modal Universal Model Leveraging Telecom Domain Large Model
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912376626872320 |
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| author | Jiao, Tianyu Xiao, Zhuoran Huang, Yihang Ye, Chenhui Feng, Yijia Cai, Liyu Chang, Jiang Liu, Fangkun Xu, Yin He, Dazhi Guan, Yunfeng Zhang, Wenjun |
| author_facet | Jiao, Tianyu Xiao, Zhuoran Huang, Yihang Ye, Chenhui Feng, Yijia Cai, Liyu Chang, Jiang Liu, Fangkun Xu, Yin He, Dazhi Guan, Yunfeng Zhang, Wenjun |
| contents | Designing a 6G-oriented universal model capable of processing multi-modal data and executing diverse air interface tasks has emerged as a common goal in future wireless systems. Building on our prior work in communication multi-modal alignment and telecom large language model (LLM), we propose a scalable, task-aware artificial intelligence-air interface multi-modal universal model (AI2MMUM), which flexibility and effectively perform various physical layer tasks according to subtle task instructions. The LLM backbone provides robust contextual comprehension and generalization capabilities, while a fine-tuning approach is adopted to incorporate domain-specific knowledge. To enhance task adaptability, task instructions consist of fixed task keywords and learnable, implicit prefix prompts. Frozen radio modality encoders extract universal representations and adapter layers subsequently bridge radio and language modalities. Moreover, lightweight task-specific heads are designed to directly output task objectives. Comprehensive evaluations demonstrate that AI2MMUM achieves SOTA performance across five representative physical environment/wireless channel-based downstream tasks using the WAIR-D and DeepMIMO datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10003 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI2MMUM: AI-AI Oriented Multi-Modal Universal Model Leveraging Telecom Domain Large Model Jiao, Tianyu Xiao, Zhuoran Huang, Yihang Ye, Chenhui Feng, Yijia Cai, Liyu Chang, Jiang Liu, Fangkun Xu, Yin He, Dazhi Guan, Yunfeng Zhang, Wenjun Machine Learning Signal Processing Designing a 6G-oriented universal model capable of processing multi-modal data and executing diverse air interface tasks has emerged as a common goal in future wireless systems. Building on our prior work in communication multi-modal alignment and telecom large language model (LLM), we propose a scalable, task-aware artificial intelligence-air interface multi-modal universal model (AI2MMUM), which flexibility and effectively perform various physical layer tasks according to subtle task instructions. The LLM backbone provides robust contextual comprehension and generalization capabilities, while a fine-tuning approach is adopted to incorporate domain-specific knowledge. To enhance task adaptability, task instructions consist of fixed task keywords and learnable, implicit prefix prompts. Frozen radio modality encoders extract universal representations and adapter layers subsequently bridge radio and language modalities. Moreover, lightweight task-specific heads are designed to directly output task objectives. Comprehensive evaluations demonstrate that AI2MMUM achieves SOTA performance across five representative physical environment/wireless channel-based downstream tasks using the WAIR-D and DeepMIMO datasets. |
| title | AI2MMUM: AI-AI Oriented Multi-Modal Universal Model Leveraging Telecom Domain Large Model |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2505.10003 |