Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908750223245312 |
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| author | Zhang, Han Farzanullah, Mohammad Ghassemi, Mohammad Sediq, Akram Bin Afana, Ali Erol-Kantarci, Melike |
| author_facet | Zhang, Han Farzanullah, Mohammad Ghassemi, Mohammad Sediq, Akram Bin Afana, Ali Erol-Kantarci, Melike |
| contents | Foundation models (FMs) are recognized as a transformative breakthrough that has started to reshape the future of artificial intelligence (AI) across both academia and industry. The integration of FMs into wireless networks is expected to enable the development of general-purpose AI agents capable of handling diverse network management requests and highly complex wireless-related tasks involving multi-modal data. Inspired by these ideas, this work discusses the utilization of FMs, especially multi-modal FMs in wireless networks. We focus on two important types of tasks in wireless network management: prediction tasks and control tasks. In particular, we first discuss FMs-enabled multi-modal contextual information understanding in wireless networks. Then, we explain how FMs can be applied to prediction and control tasks, respectively. Following this, we introduce the development of wireless-specific FMs from two perspectives: available datasets for development and the methodologies used. Finally, we conclude with a discussion of the challenges and future directions for FM-enhanced wireless networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03181 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey Zhang, Han Farzanullah, Mohammad Ghassemi, Mohammad Sediq, Akram Bin Afana, Ali Erol-Kantarci, Melike Networking and Internet Architecture Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Foundation models (FMs) are recognized as a transformative breakthrough that has started to reshape the future of artificial intelligence (AI) across both academia and industry. The integration of FMs into wireless networks is expected to enable the development of general-purpose AI agents capable of handling diverse network management requests and highly complex wireless-related tasks involving multi-modal data. Inspired by these ideas, this work discusses the utilization of FMs, especially multi-modal FMs in wireless networks. We focus on two important types of tasks in wireless network management: prediction tasks and control tasks. In particular, we first discuss FMs-enabled multi-modal contextual information understanding in wireless networks. Then, we explain how FMs can be applied to prediction and control tasks, respectively. Following this, we introduce the development of wireless-specific FMs from two perspectives: available datasets for development and the methodologies used. Finally, we conclude with a discussion of the challenges and future directions for FM-enhanced wireless networks. |
| title | Multi-Modal Data-Enhanced Foundation Models for Prediction and Control in Wireless Networks: A Survey |
| topic | Networking and Internet Architecture Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.03181 |