Peng, Yubo and Xiang, Luping and Yang, Kun and Chen, Jienan (2026) Large AI Model for Multimodal Integrated Sensing and Communication. IEEE Network. pp. 1-8. DOI https://doi.org/10.1109/mnet.2026.3661589
Peng, Yubo and Xiang, Luping and Yang, Kun and Chen, Jienan (2026) Large AI Model for Multimodal Integrated Sensing and Communication. IEEE Network. pp. 1-8. DOI https://doi.org/10.1109/mnet.2026.3661589
Peng, Yubo and Xiang, Luping and Yang, Kun and Chen, Jienan (2026) Large AI Model for Multimodal Integrated Sensing and Communication. IEEE Network. pp. 1-8. DOI https://doi.org/10.1109/mnet.2026.3661589
Abstract
Multimodal integrated sensing and communication (ISAC) exploits heterogeneous modalities to enhance perception accuracy, communication robustness, and environmental adaptability, becoming a key enabler of the Internet of Everything (IoE). Nevertheless, existing multimodal ISAC systems remain constrained by heterogeneous data characteristics, dynamic modality availability, and the limited adaptability of current fusion strategies. To overcome these limitations, we propose a LAM-enabled multimodal ISAC (LAM-MSAC) framework. First, modality-specific feature encoders are introduced to provide native compatibility for diverse sensing data. Second, to cope with dynamically changing modality combinations, we design a Mixture-of-Experts (MoE) fusion module in which multiple experts process different modal combinations. Finally, the MoE structure activates only a subset of experts for each inference, substantially reducing computational cost without sacrificing model capacity. A case study shows that LAM-MSAC achieves over 90% beam prediction accuracy while significantly lowering computation compared with maintaining multiple single- or multi-modality models. Potential research directions are further discussed to advance the integration of LAMs into multimodal ISAC.
| Item Type: | Article |
|---|---|
| Subjects: | Z Bibliography. Library Science. Information Resources > ZR Rights Retention |
| Divisions: | Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
| SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
| Depositing User: | Unnamed user with email elements@essex.ac.uk |
| Date Deposited: | 18 Mar 2026 12:40 |
| Last Modified: | 18 Mar 2026 12:40 |
| URI: | http://repository.essex.ac.uk/id/eprint/42962 |
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