WiFo-MiSAC: A Wireless Foundation Model for Multimodal Sensing and Communication Integration via Synesthesia of Machines (SoM)
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910148813914112 |
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| author | Liu, Xuanyu Gao, Shijian Liu, Boxun Cheng, Xiang Yang, Liuqing |
| author_facet | Liu, Xuanyu Gao, Shijian Liu, Boxun Cheng, Xiang Yang, Liuqing |
| contents | Current learning-based wireless methods struggle with generalization due to the fragmented processing of communication and sensing data. WiFo-MiSAC addresses this as a task-agnostic foundation model that tokenizes heterogeneous signals into a unified space for self-supervised pre-training. A shared-specific disentangled mixture-of-experts (SS-DMoE) architecture is employed to decouple modality-shared and modality-specific representations, facilitating interaction without cross-modal interference. By combining masked reconstruction with contrastive alignment, the model achieves state-of-the-art performance across downstream tasks, including beam prediction and channel estimation. Experimental results demonstrate robust few-shot adaptation and seamless integration of new modalities, positioning WiFo-MiSAC as a scalable backbone for future integrated sensing and communication systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_18255 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | WiFo-MiSAC: A Wireless Foundation Model for Multimodal Sensing and Communication Integration via Synesthesia of Machines (SoM) Liu, Xuanyu Gao, Shijian Liu, Boxun Cheng, Xiang Yang, Liuqing Signal Processing Current learning-based wireless methods struggle with generalization due to the fragmented processing of communication and sensing data. WiFo-MiSAC addresses this as a task-agnostic foundation model that tokenizes heterogeneous signals into a unified space for self-supervised pre-training. A shared-specific disentangled mixture-of-experts (SS-DMoE) architecture is employed to decouple modality-shared and modality-specific representations, facilitating interaction without cross-modal interference. By combining masked reconstruction with contrastive alignment, the model achieves state-of-the-art performance across downstream tasks, including beam prediction and channel estimation. Experimental results demonstrate robust few-shot adaptation and seamless integration of new modalities, positioning WiFo-MiSAC as a scalable backbone for future integrated sensing and communication systems. |
| title | WiFo-MiSAC: A Wireless Foundation Model for Multimodal Sensing and Communication Integration via Synesthesia of Machines (SoM) |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.18255 |