# WiFo-MiSAC: A Wireless Foundation Model for Multimodal Sensing and Communication Integration via Synesthesia of Machines (SoM)

arXiv: https://arxiv.org/abs/2604.18255 · HTML: https://mlsanity.com/paper/2604.18255/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2026.

## Built on by (most cited first, 4 of 4)

- [Lightweight Adaptive Feature Composition for Heterogeneous Downstream Adaptation of Wireless Foundation Models](https://mlsanity.com/paper/2606.10277/) (2026)
- [A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks](https://mlsanity.com/paper/2608.14694/) (2026)
- [CM-MAE: A Physics-Guided Cross-Modal Self-Supervised Learning Framework for Vision-Wireless Applications](https://mlsanity.com/paper/2608.15972/) (2026)
- [Wireless Foundation Models: State-of-the-Art and Open Challenges](https://mlsanity.com/paper/2609.04707/) (2026)

## Abstract (arXiv)

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.