WiFo-M$^2$: Plug-and-Play Multi-Modal Sensing via Foundation Model to Empower Wireless Communications

The growing adoption of sensor-rich intelligent systems has boosted the use of multi-modal sensing to improve wireless communications. However, traditional methods require extensive manual design of data preprocessing, network architecture, and task-specific fine-tuning, which limits both development scalability and real-world deployment. To address this, we propose WiFo-M$^2$, a foundation model that can be easily plugged into existing deep learning-based transceivers for universal performance gains. To extract generalizable out-of-band (OOB) channel features from multi-modal sensing, we introduce ContraSoM, a contrastive pre-training strategy. Once pre-trained, WiFo-M$^2$ infers future OOB channel features from historical sensor data and strengthens feature robustness via modality-specific data augmentation. Experiments show that WiFo-M$^2$ improves performance across multiple transceiver designs and demonstrates strong generalization to unseen scenarios.