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[2511.15162] Multimodal Wireless Foundation Models
Summary
The paper introduces Multimodal Wireless Foundation Models (WFMs) that integrate multiple data modalities, enhancing wireless function performance across various tasks.
Why It Matters
This research is significant as it addresses the limitations of current wireless models that rely on a single modality. By enabling multimodal processing, the study paves the way for more robust wireless communication systems, essential for future technologies like AI-native 6G.
Key Takeaways
- Multimodal WFMs can process both raw IQ streams and image-like wireless data.
- The proposed model uses masked wireless modeling for effective self-supervised learning.
- Evaluation shows multimodal WFMs outperform single-modality models in several tasks.
- This advancement supports diverse wireless applications, enhancing adaptability.
- The research contributes to the vision of integrated sensing, communication, and localization.
Electrical Engineering and Systems Science > Signal Processing arXiv:2511.15162 (eess) [Submitted on 19 Nov 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:Multimodal Wireless Foundation Models Authors:Ahmed Aboulfotouh, Hatem Abou-Zeid View a PDF of the paper titled Multimodal Wireless Foundation Models, by Ahmed Aboulfotouh and Hatem Abou-Zeid View PDF HTML (experimental) Abstract:Wireless foundation models (WFMs) have recently demonstrated promising capabilities, jointly performing multiple wireless functions and adapting effectively to new environments. However, while current WFMs process only one modality, depending on the task and operating conditions, the most informative modality changes and no single modality is best for all tasks. WFMs should therefore be designed to accept multiple modalities to enable a broader and more diverse range of tasks and scenarios. In this work, we propose and build the first multimodal wireless foundation model capable of processing both raw IQ streams and image-like wireless modalities (e.g., spectrograms and CSI) and performing multiple tasks across both. We introduce masked wireless modeling for the multimodal setting, a self-supervised objective and pretraining recipe that learns a joint representation from IQ streams and image-like wireless modalities. We evaluate the model on five tasks across both modality families: image-based (human activity sensing, RF signal classification, 5G NR positioning) and IQ-based (RF ...
