A Multi-Modal Foundational Model for Wireless Communication and Sensing

2026-02-03

AI Summary

This work addresses the limited generalization capability of existing learning-based wireless techniques, which often suffer from task specificity, environmental dependency, and single-modality constraints. To overcome these limitations, the paper proposes a task-agnostic, multimodal foundation model that leverages physics-guided self-supervised pretraining to integrate electromagnetic propagation principles into dedicated physical tokens. This approach enables cross-modal, physics-aware representation learning. By combining a multimodal fusion architecture with deep neural networks, the method achieves substantial improvements in generalization, deployment robustness, and data efficiency across diverse wireless tasks—including massive MIMO optimization, channel estimation, and device localization—using only minimal labeled data.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Artificial intelligence is a key enabler for next-generation wireless communication and sensing. Yet, today's learning-based wireless techniques do not generalize well: most models are task-specific, environment-dependent, and limited to narrow sensing modalities, requiring costly retraining when deployed in new scenarios. This work introduces a task-agnostic, multi-modal foundational model for physical-layer wireless systems that learns transferable, physics-aware representations across heterogeneous modalities, enabling robust generalization across tasks and environments. Our framework employs a physics-guided self-supervised pretraining strategy incorporating a dedicated physical token to capture cross-modal physical correspondences governed by electromagnetic propagation. The learned representations enable efficient adaptation to diverse downstream tasks, including massive multi-antenna optimization, wireless channel estimation, and device localization, using limited labeled data. Our extensive evaluations demonstrate superior generalization, robustness to deployment shifts, and reduced data requirements compared to task-specific baselines.

Problem

• generalization
• wireless communication
• sensing
• multi-modal
• task-specific

Innovation

• foundational model
• multi-modal
• physics-aware representation
• self-supervised pretraining
• wireless sensing