构建可跨场景通用的无线通信感知基础模型，提升系统泛化能力。
A Multi-Modal Foundational Model for Wireless Communication and Sensing
- 基于物理引导的自监督预训练，融合电磁传播规律建模多模态关联
- 仅需少量标注数据即可适配大规模天线优化、信道估计等任务
- 适合需要快速部署、跨环境适应的无线系统研发人员
人工智能是下一代无线通信与感知的关键使能技术。然而，当前基于学习的无线技术泛化能力差：多数模型任务特定、依赖环境、仅限窄范围感知模态，新场景部署需高昂重训成本。本文提出一种面向物理层无线系统的任务无关、多模态基础模型，可在异构模态间学习可迁移的、具备物理意识的表征，实现跨任务与跨环境的鲁棒泛化。框架采用物理引导的自监督预训练策略，引入专用物理标记以捕捉由电磁传播决定的跨模态对应关系。所学表征支持在有限标注数据下高效适配多样下游任务，包括大规模多天线优化、无线信道估计和设备定位。大量实验表明，该模型相比任务特定基线具备更优泛化性能、更强部署漂移鲁棒性及更低数据需求。
原文摘要 · Abstract (English)
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.
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