发表机构
Shenzhen Research Institute of Big Data; The Chinese University of Hong Kong, Shenzhen; Shenzhen Loop Area Institute（深圳大数据研究院; 香港中文大学（深圳）; 深圳河套学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在解决基于CSI的WiFi传感异构问题，提出基础模型框架，通过整理标准化数据集、引入模块化架构，将CSI视为有通用语法的语言，实现跨设备和环境统一无线传感，提升泛化能力，优于特定任务基线。
AI 中文摘要
基于信道状态信息（CSI）的WiFi传感有望实现无处不在的、无设备感知，但目前的研究仍被困在“巴别塔”中，模型针对特定硬件方言、固定环境和狭窄任务碎片化。主要瓶颈是异构差距。为弥合这一差距，我们提出一个基础模型框架，将CSI视为有可学习通用语法的结构化语言。首先整理并标准化大量异构真实世界CSI数据集，建立统一基础设施。其次引入模块化架构，作为通用翻译器，特定数据集的轻量级适配器将不同信号输入标记化为共享潜在词汇，共享自监督Transformer主干学习人类运动和环境动态的时间语法。广泛评估表明，该方法始终优于特定任务基线，在新环境中具有强大泛化能力，为稳健通用的无线传感提供了途径。
英文摘要
WiFi sensing based on Channel State Information (CSI) promises ubiquitous, device-free perception, yet current research remains trapped in a Tower of Babel - fragmented into isolated silos where models are tailored to specific hardware dialects, fixed environments, and narrow tasks. The primary bottleneck is the Heterogeneity Gap: the disparity in signal dimensions, sampling rates, and semantic labels that prevents cross-system understanding. To bridge this gap, we propose a foundation-model framework that treats CSI not merely as raw signals but as a structured language with a learnable universal grammar. We first curate and standardize a large collection of heterogeneous real-world CSI datasets, establishing a unified infrastructure that allows incompatible signal formats to be treated as a single corpus. Second, we introduce a modular architecture that acts as a universal translator where lightweight dataset-specific adapters tokenize diverse signal inputs into a shared latent vocabulary, while a shared self-supervised Transformer backbone learns the temporal syntax of human motion and environmental dynamics. This design decouples sensing semantics from hardware syntax. Extensive evaluations show that by mastering this universal language, our approach consistently outperforms task-specific baselines and exhibits strong generalization capability in new environments, achieving superior efficiency in few-shot scenarios. By effectively absorbing heterogeneity, the framework offers a path toward robust, general-purpose wireless sensing, mirroring the linguistic generalization observed in Large Language Models. The code implementation is available at: https://github.com/cjychenjiayi/WiLLM.
