The Universal Language of CSI:Unifying Wireless Sensing Across Devices and Environments

• URL: http://arxiv.org/abs/2607.09727v1
• Date: Tue, 30 Jun 2026 04:30:04 GMT
• Title: The Universal Language of CSI:Unifying Wireless Sensing Across Devices and Environments
• Abstract summary: WiFi sensing based on Channel State Information promises ubiquitous, device-free perception.<n>Current research remains trapped in a Tower of Babel, fragmented into isolated silos.<n>We propose a foundation-model framework that treats CSI as a structured language with a learnable universal grammar.
• Score: 18.372157907342817
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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