Uncertainty Aware and Scalable WiFi CSI Sensing with Large Language Models
Conference
Sun, Y, Wang, X, Cao, G et al. (2026). Uncertainty Aware and Scalable WiFi CSI Sensing with Large Language Models
. 230-234. 10.1109/CHASE69719.2026.00037
Sun, Y, Wang, X, Cao, G et al. (2026). Uncertainty Aware and Scalable WiFi CSI Sensing with Large Language Models
. 230-234. 10.1109/CHASE69719.2026.00037
In this paper, we propose a robust, scalable, and interpretable WiFi Channel State Information (CSI) based sensing system that integrates functional data analysis (FDA) and large language models (LLMs) to achieve high performance on human activity and identity recognition tasks. Specifically, FDA is applied to reconstruct and extract structured features from CSI sequences, which are then processed by a lightweight LLM backbone incorporated with an uncertainty aware mechanism. To further enhance confidence calibration and feature representation, we design a composite loss function that integrates generative classification, contrastive learning, and uncertainty aware objectives. We conduct experiments on multiple public CSI-based sensing datasets. The results demonstrate competitive recognition accuracy and meaningful confidence calibration compared with representative deep learning baselines.
