WiSenseNet: A Unified Foundation Model for Diverse Wi-Fi Sensing Tasks Using Channel State Information

Abstract

Wi-Fi sensing utilizing Channel State Information (CSI) has emerged as a promising non-invasive technique for environmental perception, but current approaches are hindered by task-specific architectures, limited generalization, and data inefficiency, impeding its versatility across diverse applications. We introduce WiSenseNet, a novel foundation model for multitask Wi-Fi sensing that addresses these challenges. Inspired by large language models, WiSenseNet adapts architectures like Mamba to process CSI data, employing a hybrid design of self-attention and state space layers to capture complex spatiotemporal dependencies in Wi-Fi signals. Our model demonstrates remarkable versatility across diverse sensing tasks, including gesture recognition, gait analysis, human activity recognition, and occupancy detection. Rigorous experiments on multiple benchmark datasets reveal WiSenseNet’s superior performance: in gesture recognition, it achieves 97.4% accuracy with only 797.70K Multiply-Accumulate Operations (MACs), surpassing both CNN and Transformer baselines by 3.6% and 1.8% respectively, while reducing computational complexity by 66%. This efficiency extends across tasks, with WiSenseNet achieving 95.4% accuracy in human activity recognition and 94% in gait analysis. For human presence sensing, WiSenseNet attains 89.7% accuracy with 462.34K MACs, demonstrating its ability to extract meaningful features from subtle CSI variations. Our comprehensive ablation study reveals task-specific optimal configurations, elucidating the relationship between model complexity and sensing performance. WiSenseNet represents a significant advancement in Wi-Fi sensing, offering a unified, scalable solution that outperforms tasks-pecific models while maintaining computational efficiency.