Robust Representation Learning Solutions for Wireless Sensing Applications

WiFi sensing, a technique for utilizing wireless signals for monitoring human activities and environmental conditions, holds substantial potential in diverse applications including human activity recognition (HAR). It offers a powerful, continuous, and non-intrusive monitoring solution. This technology eliminates the need for wearable sensors, and even functions outside the line-of-sight. However, the large-scale deployment of WiFi sensing faces several challenges: (1) limited computational power in WiFi devices, (2) the cost and complexity of annotating channel state information (CSI) data, and (3) ensuring model generalization across different environments.

The first part of the thesis addresses the limited computation power of edge devices by developing a Real-time Sensing and Compression Network (RSCNet). RSCNet is a cloud-based architecture designed to alleviate computational constraints on edge devices. It achieves this through efficient CSI compression at the edge and subsequent sensing and reconstruction in the cloud. RSCNet employs window-based CSI compression and LSTM-based recurrent blocks, significantly reducing computational demands and communication overheads while maintaining high sensing accuracy.

The second part of the thesis addresses the issue of limited labeled data by developing self-supervised learning (SSL) method, namely Context-Aware Predictive Coding (CAPC) method. CAPC combines contrastive predictive coding with the Barlow Twins method, enhancing the model's ability to learn robust representations from unlabeled CSI time-series data. This approach improves model generalization, particularly when labeled data is scarce. CAPC also introduces a novel augmentation technique, dual view, which isolates free space propagation information from hardware distortions, further enhancing representation quality for WiFi sensing applications.

Through extensive evaluations, this thesis demonstrates the effectiveness of both RSCNet and CAPC. RSCNet achieves results on par with the state-of-the-art performance in HAR tasks while drastically reducing computational burdens on edge devices. CAPC outperforms baseline SSL approaches and traditional supervised methods, showcasing its superior generalization capabilities in unseen environments. The dual view augmentation further enhances CAPC's performance by reducing electronic distortions. This thesis concludes that RSCNet and CAPC contribute significantly to the advancement of robust and practical wireless sensing technologies. These frameworks address critical challenges in the field, paving the way for wider adoption of WiFi sensing in real-world applications.