Channel characterization and adaptive learning solutions for WiFi-assisted sensing in indoor environments

点击翻译按钮获取中文摘要

Motion sensing in an indoor environment is a strong desire of a multitude of people due to its potential applications in smart homes, remote health monitoring and assisted daily living. Contrary to image-based sensing technologies, wireless signals (e.g., Wi-Fi) can be leveraged for motion sensing in a privacy-preserved manner without affecting daily routines. Considering the ubiquitous presence of Wi-Fi signals and significance of motion sensing, our partner organization (PO) 'Cognitive Systems Corp' developed a Wi-Fi Motion Sensing (WMS) system. However, the existing WMS system has few limitations such as (i) predicting only a specific motion, limited in resolution to an independent Wi-Fi device operating in the system. (ii) limited multi-user resolution, due to the absence of distinguishing features between single and multi-user channel disturbances, (iii) limited training labels for supervised machine learning (ML) solutions, and (iv) can only be deployed in a few particular indoor environments. In this research project, we aim to address these limitations through proposing novel ML solutions and perform the feasibility analysis of disruptive communication technologies such as reconfigurable intelligent surfaces (RISs) for the WMS system. The primary goals of this project are (A) Characterize indoor wireless channel models mathematically in multi-user environments and generate a comprehensive dataset for various ML solutions. These synthetic datasets will be able to diversify the labeled datasets that are currently owned by our PO and thus can potentially enhance the localization accuracy of the ML-empowered WMS system, (B) Derive novel customized loss functions and develop various supervised, self-supervised, and semi-supervised ML solutions, and (C) Analyze the significance of RISs in increasing the accuracy of the WMS system through computer simulations. The PO will make full use of the advancements and findings of this research project, hence will be able to continue to lead in the field of motion sensing at a global scale.