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

由于其在智能家居、远程健康监测和辅助日常生活中的潜在应用，室内环境中的运动感知是许多人的强烈愿望。与基于图像的传感技术相反，无线信号(例如Wi-Fi)可以在不影响日常生活的情况下以隐私保护的方式用于运动传感。考虑到Wi-Fi信号无处不在的存在和运动感知的重要性，我们的合作伙伴组织(PO)认知系统公司开发了Wi-Fi运动感知(WMS)系统。然而，现有的WMS系统几乎没有什么限制，例如(I)仅预测特定的运动，分辨率仅限于在系统中运行的独立Wi-Fi设备。(Ii)有限的多用户分辨率，这是由于单用户和多用户信道干扰之间缺乏区分特征，(Iii)用于有监督的机器学习(ML)解决方案的训练标签有限，以及(Iv)只能在少数特定的室内环境中部署。在这个研究项目中，我们旨在通过提出新的ML解决方案来解决这些限制，并对可重构智能表面(RISS)等破坏性通信技术在WMS系统中的可行性进行分析。该项目的主要目标是(A)对多用户环境下的室内无线信道模型进行数学描述，并为各种ML解决方案生成一个全面的数据集。这些合成数据集将能够使我们PO目前拥有的标签数据集多样化，从而潜在地提高ML授权的WMS系统的定位精度，(B)推导出新的定制损失函数，并开发各种监督、自我监督和半监督的ML解决方案，以及(C)通过计算机模拟分析RISS在提高WMS系统精度方面的重要性。该组织将充分利用这一研究项目的进展和成果，从而能够继续在全球范围内引领运动传感领域。

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.