6G Sensing Systems: From Radio Channels to Machine Learning (wECHO2)

Abstract

In the context of future 6th generation (6G) and Integrated Sensing and Communication (ISAC) applications, this thesis explores the possibility of using data from the S21 parameter for indoor detection of human presence in the 4 GHz to 16 GHz frequency range. Physical measurements in Chalmers’ antenna lab were combined with simulations in a digital twin of the lab environment to analyze how a stationary human affects the wireless channel. The Multiple Signal Classification (MUSIC) algorithm was applied to both physical and simulated data in order to analyze directions of arrival (DoA) and reflections caused by human presence. The results show that the MUSIC algorithm was able to detect changes caused by a human in the wireless channel, although the accuracy depended on the environment, the placement of the antennas as well as the assumed number of sources. Additionally, a Convolutional Neural Network (CNN) was trained on simulated and measured data in several dataset combinations. As a result it could be observed that combining simulated and real data in training improved the models ability to adapt to realistic environments, while training on only simulated data resulted in reduced transferability to the real measurements.