Abstract
Extended Reality (XR) has become the killer application for future wireless networks. XR is expected to be a major source of traffic for 6G. Use cases include education, health care, and gaming. XR requires accurate and real-time pose information (gesture recognition) to enable seamless experience. Recently, researchers have investigated the use of sub-6 GHz Wi-Fi signals for pose estimation and the results are very promising. Radio waves at these frequencies offer limited resolution due to low bandwidth. On the other hand, mmWave frequencies (>30 GHz) not only offer high data-rates but also high spatial resolution. The improved spatial resolution can benefit pose estimation in XR applications where accurate motion tracking is important for immersive and realistic experience. In this project, my aim is to leverage mmWave signals for pose estimation. This idea of using communications signals for sensing is known as Integrated Sensing and Communication (ISAC). To do this, I aim to collect an extensive and realistic dataset of gestures across several people and environments. I will then develop novel signal processing and deep learning-based algorithms for environment independent and multi-user sensing. Moreover, I will investigate a low power solution for pose estimation on the edge devices. Finally, I will develop a real-time prototype, which projects real-world movements onto the virtual world avatar of the user.
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