Computer Vision Enabled Sub-THz Radio Channel Characterization of Dynamic Objects

To enable a wide range of joint communications and sensing applications, future channel models must support dynamic variations of the propagation environment. Moreover, when discovering sensing applications, it is essential to connect physical objects and actions to the radio channel characteristics. In this paper, we perform computer vision (CV) aided automatic mapping and showcase that channel measurements can be linked to the dynamic actions and objects in the environment, thus supporting sensing applications. The proposed method employs a vector network analyzer-based channel measurement system operating at 300 GHz and a camera to enable CV for tracking the object in the radio link. Using human hand as the blockage object, the CV-extracted coordinates and the measured channel responses are combined to generate radio channel footprints for different hand movements.