Trajectory Planning for UAV-Based Path Loss Data Collection to Enhance Permittivity Sensing in Ray-Tracing

Electromagnetic (EM) parameter plays a crucial role in wireless link prediction models such as Ray-tracing (RT), to shape and predict the effect of environmental materials on the characteristics of received signals. The use of field wireless measurements is a direct solution to sense EM parameters of materials. The locations of the field measurements have a significant effect on sensing and calibration performance of EM parameters. In this paper, a data collection algorithm is proposed to plan the measured locations of the field wireless measurement. Specifically, this paper presents an unmanned aerial vehicle (UAV) trajectory planning scheme for path loss data collection within a limited time to enhance the sensing and calibration of permittivity. The proposed algorithm is designed from two aspects simultaneously to enhance the sensing of permittivity: the detection of estimate-able permittivity parameters from the collected data, and the expected Cramér-Rao lower boundary (CRLB) of the detected permittivity parameters. The numerical results show that the proposed algorithm is effective in reducing the expected CRLB and detecting the permittivity parameters. Furthermore, these offline trajectories are validated in a simulation scenario, which demonstrates the benefits in estimating permittivity parameters, and the gain from sensing results in path loss prediction.