Machine-Learning-Empowered Propagation Measurement and Modeling for an Amphitheater

This paper presents a machine learning (ML) approach to modeling radio signal strength in an open-air amphitheater environment. Towards this end, we have first collected field measurement data from an amphitheater located on the Malaysia Campus of the University of Nottingham at 900 MHz, 2.4 and 5.8 GHz. We have conducted the measurement campaign over the course of six months, with repeatability test done for each frequency for at least one cavea level to make sure the results are repeatable. These measurement data are plotted and analyzed before being fed to a ML model for training purposes. In particular, we have explored three options of the ML methods, namely, the Linear Regression method, the Random Forest method, and the Neural Network method; and finally settled on the Neural Network method for is superiority over the other two methods -it performs better when more input data were inserted to train continuously. In addition, we have run ray-tracing simulation to provide an extra layer of comparison to the ML-generated prediction results. Beyond this, we have expanded the ML model to account for a larger geometry of amphitheater. The output of this work is expected to enhance wireless communication reliability in amphitheaters, with potential benefits for event management, public safety, and entertainment industries.