Performance Evaluation of Learning-Based Channel Prediction for Communication Relay UAVs in Urban Environments

Abstract

This paper presents the performance evaluation of the communication channel prediction method based on Gaussian process (GP) regression for relay missions in urban environments. Considering restrictions from outdoor urban flight experiments, a way to simulate complex urban environments at an indoor room scale is introduced. Since water significantly absorbs wireless communication signal, water containers are utilized to replace buildings in a real-world city. To evaluate the performance of the GP-based channel prediction approach, several indoor experiments in an artificial urban environment are conducted. The performance of the GP-based and empirical model-based prediction methods for a relay mission is evaluated by measuring and comparing the communication signal strength at the optimal relay position obtained from each method. The GP-based prediction approach shows an advantage over the model-based one as it provides a reasonable performance without a need for a priori information of the environment (e.g. 3-D map of the city and communication model parameters) in dynamic urban environments.