Prediction of air-to-ground communication strength for relay UAV trajectory planner in urban environments

This paper proposes the use of a learning approach to predict air-to-ground (A2G) communication strength in support of the communication relay mission using UAVs in an urban environment. To plan an efficient relay trajectory, A2G communication link quality needs to be predicted between the UAV and ground nodes. However, due to frequent occlusions by buildings in the urban environment, modelling and predicting communication strength is a difficult task. Thus, a need for learning techniques such as Gaussian Process (GP) arises to learn about inaccuracies in a pre-defined communication model and the effect of line-of-sight obstruction. Two ways of combining GP with a relay trajectory planner are presented: i) scanning the area of interest with the UAV to collect communication strength data first and then using learned data in the trajectory planner and ii) collecting data and running the trajectory planner simultaneously. The performance of both approaches is compared with Monte Carlo simulations. It is shown that the first implementation results in slightly better predictions, however the second one benefits from being able to start the relay mission immediately.