Online Radio Environment Map Creation via UAV Vision for Aerial Networks

Abstract

Radio environment maps provide a comprehensive spatial view of the wireless channel and are especially useful in on-demand UAV wireless networks where operators are not afforded the typical time spent planning base station deployments (e.g. emergency response). Equipped with an accurate radio environment map, a mobile UAV can quickly locate to an optimal location to serve users on the ground. Machine learning has recently been proposed as a tool to create radio environment maps from satellite images of the target environment. However, the highly dynamic nature that precipitates most on-demand aerial network deployments likely renders the satellite image data available for the environment to be inaccurate. In this paper we present, OREMAN, a hybrid offline-online system for aerial radio environment map creation which leverages a common sensing modality present on most UAVs: visual cameras. OREMAN combines a suite of off-line trained neural network models with an adaptive trajectory planning algorithm to iteratively predict/refine the radio environment map and estimate the most valuable trajectory locations. By using UAV vision, OREMAN arrives at a highly accurate map much faster and with fewer measurements than other approaches, and is very effective even in scenarios where no prior environmental knowledge is available.