Geography-aware Radio Map Reconstruction for UAV-aided Communications and Localization

Abstract

Radio maps can be used for source localization, link performance prediction, and wireless relay planning. This paper studies an air-to-ground radio map learning problem to predict the channel gain between a ground terminal and a low altitude unmanned aerial vehicle (UAV). The challenge is the insufficient measurement samples under the high dimensionality of the radio map, where both the transmitter and the receiver have full spatial degrees of freedom. Classical methods, such as k-nearest neighbor (KNN) and Kriging, may fail due to insufficient data. In this paper, a multi-degree channel model is proposed to fit low altitude air-to-ground propagation scenario. To address the high dimensionality issue, a hidden multi-class virtual obstacle model is developed, and a corresponding obstacle map is estimated from a small set of training data. Our numerical results confirm that the proposed model and the learning algorithm can significantly increase the prediction accuracy. When the constructed radio map applied to received signal strength (RSS) based source localization in a cellular communication scenario, a sub-20-meter accuracy is achieved, substantially better than the baseline model-based localization method.