Efficient algorithms for air-to-ground channel reconstruction in UAV-aided communications

This paper develops an efficient algorithm to learn and reconstruct from a small measurement samples an air-toground radio map with fine-grained propagation details so as to predict the signal strength between a wireless equipped UAV and arbitrary ground users, and ultimately the optimal position of the UAV as a mobile relay. In this paper, a joint data clustering and parameter estimation algorithm is developed to learn an multisegment propagation model from energy measurements that may contain large observation noise. To reduce the reconstruction complexity, we propose to learn a hidden multi-class virtual obstacle model to help efficiently predict the air-to-ground channel. Numerical results demonstrate that the channel prediction error is significantly reduced, and meanwhile, the radio map reconstruction time is reduced to 1/300.