UAV-aided Joint Radio Map and 3D Environment Reconstruction using Deep Learning Approaches
Pengxi Zeng, Junting Chen
Abstract
Radio maps have been exploited for a wide range of applications including UAV trajectory planning, network resource optimization, and localization. In this paper, a model-driven deep learning (DL) model is developed for joint radio map learning and 3D environment reconstruction. The challenge mainly comes from the high dimensionality of the radio map for characterizing radio links between transmitters and receivers with full spatial degrees of freedom. A framework with model-assisted deep neural network (DNN) structures to learn and memorize the 3D virtual obstacle environment is proposed. A semi-self-adaptive training strategy is developed for the proposed DL-based radio map, which demonstrates robustness over existing model-based radio map construction methods. Numerical results show that our DL framework outperforms pure model-based method in terms of higher radio map reconstruction accuracy and computation efficiency.