Baykaş, TunçerShoer, IbrahimGunturk, Bahadir K.Ates, Hasan F.Baykas, Tuncer2024-10-152024-10-152022097816654962091522-4880https://doi.org/10.1109/ICIP46576.2022.9897467https://hdl.handle.net/20.500.12469/6334It is expected that unmanned aerial vehicles (UAVs) will play a vital role in future communication systems. Optimum positioning of UAVs, serving as base stations, can be done through extensive field measurements or ray tracing simulations when the 3D model of the region of interest is available. In this paper, we present an alternative approach to optimize UAV base station altitude for a region. The approach is based on deep learning; specifically, a 2D satellite image of the target region is input to a deep neural network to predict path loss distributions for different UAV altitudes. The neural network is designed and trained to produce multiple path loss distributions in a single inference; thus, it is not necessary to train a separate network for each altitude.eninfo:eu-repo/semantics/closedAccessconvolutional neural networksdeep learningpath loss estimationUAV networksConference Object24712475WOS:00105810950211310.1109/ICIP46576.2022.9897467N/AN/A