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It is expected that unmanned aerial vehicles (UAVs) will play a vital role infuture communication systems. Optimum positioning of UAVs, serving as basestations, can be done through extensive field measurements or ray tracingsimulations when the 3D model of the region of interest is available. In thispaper, we present an alternative approach to optimize UAV base station altitudefor a region. The approach is based on deep learning; specifically, a 2Dsatellite image of the target region is input to a deep neural network topredict path loss distributions for different UAV altitudes. The predicted pathdistributions are used to calculate the coverage in the region; and the optimumaltitude, maximizing the coverage, is determined. The neural network isdesigned and trained to produce multiple path loss distributions in a singleinference; thus, it is not necessary to train a separate network for eachaltitude.

Altitude Optimization of UAV Base Stations from Satellite Images Using Deep Neural Network