Altitude Optimization of UAV Base Stations from Satellite Images Using Deep Neural Network
This provides a more efficient method for UAV positioning in communication systems, though it is incremental as it builds on existing deep learning and satellite image techniques.
The paper tackles the problem of optimizing UAV base station altitude for communication coverage by using a deep neural network to predict path loss distributions from 2D satellite images, enabling determination of the altitude that maximizes coverage without needing separate models for each altitude.
It 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 predicted path distributions are used to calculate the coverage in the region; and the optimum altitude, maximizing the coverage, is determined. 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.