We consider a scenario where an unmanned aerial vehicle (UAV)-mounted flying base station is providing data communication services to a number of radio nodes spread over the ground. We focus on the problem of resource-constrained UAV trajectory design with optimal data throughput as a key objective. The advantage of this work comes from the exploitation of a 3-D city map. Unfortunately, the trajectory design based on the raw map data leads to an intractable optimization problem. To solve this issue, we introduce a map compression method that allows us to tackle the problem with standard optimization tools. The trajectory optimization is then combined with a node scheduling algorithm. The advantages of the learning-optimized trajectory and of the map compression method are illustrated in the context of intelligent Internet of Things data harvesting.

Link to the paper:

https://ieeexplore.ieee.org/abstract/document/8525324