  We consider a scenario where an 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 (i)
optimal channel parameters learning and (ii) optimal data throughput as key
objectives, respectively. While the problem of throughput optimized
trajectories has been addressed in prior works, the formulation of an optimized
trajectory to efficiently discover the propagation parameters has not yet been
addressed. When it comes to the communication phase, the advantage of this work
comes from the exploitation of a 3D city map. Unfortunately, the communication
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 IoT data harvesting.
