Learning to Communicate in UAV-aided Wireless Networks: Map-based Approaches

TL;DR

This paper presents a novel approach for UAV trajectory design that combines map-based data compression with optimization techniques to improve communication and parameter learning in UAV-assisted wireless networks.

Contribution

It introduces a map compression method enabling efficient trajectory optimization for UAVs, addressing both channel parameter learning and data throughput objectives.

Findings

Map compression enables tractable optimization.

Optimized trajectories improve data harvesting efficiency.

Combined approach enhances UAV communication performance.

Abstract

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.