Deployment and Trajectory Optimization of UAVs: A Quantization Theory Approach

TL;DR

This paper applies quantization theory to optimize the deployment and trajectories of UAVs for efficient ground terminal communication, analyzing static and dynamic scenarios with theoretical and numerical insights.

Contribution

It introduces a quantization theory-based framework for UAV deployment and trajectory optimization, providing analytical formulas and algorithms for static and dynamic cases.

Findings

Optimal UAV deployment minimizes average transmission power.

Derived formulas for UAV movement in dynamic scenarios.

Numerical simulations validate theoretical results.

Abstract

Optimal deployment and movement of multiple unmanned aerial vehicles (UAVs) is studied. The considered scenario consists of several ground terminals (GTs) communicating with the UAVs using variable transmission power and fixed data rate. First, the static case of a fixed geographical GT density is analyzed. Using high resolution quantization theory, the corresponding best achievable performance (measured in terms of the average GT transmission power) is determined in the asymptotic regime of a large number of UAVs. Next, the dynamic case where the GT density is allowed to vary periodically through time is considered. For one-dimensional networks, an accurate formula for the total UAV movement that guarantees the best time-averaged performance is determined. In general, the tradeoff between the total UAV movement and the achievable performance is obtained through a Lagrangian approach. A corresponding trajectory optimization algorithm is introduced and shown to guarantee a convergent Lagrangian. Numerical simulations confirm the analytical findings. Extensions to different system models and performance measures are also discussed.