Optimal Charging Profile Design for Solar-Powered Sustainable UAV Communication Networks

TL;DR

This paper develops a deep reinforcement learning framework to optimize solar charging and UAV operation for sustainable communication networks, balancing coverage and energy efficiency amid variable solar conditions.

Contribution

It introduces a novel DRL-based approach to solve a complex, time-coupled optimization problem for solar-powered UAV networks, including a relaxation mechanism for large action spaces.

Findings

DRL algorithms effectively balance coverage and energy loss.

Simulation shows parameter impacts on tradeoff performance.

Proposed method outperforms baseline strategies.

Abstract

This work studies optimal solar charging for solar-powered self-sustainable UAV communication networks, considering the day-scale time-variability of solar radiation and user service demand. The objective is to optimally trade off between the user coverage performance and the net energy loss of the network by proactively assigning UAVs to serve, charge, or land. Specifically, the studied problem is first formulated into a time-coupled mixed-integer non-convex optimization problem, and further decoupled into two sub-problems for tractability. To solve the challenge caused by time-coupling, deep reinforcement learning (DRL) algorithms are respectively designed for the two sub-problems. Particularly, a relaxation mechanism is put forward to overcome the "dimension curse" incurred by the large discrete action space in the second sub-problem. At last, simulation results demonstrate the efficacy of our designed DRL algorithms in trading off the communication performance against the net energy loss, and the impact of different parameters on the tradeoff performance.