Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning

TL;DR

This paper introduces a cooperative multi-agent deep reinforcement learning method to optimize energy efficiency in UAV-assisted wireless networks, considering 3D trajectories, user connections, and interference, significantly outperforming existing methods.

Contribution

It presents a novel MAD-DDQN approach for joint optimization of UAV trajectories and user connections, accounting for interference, which improves energy efficiency.

Findings

Achieves up to 80% improvement in energy efficiency.

Outperforms existing baselines significantly.

Effectively manages interference in UAV networks.

Abstract

In this letter, we study the energy efficiency (EE) optimisation of unmanned aerial vehicles (UAVs) providing wireless coverage to static and mobile ground users. Recent multi-agent reinforcement learning approaches optimise the system's EE using a 2D trajectory design, neglecting interference from nearby UAV cells. We aim to maximise the system's EE by jointly optimising each UAV's 3D trajectory, number of connected users, and the energy consumed, while accounting for interference. Thus, we propose a cooperative Multi-Agent Decentralised Double Deep Q-Network (MAD-DDQN) approach. Our approach outperforms existing baselines in terms of EE by as much as 55 -- 80%.