Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning
This work addresses energy efficiency for UAV networks, which is an incremental improvement over prior methods that neglected interference.
The paper tackled the problem of optimizing energy efficiency in UAV-assisted networks by jointly optimizing 3D trajectories, user connections, and energy consumption while accounting for interference, resulting in a 55-80% improvement in energy efficiency over existing baselines.
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%.