论文标题
使用深厚的加强学习优化无人机辅助网络中的能源效率
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning
论文作者
论文摘要
在这封信中，我们研究了无人机（UAV）的能源效率（EE）优化，为静态和移动地面用户提供无线覆盖范围。最近的多机构增强学习方法使用2D轨迹设计优化了系统的EE，从而忽略了附近无人机细胞的干扰。我们的目标是通过共同优化每个无人机的3D轨迹，连接用户的数量以及消耗的能量，同时考虑干扰，从而最大程度地提高了系统的EE。因此，我们提出了一种合作的多机构分散的双重深度Q网络（MAD-DDQN）方法。我们的方法在EE方面的表现高达55％至80％。
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%.
