Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach
Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach
复制标题
用于有效和公平通信覆盖的节能无人机控制：深度强化学习方法
DOI：
10.1109/jsac.2018.2864373
复制
发表时间：
2018-09-01
影响因子：
16.4
通讯作者：
Piao, Chengzhe
中科院分区：
文献类型：
--
作者：
Liu, Chi Harold;Chen, Zheyu;Piao, Chengzhe
无人机可作为空中基站，增强各种场景下通信网络的覆盖范围和性能，例如应急通信、偏远地区的网络接入等。移动无人机可以为地面用户建立通信链路来传送数据包。然而，无人机的通信范围和能源有限。特别是对于较大的区域，它们无法始终覆盖整个区域或长时间保持飞行。因此，从长远来看，控制一组无人机实现一定的通信覆盖范围，同时保持其连接性并最大限度地减少能耗是一项挑战。为此，我们建议利用新兴的深度强化学习（DRL）进行无人机控制，并提出一种新颖且高能效的基于 DRL 的方法，我们将其称为基于 DRL 的覆盖和连接节能控制（DRL-EC3）。所提出的方法1）综合考虑通信覆盖、公平性、能耗和连接性，最大化新颖的能效函数； 2）了解环境及其动态； 3）在两个强大的深度神经网络的指导下做出决策。我们对性能评估进行了广泛的模拟。仿真结果表明，DRL-EC3 在覆盖率、公平性和能耗方面明显且持续优于两种常用的基线方法。
Unmanned aerial vehicles (UAVs) can be used to serve as aerial base stations to enhance both the coverage and performance of communication networks in various scenarios, such as emergency communications and network access for remote areas. Mobile UAVs can establish communication links for ground users to deliver packets. However, UAVs have limited communication ranges and energy resources. Particularly, for a large region, they cannot cover the entire area all the time or keep flying for a long time. It is thus challenging to control a group of UAVs to achieve certain communication coverage in a long run, while preserving their connectivity and minimizing their energy consumption. Toward this end, we propose to leverage emerging deep reinforcement learning (DRL) for UAV control and present a novel and highly energy-efficient DRL-based method, which we call DRL-based energy-efficient control for coverage and connectivity (DRL-EC3). The proposed method 1) maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity; 2) learns the environment and its dynamics; and 3) makes decisions under the guidance of two powerful deep neural networks. We conduct extensive simulations for performance evaluation. Simulation results have shown that DRL-EC3 significantly and consistently outperform two commonly used baseline methods in terms of coverage, fairness, and energy consumption.