Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach

By Chi Harold Liu, Zheyu Chen, Jian Tang, Jie Xu, Chengzhe Piao. IEEE Journal on Selected Areas in Communications, 2018

455 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/71e557c8f642eebf24fac1252f8210ddaac52df3

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Citations in the corpus

UAV Communications for 5G and Beyond: Recent Advances and Future Trends (2019, background): “[76] proposed a framework that leveraged deep reinforcement learning to study the energy consumption used for UAV movements, while maintaining the fair communication coverage and the network connectivity.”
Artificial-Intelligence-Enabled Intelligent 6G Networks (2019, methodology): “Note: mobility and handover parameters include BS association, spectrum access, and trajectory [10].”
UAV assistance paradigm: State-of-the-art in applications and challenges (2020, background): “This allows UAVs to better serve ground users (GUs) and to successfully accomplish the assigned tasks [9].”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, methodology): “The existing literature has studied a number of problems related to using ANNs for UAVs [90]–[97].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “vehicles using DQN is proposed in [401], where multiple objectives are targeted: maximizing energy efficiency, communications coverage, fairness and connectivity.”