Journal ArticleDOI
Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach
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TLDR
The proposed DRL-EC3 maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity, and makes decisions under the guidance of two powerful deep neural networks.Abstract:
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.read more
Citations
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Journal ArticleDOI
Deep Learning in Mobile and Wireless Networking: A Survey
TL;DR: This paper bridges the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas, and provides an encyclopedic review of mobile and Wireless networking research based on deep learning, which is categorize by different domains.
Journal ArticleDOI
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
TL;DR: This paper constitutes the first holistic tutorial on the development of ANN-based ML techniques tailored to the needs of future wireless networks and overviews how artificial neural networks (ANNs)-based ML algorithms can be employed for solving various wireless networking problems.
Journal ArticleDOI
UAV Communications for 5G and Beyond: Recent Advances and Future Trends
Bin Li,Zesong Fei,Yan Zhang +2 more
TL;DR: A comprehensive survey on UAV communication toward 5G/B5G wireless networks is presented and an exhaustive review of various 5G techniques based on Uav platforms is provided, which are categorize by different domains, including physical layer, network layer, and joint communication, computing, and caching.
Journal ArticleDOI
UAV Communications for 5G and Beyond: Recent Advances and Future Trends
Bin Li,Zesong Fei,Yan Zhang +2 more
TL;DR: A comprehensive survey on UAV communication towards 5G/B5G wireless networks is presented in this article, where UAVs are expected to be an important component of the upcoming wireless networks that can potentially facilitate wireless broadcast and support high rate transmissions.
Posted Content
Deep Learning in Mobile and Wireless Networking: A Survey
TL;DR: In this article, the authors provide an encyclopedic review of mobile and wireless networking research based on deep learning, which they categorize by different domains and discuss how to tailor deep learning to mobile environments.
References
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