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Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning

Information Theory 2020-01-22 v3 Signal Processing math.IT
Authors: Yuwei Huang , Xiaopeng Mo , Jie Xu , Ling Qiu , Yong Zeng
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Abstract

This paper considers an unmanned aerial vehicle enabled-up link non-orthogonal multiple-access system, where multiple mobile users on the ground send independent messages to a unmanned aerial vehicle in the sky via non-orthogonal multiple-access transmission. Our objective is to design the unmanned aerial vehicle dynamic maneuver for maximizing the sum-rate throughput of all mobile ground users over a finite time horizon.

Keywords

uav optimization unmanned aerial vehicle networks unmanned aerial vehicle

Cite

@article{arxiv.1908.03984,
  title  = {Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning},
  author = {Yuwei Huang and Xiaopeng Mo and Jie Xu and Ling Qiu and Yong Zeng},
  journal= {arXiv preprint arXiv:1908.03984},
  year   = {2020}
}

Comments

This paper is to appear in IEEE WCNC 2020

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