Learning to Rest: A Q-Learning Approach to Flying Base Station Trajectory Design with Landing Spots

被引:0
作者
Bayerlein, Harald [1 ]
Gangula, Rajeev [1 ]
Gesbert, David [1 ]
机构
[1] EURECOM, Commun Syst Dept, Sophia Antipolis, France
来源
2018 CONFERENCE RECORD OF 52ND ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS, AND COMPUTERS | 2018年
基金
欧洲研究理事会;
关键词
COMMUNICATION;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We consider the problem of trajectory optimization for an autonomous UAV-mounted base station that provides communication services to ground users with the aid of landing spots (LSs). Recently, the concept of LSs was introduced to alleviate the problem of short mission durations arising from the limited on-board battery budget of the UAV, which severely limits network performance. hi this work, using Q-learning, a model-free reinforcement learning (RL) technique, we train a neural network (NN) to make movement decisions for the UAV that maximize the data collected from the ground users while minimizing power consumption by exploiting the landing spots. We show that the system intelligently integrates landing spots into the trajectory to extend flying time and is able to learn the topology of the network over several flying epochs without any explicit information about the environment.
引用
收藏
页码:724 / 728
页数:5
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