Energy efficiency analysis of Drone Small Cells positioning based on reinforcement learning

被引：2

作者：

dos Reis, Ana Flavia ^{[1
]}

Brante, Glauber ^{[1
]}

Parisotto, Rafaela ^{[2
]}

Souza, Richard D. ^{[2
]}

Klaine, Paulo H., V ^{[3
]}

Battistella, Joao Pedro ^{[3
]}

Imran, Muhammad A. ^{[3
]}

机构：

[1] Fed Univ Technol Parana UTFPR, Curitiba, Parana, Brazil

[2] Fed Univ Santa Catarina UFSC, Florianopolis, SC, Brazil

[3] Univ Glasgow, Glasgow, Lanark, Scotland

来源：

INTERNET TECHNOLOGY LETTERS | 2020年 / 3卷 / 05期

基金：

英国工程与自然科学研究理事会;

关键词：

drone small cells; energy efficiency; reinforcement learning;

D O I：

10.1002/itl2.166

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

This work proposes an algorithm to optimize the positioning and the transmit power of Drone Small Cells (DSCs) based on Q-learning, a technique where the agents learn to maximize a given reward. We consider two different rewards in this work, the first focusing on coverage, while the second maximizes the lifetime. Then, the Q-learning solution determines the best positioning of the DSC in the 3D space, as well as the optimal transmit power. Results show that the optimization of the transmit power is of paramount importance to reduce the outage probability. In addition, we show that the second reward can considerably increase the network lifetime with a small penalty to the coverage.

引用

页数：6

共 12 条

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