Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication

被引：36

作者：

Lee, Woongsup ^{[1
]}

Kim, Minhoe ^{[2
]}

Cho, Dong-Ho ^{[3
]}

机构：

[1] Gyeongsang Natl Univ, Inst Marine Ind, Dept Informat & Commun Engn, Tongyeong 53064, South Korea

[2] EURECOM, Dept Commun Syst, F-06410 Sophia Antipolis, France

[3] Korea Adv Inst Sci & Technol, Sch Elect Engn, Daejeon 34141, South Korea

来源：

IEEE SYSTEMS JOURNAL | 2019年 / 13卷 / 03期

基金：

新加坡国家研究基金会;

关键词：

Channel capacity; interference; machine learning; power control; wireless communication;

D O I：

10.1109/JSYST.2018.2870483

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a means of transmit power control for underlaid device-to-device (D2D) comm proposed based on deep learning technology. In the proposed scheme, the transmit power of D2D user equipment (DUE) is autonomously learned via a deep neural network such that the weighted stun rate (WSR) of DUEs can be maximized by considering the interference from cellular user equipment. Unlike conventional transmit power control schemes in which complex optimization problems have to be solved in an iterative manner which possibly requires long c imitation time, in our proposed scheme the transmit power can be determined with a relatively low computation time. Through simulations, we confirm that the proposed scheme achieves a sufficiently high WSR with a sufficiently low computation time.

引用

页码：2551 / 2554

页数：4

共 21 条

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