Deep Learning Aided Transmit Power Estimation in Mobile Communication System

被引：10

作者：

Khan, Saud ^{[1
]}

Shin, Soo Young ^{[1
]}

机构：

[1] Kumoh Natl Inst Technol, Dept IT Convergence Engn, WENS Lab, Gumi 39177, South Korea

来源：

IEEE COMMUNICATIONS LETTERS | 2019年 / 23卷 / 08期

基金：

新加坡国家研究基金会;

关键词：

Deep learning; mobile communication; transmit power estimation;

D O I：

10.1109/LCOMM.2019.2923625

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

This letter investigates the problem of transmit power control in a mobile communication system. We propose a transmit power estimation scheme to maximize the overall system capacity where the transmit power control of the user is investigated using a recurrent neural network. This leads to a reduction in the need for generating a massive overhead with pilot signals in a dense cellular network. The gains obtained from the simulations are quantified in terms of the mean square error and overall system capacity. Our proposed scheme outperforms the conventional power control technique and other neural networks.

引用

页码：1405 / 1408

页数：4

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