Convolutional Neural Network Based Transmit Power Control for D2D Communication in Unlicensed Spectrum

Zhenyu Fan, Xinyu Gu
{"title":"Convolutional Neural Network Based Transmit Power Control for D2D Communication in Unlicensed Spectrum","authors":"Zhenyu Fan, Xinyu Gu","doi":"10.1109/IC-NIDC54101.2021.9660479","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a means of Device-to-Device communication extended to unlicensed spectrum (D2D-U) to alleviate the dense deployment of smart devices in licensed spectrum with consideration of fairly coexisting with Wi-Fi. To achieve high system performance in D2D-U, a method of managing D2D mutual interference is needed. For this issue, we propose a convolutional neural network (CNN)-based transmit power control scheme which experiences a low computational complexity compared with conventional transmit power control scheme. Simulation results indicate that the CNN-based power control scheme can achieve superior performance with a low computational complexity.","PeriodicalId":264468,"journal":{"name":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 7th IEEE International Conference on Network Intelligence and Digital Content (IC-NIDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC-NIDC54101.2021.9660479","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, we propose a means of Device-to-Device communication extended to unlicensed spectrum (D2D-U) to alleviate the dense deployment of smart devices in licensed spectrum with consideration of fairly coexisting with Wi-Fi. To achieve high system performance in D2D-U, a method of managing D2D mutual interference is needed. For this issue, we propose a convolutional neural network (CNN)-based transmit power control scheme which experiences a low computational complexity compared with conventional transmit power control scheme. Simulation results indicate that the CNN-based power control scheme can achieve superior performance with a low computational complexity.
基于卷积神经网络的无许可频谱D2D通信发射功率控制
在本文中,我们提出了一种扩展到非授权频谱(D2D-U)的设备对设备通信方式,以缓解授权频谱中智能设备的密集部署,同时考虑与Wi-Fi的公平共存。为了在D2D- u中实现较高的系统性能,需要一种控制D2D互干扰的方法。针对这一问题,我们提出了一种基于卷积神经网络(CNN)的发射功率控制方案,与传统的发射功率控制方案相比,该方案具有较低的计算复杂度。仿真结果表明,基于cnn的功率控制方案可以在较低的计算复杂度下获得较好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信