基于深度学习的毫米波海量MIMO系统混合预编码技术

Islam Osama, M. Rihan, M. Elhefnawy, S. Eldolil
{"title":"基于深度学习的毫米波海量MIMO系统混合预编码技术","authors":"Islam Osama, M. Rihan, M. Elhefnawy, S. Eldolil","doi":"10.1109/ICEEM52022.2021.9480386","DOIUrl":null,"url":null,"abstract":"Communications over millimeter-wave (mm-Wave) frequencies are considered as a new revolution of wireless communications, specifically with the official launching of 5G. Typically, mm-Wave with massive multiple-input multiple-output (MIMO) can be implemented by using the hybrid beamforming transceivers that consists of massive number of analog phase shifters and smaller number of RF chains. The power consumption and cost are reduced when the hybrid beamforming architecture is implemented by combining the digital and analog beamforming. The main motivation for this paper is to introduce a deep learning-based hybrid beamforming design to join optimization of the precoder and combiner in massive MIMO mm-Wave communication systems. Specifically, the joint optimization of the precoder and combiner is carried out by means of two convolutional neural networks (CNN) and through going into two stages of operation, namely training and prediction stages. The MATLAB simulation results show that the deep learning-based hybrid beamforming approach for the mm-Wave massive MIMO outperforms the legacy optimization-based hybrid beamforming approaches in terms of spectrum efficiency.","PeriodicalId":352371,"journal":{"name":"2021 International Conference on Electronic Engineering (ICEEM)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Deep Learning Based Hybrid Precoding Technique for Millimeter-Wave Massive MIMO Systems\",\"authors\":\"Islam Osama, M. Rihan, M. Elhefnawy, S. Eldolil\",\"doi\":\"10.1109/ICEEM52022.2021.9480386\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Communications over millimeter-wave (mm-Wave) frequencies are considered as a new revolution of wireless communications, specifically with the official launching of 5G. Typically, mm-Wave with massive multiple-input multiple-output (MIMO) can be implemented by using the hybrid beamforming transceivers that consists of massive number of analog phase shifters and smaller number of RF chains. The power consumption and cost are reduced when the hybrid beamforming architecture is implemented by combining the digital and analog beamforming. The main motivation for this paper is to introduce a deep learning-based hybrid beamforming design to join optimization of the precoder and combiner in massive MIMO mm-Wave communication systems. Specifically, the joint optimization of the precoder and combiner is carried out by means of two convolutional neural networks (CNN) and through going into two stages of operation, namely training and prediction stages. The MATLAB simulation results show that the deep learning-based hybrid beamforming approach for the mm-Wave massive MIMO outperforms the legacy optimization-based hybrid beamforming approaches in terms of spectrum efficiency.\",\"PeriodicalId\":352371,\"journal\":{\"name\":\"2021 International Conference on Electronic Engineering (ICEEM)\",\"volume\":\"85 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-07-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Electronic Engineering (ICEEM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICEEM52022.2021.9480386\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Electronic Engineering (ICEEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEEM52022.2021.9480386","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

毫米波(mm-Wave)频率的通信被认为是无线通信的新革命,特别是随着5G的正式推出。通常,具有大量多输入多输出(MIMO)的毫米波可以通过使用混合波束成形收发器来实现,该收发器由大量模拟移相器和较少数量的射频链组成。采用数字波束形成和模拟波束形成相结合的方法实现混合波束形成,降低了波束形成的功耗和成本。本文的主要动机是介绍一种基于深度学习的混合波束形成设计,以结合大规模MIMO毫米波通信系统中预编码器和组合器的优化。具体来说,预编码器和组合器的联合优化是通过两个卷积神经网络(CNN)进行的,分为训练和预测两个阶段。MATLAB仿真结果表明,基于深度学习的毫米波大规模MIMO混合波束形成方法在频谱效率方面优于传统的基于优化的混合波束形成方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning Based Hybrid Precoding Technique for Millimeter-Wave Massive MIMO Systems
Communications over millimeter-wave (mm-Wave) frequencies are considered as a new revolution of wireless communications, specifically with the official launching of 5G. Typically, mm-Wave with massive multiple-input multiple-output (MIMO) can be implemented by using the hybrid beamforming transceivers that consists of massive number of analog phase shifters and smaller number of RF chains. The power consumption and cost are reduced when the hybrid beamforming architecture is implemented by combining the digital and analog beamforming. The main motivation for this paper is to introduce a deep learning-based hybrid beamforming design to join optimization of the precoder and combiner in massive MIMO mm-Wave communication systems. Specifically, the joint optimization of the precoder and combiner is carried out by means of two convolutional neural networks (CNN) and through going into two stages of operation, namely training and prediction stages. The MATLAB simulation results show that the deep learning-based hybrid beamforming approach for the mm-Wave massive MIMO outperforms the legacy optimization-based hybrid beamforming approaches in terms of spectrum efficiency.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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:604180095
Book学术官方微信