基于深度学习的期望时调制平面阵列基频和谐波波束形成

IF 1.1 4区 计算机科学 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Mohammad Mashayekhi, Hossein Soleimani
{"title":"基于深度学习的期望时调制平面阵列基频和谐波波束形成","authors":"Mohammad Mashayekhi,&nbsp;Hossein Soleimani","doi":"10.1049/mia2.70018","DOIUrl":null,"url":null,"abstract":"<p>In recent years, there has been a significant surge in the utilisation of deep learning and machine learning techniques for addressing complex and time-intensive problems. The significance of employing deep learning becomes increasingly evident as the complexity of the problem increases. In the field of electromagnetics, the utilisation of deep learning techniques has exhibited exceptional efficacy across many applications, especially in wireless communications. In wireless communications, providing a structure that can simultaneously generate multiple beams and beamform them involves complexity and specific constraints. In this article, the time modulation technique is utilised to generate harmonics in the sidebands alongside the fundamental beam in various planar antenna arrays. By demonstrating the nonlinear shaping of the harmonic beams relative to each other, a novel approach is proposed that leverages deep learning techniques for the beamforming of both the fundamental beam and harmonic beams. In this regard, two models are proposed: a deep neural network (DNN) and a convolutional neural network (CNN). The input of CNN is comprised of two-dimensional patterns of the main beam and harmonics. The input to DNN, on the other hand, includes useful details about the main beam and harmonics, such as their scanning angles, side lobe levels and directivities. The output of the models consists of the time modulation parameters of the array elements, including the pulse width and the pulse delay. The results demonstrate that DNN has achieved better accuracy and a shorter processing time in comprehending the relationship between the time modulation of array elements with different array dimensions and the radiation pattern of the fundamental beam and harmonic beams. Additionally, several samples are presented to evaluate the proposed model. The results demonstrate a high level of accuracy in fundamental beamforming, as well as in harmonic beamforming and beam steering.</p>","PeriodicalId":13374,"journal":{"name":"Iet Microwaves Antennas & Propagation","volume":"19 1","pages":""},"PeriodicalIF":1.1000,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/mia2.70018","citationCount":"0","resultStr":"{\"title\":\"Fundamental and Harmonic Beamforming of Desire Time-Modulated Planar Arrays With Deep Learning\",\"authors\":\"Mohammad Mashayekhi,&nbsp;Hossein Soleimani\",\"doi\":\"10.1049/mia2.70018\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In recent years, there has been a significant surge in the utilisation of deep learning and machine learning techniques for addressing complex and time-intensive problems. The significance of employing deep learning becomes increasingly evident as the complexity of the problem increases. In the field of electromagnetics, the utilisation of deep learning techniques has exhibited exceptional efficacy across many applications, especially in wireless communications. In wireless communications, providing a structure that can simultaneously generate multiple beams and beamform them involves complexity and specific constraints. In this article, the time modulation technique is utilised to generate harmonics in the sidebands alongside the fundamental beam in various planar antenna arrays. By demonstrating the nonlinear shaping of the harmonic beams relative to each other, a novel approach is proposed that leverages deep learning techniques for the beamforming of both the fundamental beam and harmonic beams. In this regard, two models are proposed: a deep neural network (DNN) and a convolutional neural network (CNN). The input of CNN is comprised of two-dimensional patterns of the main beam and harmonics. The input to DNN, on the other hand, includes useful details about the main beam and harmonics, such as their scanning angles, side lobe levels and directivities. The output of the models consists of the time modulation parameters of the array elements, including the pulse width and the pulse delay. The results demonstrate that DNN has achieved better accuracy and a shorter processing time in comprehending the relationship between the time modulation of array elements with different array dimensions and the radiation pattern of the fundamental beam and harmonic beams. Additionally, several samples are presented to evaluate the proposed model. The results demonstrate a high level of accuracy in fundamental beamforming, as well as in harmonic beamforming and beam steering.</p>\",\"PeriodicalId\":13374,\"journal\":{\"name\":\"Iet Microwaves Antennas & Propagation\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2025-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/mia2.70018\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Iet Microwaves Antennas & Propagation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/mia2.70018\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iet Microwaves Antennas & Propagation","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/mia2.70018","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

近年来,深度学习和机器学习技术在解决复杂和时间密集型问题方面的应用激增。随着问题复杂性的增加,采用深度学习的重要性变得越来越明显。在电磁学领域,深度学习技术的应用在许多应用中都表现出了非凡的功效,尤其是在无线通信领域。在无线通信中,提供一种可以同时产生多个波束并对其进行波束形成的结构涉及复杂性和特定的限制。在本文中,利用时间调制技术在各种平面天线阵列的基束旁带产生谐波。通过展示谐波光束相对于彼此的非线性形状,提出了一种利用深度学习技术对基束和谐波光束进行波束形成的新方法。在这方面,提出了两种模型:深度神经网络(DNN)和卷积神经网络(CNN)。CNN的输入由主波束的二维图样和谐波组成。另一方面,深度神经网络的输入包括有关主波束和谐波的有用细节,例如它们的扫描角度、旁瓣电平和指向性。模型的输出由阵列元素的时间调制参数组成,包括脉冲宽度和脉冲延迟。结果表明,深度神经网络在理解不同阵列尺寸阵列单元的时间调制与基波束和谐波波束辐射方向图之间的关系方面取得了更好的精度和更短的处理时间。此外,还提供了几个样本来评估所提出的模型。结果表明,该方法在基波波束形成、谐波波束形成和波束转向方面具有较高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Fundamental and Harmonic Beamforming of Desire Time-Modulated Planar Arrays With Deep Learning

Fundamental and Harmonic Beamforming of Desire Time-Modulated Planar Arrays With Deep Learning

In recent years, there has been a significant surge in the utilisation of deep learning and machine learning techniques for addressing complex and time-intensive problems. The significance of employing deep learning becomes increasingly evident as the complexity of the problem increases. In the field of electromagnetics, the utilisation of deep learning techniques has exhibited exceptional efficacy across many applications, especially in wireless communications. In wireless communications, providing a structure that can simultaneously generate multiple beams and beamform them involves complexity and specific constraints. In this article, the time modulation technique is utilised to generate harmonics in the sidebands alongside the fundamental beam in various planar antenna arrays. By demonstrating the nonlinear shaping of the harmonic beams relative to each other, a novel approach is proposed that leverages deep learning techniques for the beamforming of both the fundamental beam and harmonic beams. In this regard, two models are proposed: a deep neural network (DNN) and a convolutional neural network (CNN). The input of CNN is comprised of two-dimensional patterns of the main beam and harmonics. The input to DNN, on the other hand, includes useful details about the main beam and harmonics, such as their scanning angles, side lobe levels and directivities. The output of the models consists of the time modulation parameters of the array elements, including the pulse width and the pulse delay. The results demonstrate that DNN has achieved better accuracy and a shorter processing time in comprehending the relationship between the time modulation of array elements with different array dimensions and the radiation pattern of the fundamental beam and harmonic beams. Additionally, several samples are presented to evaluate the proposed model. The results demonstrate a high level of accuracy in fundamental beamforming, as well as in harmonic beamforming and beam steering.

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
Iet Microwaves Antennas & Propagation
Iet Microwaves Antennas & Propagation 工程技术-电信学
CiteScore
4.30
自引率
5.90%
发文量
109
审稿时长
7 months
期刊介绍: Topics include, but are not limited to: Microwave circuits including RF, microwave and millimetre-wave amplifiers, oscillators, switches, mixers and other components implemented in monolithic, hybrid, multi-chip module and other technologies. Papers on passive components may describe transmission-line and waveguide components, including filters, multiplexers, resonators, ferrite and garnet devices. For applications, papers can describe microwave sub-systems for use in communications, radar, aerospace, instrumentation, industrial and medical applications. Microwave linear and non-linear measurement techniques. Antenna topics including designed and prototyped antennas for operation at all frequencies; multiband antennas, antenna measurement techniques and systems, antenna analysis and design, aperture antenna arrays, adaptive antennas, printed and wire antennas, microstrip, reconfigurable, conformal and integrated antennas. Computational electromagnetics and synthesis of antenna structures including phased arrays and antenna design algorithms. Radiowave propagation at all frequencies and environments. Current Special Issue. Call for papers: Metrology for 5G Technologies - https://digital-library.theiet.org/files/IET_MAP_CFP_M5GT_SI2.pdf
×
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