面向物联网应用的高效机器学习辅助多目标天线设计

IF 3.7 2区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Wen Tao Li;Han Qi Li;Lei Li;Yong Qiang Hei;Xiao Wei Shi
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引用次数: 0

摘要

在这封信中,提出了一个为多目标天线设计量身定制的高效机器学习框架。设计了一种混合神经网络作为训练解码器,具有自动确定最优网络层数的能力。这种适应性极大地加快了天线的电磁(EM)响应产生,消除了对密集的EM模拟的需要。随后,编码器、映射网络和预训练的解码器依次连接,以训练编码器和映射网络,目的是学习与多个指标相关的特征。为此,当多目标发生变化时,我们提出的框架具有促进神经网络快速再训练的优点。针对物联网(IoT)终端的劈裂环谐振器(SRR)负载贴片天线和8元多输入多输出天线,给出了仿真结果来验证所提出的框架。两根天线的带宽分别为6ghz (21.20 GHz ~ 27.20 GHz)和2.76 GHz (3.26 GHz ~ 6.02 GHz)。数值结果表明了该框架的优越性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Machine Learning-Assisted Multiobjective Antenna Design for Internet-of-Things Applications
In this letter, an efficient machine learning framework tailored for multiobjective antenna design is proposed. A hybrid neural network is designed as the decoder for training, possessing the ability to automatically determine the optimal number of network layers. This adaptability vastly speeds up electromagnetic (EM) response generation for antennas, eliminating the need for intensive EM simulations. Subsequently, encoders, mapping networks, and pretrained decoders are sequentially connected to train the encoders and mapping network, with the purpose of learning features associated with multiple metrics. To this end, when the multiobjective changes, our proposed framework has the merit of facilitating swift retraining of the neural network. Simulation results of an split-ring resonator (SRR)-loaded patch antenna and an 8-element multiple-input–multiple-output antenna, both for Internet-of-Things (IoT) terminals, are provided to verify the proposed framework. The two antennas, respectively, achieve bandwidths of 6 GHz (21.20 GHz to 27.20 GHz) and 2.76 GHz (3.26 GHz to 6.02 GHz). Numerical results reveal the superiority and effectiveness of the proposed framework.
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来源期刊
CiteScore
8.00
自引率
9.50%
发文量
529
审稿时长
1.0 months
期刊介绍: IEEE Antennas and Wireless Propagation Letters (AWP Letters) is devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation. These are areas of competence for the IEEE Antennas and Propagation Society (AP-S). AWPL aims to be one of the "fastest" journals among IEEE publications. This means that for papers that are eventually accepted, it is intended that an author may expect his or her paper to appear in IEEE Xplore, on average, around two months after submission.
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