Yan-Fang Liu;Li-Ye Xiao;Wei Shao;Lin Peng;Qing Huo Liu
{"title":"A Machine Learning-Enabled Radiation-Scattering Integrated Design Approach for Low-Scattering Phased Arrays","authors":"Yan-Fang Liu;Li-Ye Xiao;Wei Shao;Lin Peng;Qing Huo Liu","doi":"10.1109/LAWP.2024.3437436","DOIUrl":null,"url":null,"abstract":"To facilitate a rapid and synchronous design of radiation and scattering characteristics in low-scattering phased arrays, a machine learning (ML)-enabled radiation-scattering integrated design (MLE-RSID) approach is proposed in this letter. In this MLE-RSID approach, an inverse ML model is developed, wherein the radiation and scattering characteristics (|\n<italic>S</i>\n<sub>11</sub>\n| and reflection phase difference) of each two combined array elements are set as inputs, and their geometric parameters as outputs. Utilizing the proposed approach, designers can efficiently achieve phased arrays with on-demand radiation and scattering performances in near real-time. To validate the proposed approach, two antenna elements featuring a wideband scattering characteristic of (180° ± 37°) reflection phase difference and similar radiation characteristics are designed using the MLE-RSID approach, to construct a low-scattering 1 × 10 phased array.","PeriodicalId":51059,"journal":{"name":"IEEE Antennas and Wireless Propagation Letters","volume":"23 12","pages":"4169-4173"},"PeriodicalIF":4.8000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Antennas and Wireless Propagation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10621577/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
To facilitate a rapid and synchronous design of radiation and scattering characteristics in low-scattering phased arrays, a machine learning (ML)-enabled radiation-scattering integrated design (MLE-RSID) approach is proposed in this letter. In this MLE-RSID approach, an inverse ML model is developed, wherein the radiation and scattering characteristics (|
S
11
| and reflection phase difference) of each two combined array elements are set as inputs, and their geometric parameters as outputs. Utilizing the proposed approach, designers can efficiently achieve phased arrays with on-demand radiation and scattering performances in near real-time. To validate the proposed approach, two antenna elements featuring a wideband scattering characteristic of (180° ± 37°) reflection phase difference and similar radiation characteristics are designed using the MLE-RSID approach, to construct a low-scattering 1 × 10 phased array.
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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