Wen Tao Li;Han Qi Li;Lei Li;Yong Qiang Hei;Xiao Wei Shi
{"title":"面向物联网应用的高效机器学习辅助多目标天线设计","authors":"Wen Tao Li;Han Qi Li;Lei Li;Yong Qiang Hei;Xiao Wei Shi","doi":"10.1109/LAWP.2025.3532623","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":51059,"journal":{"name":"IEEE Antennas and Wireless Propagation Letters","volume":"24 5","pages":"1263-1267"},"PeriodicalIF":3.7000,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient Machine Learning-Assisted Multiobjective Antenna Design for Internet-of-Things Applications\",\"authors\":\"Wen Tao Li;Han Qi Li;Lei Li;Yong Qiang Hei;Xiao Wei Shi\",\"doi\":\"10.1109/LAWP.2025.3532623\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":51059,\"journal\":{\"name\":\"IEEE Antennas and Wireless Propagation Letters\",\"volume\":\"24 5\",\"pages\":\"1263-1267\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2025-01-22\",\"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/10849804/\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Antennas and Wireless Propagation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10849804/","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}
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.
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.