Machine learning driven design and optimization of a compact dual Port CPW fed UWB MIMO antenna for wireless communication

Abstract In this article, a compact dual port Multiple Input Multiple Output (MIMO) Coplanar Waveguide (CPW) fed Ultra-Wideband (UWB) antenna for the next generation wireless communication using Machine Learning (ML) optimization is presented. It is designed on an FR4 epoxy substrate of 16 × 30 mm2...

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Main Authors: Jayant Kumar Rai, Swati Yadav, Ajay Kumar Dwivedi, Vivek Singh, Pinku Ranjan, Anand Sharma, Somesh Kumar, Stuti Pandey
Format: Article
Language:English
Published: Nature Portfolio 2025-04-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-98933-w
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author Jayant Kumar Rai
Swati Yadav
Ajay Kumar Dwivedi
Vivek Singh
Pinku Ranjan
Anand Sharma
Somesh Kumar
Stuti Pandey
author_facet Jayant Kumar Rai
Swati Yadav
Ajay Kumar Dwivedi
Vivek Singh
Pinku Ranjan
Anand Sharma
Somesh Kumar
Stuti Pandey
author_sort Jayant Kumar Rai
collection DOAJ
description Abstract In this article, a compact dual port Multiple Input Multiple Output (MIMO) Coplanar Waveguide (CPW) fed Ultra-Wideband (UWB) antenna for the next generation wireless communication using Machine Learning (ML) optimization is presented. It is designed on an FR4 epoxy substrate of 16 × 30 mm2 with a thickness of 1.6 mm. A bandwidth of 8.7 GHz (2.78–11.48 GHz) is achieved. It is used for 5G New Radio Bands (n78/n46/n47/n77/n48/ n79/n96), Wi-Fi 5, DSRC, Wi-Fi 6, and Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V), and Vehicle to Network (V2N) in the entire operating band. The proposed antenna is optimized through the different ML algorithms Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). The DT ML algorithms provide a higher accuracy of 99.92% compared to the remaining ML algorithms. A test and fabrication of the suggested antenna is also done. The findings showed that there was a good correlation between measurement and simulation data for several parameters, including S-parameters, radiation patterns, and MIMO parameters like diversity gain (DG), channel capacity loss (CCL), mean effective gain (MEG), envelope correlation coefficients (ECC), and total active reflection coefficients (TARC). Hence, it is suitable for next-generation wireless communication.
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spelling doaj-art-6d02508daea542efacce2d6ce6a062232025-08-20T03:13:54ZengNature PortfolioScientific Reports2045-23222025-04-0115111310.1038/s41598-025-98933-wMachine learning driven design and optimization of a compact dual Port CPW fed UWB MIMO antenna for wireless communicationJayant Kumar Rai0Swati Yadav1Ajay Kumar Dwivedi2Vivek Singh3Pinku Ranjan4Anand Sharma5Somesh Kumar6Stuti Pandey7Department of Electronics and Telecommunication, RKR Government PolytechnicDepartment of Electrical and Electronics, College of Engineering, COER UniversityDepartment of Electronics and Communication Engineering, Nagarjuna College of Engineering and TechnologyDepartment of Electronics and Communication Engineering, Nagarjuna College of Engineering and TechnologyDepartment of Electrical and Electronics Engineering, ABV Indian Institute of Information Technology and ManagementDepartment of Electronics and Communication Engineering, Motilal Nehru National Institute of Technology AllahabadDepartment of Electrical and Electronics Engineering, ABV Indian Institute of Information Technology and ManagementDepartment of Artificial Intelligence & Machine Learning, School of Computer Science & Engineering, Manipal University JaipurAbstract In this article, a compact dual port Multiple Input Multiple Output (MIMO) Coplanar Waveguide (CPW) fed Ultra-Wideband (UWB) antenna for the next generation wireless communication using Machine Learning (ML) optimization is presented. It is designed on an FR4 epoxy substrate of 16 × 30 mm2 with a thickness of 1.6 mm. A bandwidth of 8.7 GHz (2.78–11.48 GHz) is achieved. It is used for 5G New Radio Bands (n78/n46/n47/n77/n48/ n79/n96), Wi-Fi 5, DSRC, Wi-Fi 6, and Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V), and Vehicle to Network (V2N) in the entire operating band. The proposed antenna is optimized through the different ML algorithms Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbor (KNN), and Decision Tree (DT). The DT ML algorithms provide a higher accuracy of 99.92% compared to the remaining ML algorithms. A test and fabrication of the suggested antenna is also done. The findings showed that there was a good correlation between measurement and simulation data for several parameters, including S-parameters, radiation patterns, and MIMO parameters like diversity gain (DG), channel capacity loss (CCL), mean effective gain (MEG), envelope correlation coefficients (ECC), and total active reflection coefficients (TARC). Hence, it is suitable for next-generation wireless communication.https://doi.org/10.1038/s41598-025-98933-wCoplanar waveguide (CPW)MIMOMachine learningUltra-Wide band (UWB)
spellingShingle Jayant Kumar Rai
Swati Yadav
Ajay Kumar Dwivedi
Vivek Singh
Pinku Ranjan
Anand Sharma
Somesh Kumar
Stuti Pandey
Machine learning driven design and optimization of a compact dual Port CPW fed UWB MIMO antenna for wireless communication
Scientific Reports
Coplanar waveguide (CPW)
MIMO
Machine learning
Ultra-Wide band (UWB)
title Machine learning driven design and optimization of a compact dual Port CPW fed UWB MIMO antenna for wireless communication
title_full Machine learning driven design and optimization of a compact dual Port CPW fed UWB MIMO antenna for wireless communication
title_fullStr Machine learning driven design and optimization of a compact dual Port CPW fed UWB MIMO antenna for wireless communication
title_full_unstemmed Machine learning driven design and optimization of a compact dual Port CPW fed UWB MIMO antenna for wireless communication
title_short Machine learning driven design and optimization of a compact dual Port CPW fed UWB MIMO antenna for wireless communication
title_sort machine learning driven design and optimization of a compact dual port cpw fed uwb mimo antenna for wireless communication
topic Coplanar waveguide (CPW)
MIMO
Machine learning
Ultra-Wide band (UWB)
url https://doi.org/10.1038/s41598-025-98933-w
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