Machine Learning Aided Tapered Four-Port MIMO Antenna for V2X Communications With Enhanced Gain and Isolation
In this communication, a 4-port Multiple-Input-Multiple-Output (MIMO) antenna is analyzed and investigated for vehicle-to-everything (V2X) communication centered at 5.9 GHz. The proposed optimized single antenna consists of a tapered radiating antenna with a defective ground structure fed with stepp...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10891790/ |
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| author | Nagesh Kallollu Narayanaswamy Yazeed Alzahrani Krishna Kanth Varma Penmatsa Ashish Pandey Ajay Kumar Dwivedi Vivek Singh Manoj Tolani |
| author_facet | Nagesh Kallollu Narayanaswamy Yazeed Alzahrani Krishna Kanth Varma Penmatsa Ashish Pandey Ajay Kumar Dwivedi Vivek Singh Manoj Tolani |
| author_sort | Nagesh Kallollu Narayanaswamy |
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| description | In this communication, a 4-port Multiple-Input-Multiple-Output (MIMO) antenna is analyzed and investigated for vehicle-to-everything (V2X) communication centered at 5.9 GHz. The proposed optimized single antenna consists of a tapered radiating antenna with a defective ground structure fed with stepped impedance transmission line feed. The proposed 4-port MIMO antenna has a dimension of <inline-formula> <tex-math notation="LaTeX">$96\times 64\times 0.8$ </tex-math></inline-formula> mm3 printed on the FR4 substrate with a relative permittivity of 4.4 and loss tangent of 0.02. To obtain the proposed single antenna unit, parametric analysis, and evolution stages have been investigated and discussed. The impedance bandwidth of the proposed antenna is 5.66 - 6.00 GHz with a peak gain of 7.85 dB and radiation efficiency of 99%. In addition, machine learning techniques such as XG (Extreme Gradient) Boost, Random Forest, and Deep Neural Networks (DNN) were employed in the optimization process to predict and fine-tune the antenna’s design parameters. The stacking ensemble method, combining these models, was used to improve the accuracy of the antenna performance prediction. By leveraging machine learning, the final design was achieved more efficiently, significantly reducing the simulation time and enabling more precise parameter tuning for optimal performance. Further, to validate the MIMO antenna characteristics, different diversity parameters have been calculated such as ECC (Envelope Correlation Coefficient), DG (Diversity Gain), CCL (Channel Capacity Loss), and TARC (Total Active Reflection Coefficient). The fabricated antenna is modeled, and measured findings are found to be in coherence with simulated findings. |
| format | Article |
| id | doaj-art-e06db24f456548c4819e5b7f1b4c1421 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e06db24f456548c4819e5b7f1b4c14212025-08-20T02:15:37ZengIEEEIEEE Access2169-35362025-01-0113324113242310.1109/ACCESS.2025.354318310891790Machine Learning Aided Tapered Four-Port MIMO Antenna for V2X Communications With Enhanced Gain and IsolationNagesh Kallollu Narayanaswamy0Yazeed Alzahrani1https://orcid.org/0000-0001-9671-4398Krishna Kanth Varma Penmatsa2https://orcid.org/0000-0003-2626-5288Ashish Pandey3https://orcid.org/0000-0003-1713-4745Ajay Kumar Dwivedi4https://orcid.org/0000-0003-1146-0902Vivek Singh5https://orcid.org/0000-0001-7569-0104Manoj Tolani6https://orcid.org/0000-0002-3795-0996Department of Electronics and Communication Engineering, Nagarjuna College of Engineering and Technology, Bengaluru, Karnataka, IndiaDepartment of Computer Engineering and Information, College of Engineering, Prince Sattam bin Abdulaziz University, Wadi ad-Dawasir, Saudi ArabiaDepartment of Electronics and Communication Engineering, Sagi Ramakrishnam Raju Engineering College, Bhimavaram, Andhra Pradesh, IndiaDepartment of Data Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, IndiaDepartment of Electronics and Communication Engineering, Nagarjuna College of Engineering and Technology, Bengaluru, Karnataka, IndiaDepartment of Electronics and Communication Engineering, Nagarjuna College of Engineering and Technology, Bengaluru, Karnataka, IndiaDepartment of Information and Communication Technology, Manipal Academy of Higher Education, Manipal, Manipal Institute of Technology, Karnataka, IndiaIn this communication, a 4-port Multiple-Input-Multiple-Output (MIMO) antenna is analyzed and investigated for vehicle-to-everything (V2X) communication centered at 5.9 GHz. The proposed optimized single antenna consists of a tapered radiating antenna with a defective ground structure fed with stepped impedance transmission line feed. The proposed 4-port MIMO antenna has a dimension of <inline-formula> <tex-math notation="LaTeX">$96\times 64\times 0.8$ </tex-math></inline-formula> mm3 printed on the FR4 substrate with a relative permittivity of 4.4 and loss tangent of 0.02. To obtain the proposed single antenna unit, parametric analysis, and evolution stages have been investigated and discussed. The impedance bandwidth of the proposed antenna is 5.66 - 6.00 GHz with a peak gain of 7.85 dB and radiation efficiency of 99%. In addition, machine learning techniques such as XG (Extreme Gradient) Boost, Random Forest, and Deep Neural Networks (DNN) were employed in the optimization process to predict and fine-tune the antenna’s design parameters. The stacking ensemble method, combining these models, was used to improve the accuracy of the antenna performance prediction. By leveraging machine learning, the final design was achieved more efficiently, significantly reducing the simulation time and enabling more precise parameter tuning for optimal performance. Further, to validate the MIMO antenna characteristics, different diversity parameters have been calculated such as ECC (Envelope Correlation Coefficient), DG (Diversity Gain), CCL (Channel Capacity Loss), and TARC (Total Active Reflection Coefficient). The fabricated antenna is modeled, and measured findings are found to be in coherence with simulated findings.https://ieeexplore.ieee.org/document/10891790/MIMOV2XDSRCECCTARCmachine learning |
| spellingShingle | Nagesh Kallollu Narayanaswamy Yazeed Alzahrani Krishna Kanth Varma Penmatsa Ashish Pandey Ajay Kumar Dwivedi Vivek Singh Manoj Tolani Machine Learning Aided Tapered Four-Port MIMO Antenna for V2X Communications With Enhanced Gain and Isolation IEEE Access MIMO V2X DSRC ECC TARC machine learning |
| title | Machine Learning Aided Tapered Four-Port MIMO Antenna for V2X Communications With Enhanced Gain and Isolation |
| title_full | Machine Learning Aided Tapered Four-Port MIMO Antenna for V2X Communications With Enhanced Gain and Isolation |
| title_fullStr | Machine Learning Aided Tapered Four-Port MIMO Antenna for V2X Communications With Enhanced Gain and Isolation |
| title_full_unstemmed | Machine Learning Aided Tapered Four-Port MIMO Antenna for V2X Communications With Enhanced Gain and Isolation |
| title_short | Machine Learning Aided Tapered Four-Port MIMO Antenna for V2X Communications With Enhanced Gain and Isolation |
| title_sort | machine learning aided tapered four port mimo antenna for v2x communications with enhanced gain and isolation |
| topic | MIMO V2X DSRC ECC TARC machine learning |
| url | https://ieeexplore.ieee.org/document/10891790/ |
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