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High Performance Quad Port Compact MIMO Antenna for 38 GHz 5G Application with Regression Machine Learning Prediction

  • 01-07-2025
  • Research
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Abstract

The article delves into the design and optimization of a high-performance quad-port compact MIMO antenna tailored for 38 GHz 5G applications. It begins with an exploration of microstrip patch antennas, highlighting their advantages and challenges, particularly in achieving suitable bandwidth and impedance matching. The study then focuses on the development of a 38-GHz 5G MIMO antenna, emphasizing its superior performance characteristics such as high gain, wide bandwidth, and exceptional efficiency. The article provides a detailed comparison with existing research, showcasing the proposed antenna's superior metrics in gain, efficiency, and isolation. A significant aspect of the research is the integration of regression machine learning techniques to predict antenna performance, streamlining the design process and enhancing accuracy. The article also discusses the design methodology, parametric studies, and the evolution of the proposed antenna, offering a comprehensive guide from conception to implementation. The use of advanced simulation tools like CST and ADS, along with the development of an RLC equivalent circuit, further validates the antenna's performance. The article concludes with a thorough analysis of the antenna's diversity performance, radiation patterns, and current distribution, underscoring its suitability for high-band 5G applications.
Title
High Performance Quad Port Compact MIMO Antenna for 38 GHz 5G Application with Regression Machine Learning Prediction
Authors
Md Ashraful Haque
Md Sharif Ahammed
Md Shokor A. Rahaman
Md Kawsar Ahmed
Kamal Hossain Nahin
Narinderjit Singh Sawaran Singh
Md Afzalur Rahman
Jafreezal Jaafar
Samir Salem Al-Bawri
Publication date
01-07-2025
Publisher
Springer US
Published in
Journal of Infrared, Millimeter, and Terahertz Waves / Issue 7/2025
Print ISSN: 1866-6892
Electronic ISSN: 1866-6906
DOI
https://doi.org/10.1007/s10762-025-01053-9
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