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Design and development of ultra-broadband THz metamaterial MIMO antenna with efficient diversity parameters optimized with machine learning for TWPAN applications
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  • Published: 24 February 2026

Design and development of ultra-broadband THz metamaterial MIMO antenna with efficient diversity parameters optimized with machine learning for TWPAN applications

  • Meshari Alsharari1,
  • Yogesh Sharma2,
  • Khaled Aliqab1,
  • Ammar Armghan1,
  • S. K. Patel3 &
  • …
  • Aymen Flah4,5,6 

Scientific Reports , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Engineering
  • Mathematics and computing

Abstract

The high-speed communication development is revolutionizing the way with interact with technology by enabling ultra-fast and intelligent connectivity. There is a need for antenna design that operates with ultrabroadband in the THz regime to be applicable for Terahertz Wireless Persona Area Network (TWPAN) applications. We have proposed an ultra-fast, broadband, and high-gain MIMO antenna design which not only smart but also small in size and low cost to be considered for high-speed communication applications. The designed antenna shows a high gain of 15.7 dBi. The ultrabroadband response gives a bandwidth of 20 THz. The MIMO diversity parameters show the ECC value near 0 and DG of 10 dB. The CCL values are also 0.0083 bits/Hz. Their values show that there is minimal correlation, which means better MIMO performance. The performance is also optimized using parametric optimization and machine learning optimization. The machine learning algorithm gives the highest R2 value of 0.99, which gives a minimum prediction error and higher antenna performance. The THz metamaterial design with optimum diversity parameters makes it a good candidate for TWPAN applications.

Data availability

The data supporting the findings in this work are available from the corresponding author with reasonable request.

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Acknowledgements

This work was funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No. (DGSSR-2025-02-01636).This article has been produced with the financial support of the European Union under the REFRESH – Research Excellence For Region Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition.

Funding

This work was funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No. (DGSSR-2025-02-01636). This article has been produced with the financial support of the European Union under the REFRESH – Research Excellence For Region Sustainability and High-tech Industries project number CZ.10.03.01/00/22_003/0000048 via the Operational Programme Just Transition.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka, 72388, Saudi Arabia

    Meshari Alsharari, Khaled Aliqab & Ammar Armghan

  2. Department of Physics & Environmental Sciences, Sharda School of Engineering & Science, Sharda University, Greater Noida, Uttar Pradesh, 201310, India

    Yogesh Sharma

  3. Department of Computer Engineering- AI & Big Data, Marwadi University, Rajkot, Gujarat, 360003, India

    S. K. Patel

  4. National school of engineering of gabes, University of gabes, gabes, 6072, Tunisia

    Aymen Flah

  5. ENET Centre, CEET, VSB-Technical University of Ostrava, Ostrava, 708 00, Czech Republic

    Aymen Flah

  6. Applied Science Research Center, Applied Science Private university, Amman, 11931, Amman, Jordan

    Aymen Flah

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  1. Meshari Alsharari
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  2. Yogesh Sharma
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Contributions

Methodology, M.A, and Y.S. ,; software, M.A., Y.S., . K.A, and A.A.; investigation, A.F. and S.K.P.; formal Analysis, all authors; writing—original draft preparation, All Authors,; writing—review and editing, All Authors,; All authors have read and agreed to the published version of the manuscript.

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Correspondence to Ammar Armghan or Aymen Flah.

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Alsharari, M., Sharma, Y., Aliqab, K. et al. Design and development of ultra-broadband THz metamaterial MIMO antenna with efficient diversity parameters optimized with machine learning for TWPAN applications. Sci Rep (2026). https://doi.org/10.1038/s41598-026-40351-7

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  • Received: 29 November 2025

  • Accepted: 12 February 2026

  • Published: 24 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-40351-7

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Keywords

  • MIMO
  • TWPAN
  • Antenna
  • Machine learning: THz
  • Diversity parameters: high gain
  • Ultrabroad bandwidth
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