Machine learning–assisted performance prediction of graphene–silicon twin-port band-notched wideband antenna for THz 6G communication systems
8 affiliations
- 1School of Computer Science, University of Petroleum and Energy Studies (UPES)
- 2Department of Electronics and Communication Engineering, Cambridge Institute of Technology
- 3Department of Electronics and Communication Engineering, Rajiv Gandhi National Aviation University
- 4Computer Science and Engineering, Galgotias University
- 5Department of Electrical Engineering, Sandip Institute of Engineering and Management
- 6Department of Electronics and Communication Engineering, Government Engineering College
- 7Department of Electronics and Communication Engineering, G L Bajaj Institute of Technology and Management
- 8Department of Data Science and Engineering, Manipal University Jaipur
Abstract
A graphene–silicon-based twin-port terahertz (THz) antenna is proposed and investigated in this work. The antenna employs an aperture-coupled asymmetric ring dielectric resonator configuration to achieve wideband operation in the THz regime. Circular metallic rings integrated with the printed feed structure introduce a band-notch characteristic between 2.75 THz and 3.15 THz for interference suppression. A graphene coating is incorporated to provide frequency tunability, while DGS is utilized to reduce mutual coupling between the antenna ports. The proposed antenna operates efficiently over the 2.2–2.65 THz and 3.3–3.7 THz frequency bands with isolation levels below − 25 dB. The antenna also exhibits stable radiation characteristics, low envelope correlation coefficient (ECC), high diversity gain (DG), low channel capacity loss (CCL), and acceptable total active reflection coefficient (TARC), confirming its suitability for THz MIMO communication systems. Furthermore, deep neural network (DNN) and random forest (RF) ML approaches are employed to predict the |S11| characteristics of the antenna using a large parametric dataset generated through full-wave electromagnetic simulations. The ML models are evaluated using MAE, MSE, RMSE, Variance Score, and R² metrics, demonstrating strong agreement between predicted and simulated results. The proposed antenna provides a compact, tunable, and ML-assisted THz communication solution suitable for future intelligent 6G wireless applications.
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BibTeX
@article{datta2026machine,
title = {{Machine learning–assisted performance prediction of graphene–silicon twin-port band-notched wideband antenna for THz 6G communication systems}},
author = {Goutam Datta and Nagesh Kallollu Narayanaswamy and Asha Verma and Pooja Singh and Sreedhar Jadapalli and Neha K. Saini and Shivesh Tripathi and Ashish Pandey},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-60977-x},
url = {https://doi.org/10.1038/s41598-026-60977-x},
note = {CC BY},
}
RIS
TY - JOUR
TI - Machine learning–assisted performance prediction of graphene–silicon twin-port band-notched wideband antenna for THz 6G communication systems
AU - Goutam Datta
AU - Nagesh Kallollu Narayanaswamy
AU - Asha Verma
AU - Pooja Singh
AU - Sreedhar Jadapalli
AU - Neha K. Saini
AU - Shivesh Tripathi
AU - Ashish Pandey
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-60977-x
UR - https://doi.org/10.1038/s41598-026-60977-x
AB - A graphene–silicon-based twin-port terahertz (THz) antenna is proposed and investigated in this work. The antenna employs an aperture-coupled asymmetric ring dielectric resonator configuration to achieve wideband operation in the THz regime. Circular metallic rings integrated with the printed feed structure introduce a band-notch characteristic between 2.75 THz and 3.15 THz for interference suppression. A graphene coating is incorporated to provide frequency tunability, while DGS is utilized to reduce mutual coupling between the antenna ports. The proposed antenna operates efficiently over the 2.2–2.65 THz and 3.3–3.7 THz frequency bands with isolation levels below − 25 dB. The antenna also exhibits stable radiation characteristics, low envelope correlation coefficient (ECC), high diversity gain (DG), low channel capacity loss (CCL), and acceptable total active reflection coefficient (TARC), confirming its suitability for THz MIMO communication systems. Furthermore, deep neural network (DNN) and random forest (RF) ML approaches are employed to predict the |S11| characteristics of the antenna using a large parametric dataset generated through full-wave electromagnetic simulations. The ML models are evaluated using MAE, MSE, RMSE, Variance Score, and R² metrics, demonstrating strong agreement between predicted and simulated results. The proposed antenna provides a compact, tunable, and ML-assisted THz communication solution suitable for future intelligent 6G wireless applications.
SN - 2045-2322
ER -