Graphene-Based Nano-Antennas for Terahertz Communication
Keywords:
FDTD simulation, Graphene, Nano-antennas, Terahertz communication, Tunable devicesAbstract
The exponential growth of wireless communication technologies demands higher data rates, lower latency, and increased spectral efficiency, all of which necessitate exploration into the terahertz (THz) frequency spectrum. However, conventional antenna technologies face significant limitations in operating efficiently at THz frequencies due to size, material constraints, and fabrication challenges. This research investigates the design and performance of graphene-based nano-antennas as a transformative solution for next-generation terahertz communication systems.
Graphene, a two-dimensional allotrope of carbon, possesses extraordinary electrical, thermal, and mechanical properties, including high carrier mobility and tunable surface conductivity. These characteristics make it exceptionally suitable for plasmonic-based nano-antennas capable of operating in the THz regime. In this study, a plasmonic dipole antenna structure made from monolayer graphene is modeled and simulated using the finite-difference time-domain (FDTD) method to evaluate its resonant behavior and radiation characteristics in the 0.1–10 THz range.
Simulation results indicate that the proposed graphene nano-antenna exhibits high radiation efficiency, compact size, and frequency tunability via electrostatic biasing. The antenna achieves a resonant frequency around 2.5 THz with a gain of approximately 2.8 dB and a fractional bandwidth of 4.6%. The study also analyzes the influence of geometrical parameters, substrate materials, and doping levels on antenna performance. Key challenges in fabrication, material stability, and integration into nano-electronic systems are also discussed.
This research demonstrates that graphene-based nano-antennas hold significant promise for enabling ultra-fast, energy-efficient wireless communication in the terahertz band, paving the way for advances in nanoscale internet-of-things (IoT), high-resolution imaging, and beyond-5G networks.
References
H. Tabata, M. R. Asakawa, and S. Yamaguchi, “Accurate phase calibration of multistatic ımaging system for medical and ındustrial applications,” Applied Sciences, vol. 14, no. 22, p. 10671, Nov. 2024, doi: https://doi.org/10.3390/app142210671
S. Bing, K. Chawang, and J.-C. Chiao, “A tuned microwave resonant system for subcutaneous ımaging,” Sensors, vol. 23, no. 6, p. 3090, Mar. 2023, doi: https://doi.org/10.3390/s23063090
A. Naghibi and A. R. Attari, “Near-field radar-based microwave ımaging for breast cancer detection: A study on resolution and ımage quality,” in IEEE Transactions on Antennas and Propagation, vol. 69, no. 3, pp. 1670-1680, March 2021, doi: https://doi.org/10.1109/TAP.2020.3016407
A. Naghibi and A. R. Attari, “Enhancing image quality of single-frequency microwave imaging with a multistatic full-view array based on sidelobe reduction,” Optics Express, vol. 29, no. 14, p. 22479, Jun. 2021, doi: https://doi.org/10.1364/oe.424508
T. Song, X. Yao, L. Wang, Y. Wang, and G. Sun, “Fast factorized Kirchhoff migration algorithm for near-field radar ımaging with sparse MIMO arrays,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14, 2024, Art. no. 5202914, doi: https://doi.org/10.1109/TGRS.2024.3354732
Ehsan Akbari Sekehravani and G. Leone, “Evaluation of the resolution in ınverse scattering of dielectric cylinders for medical applications,” Sensors, vol. 23, no. 16, pp. 7250–7250, Aug. 2023, doi: https://doi.org/10.3390/s23167250
Vaibhav Godase and Jyoti Godase, “Diet prediction and feature ımportance of gut microbiome using machine learning,” Evolution in Electrical and Electronic Engineering, vol. 5, no. 2, pp. 214–219, 2024, Available: https://publisher.uthm.edu.my/periodicals/index.php/eeee/article/view/16120
Z. Wu, “Microwave tomographic imaging for industrial and medical applications,” Proceedings of 2014 3rd Asia-Pacific Conference on Antennas and Propagation, pp. 1312–1315, Jul. 2014, doi: https://doi.org/10.1109/apcap.2014.6992763
V. Godase, A. Mulani, S. Takale, and R. Ghodake, “A holistic review of automatic drip irrigation systems: Foundations and emerging trends,” Journal of Instrumentation and Innovation Sciences, vol. 10, no. 1, pp. 38-47, Apr. 2025. Available: https://matjournals.net/engineering/index.php/JIIS/article/view/1703
V. Godase, A. Mulani, S. Takale, and R. Ghodake, “Comprehensive review on automated field irrigation using soil image analysis and IoT,” Journal of Advanced Electrical Engineering and Devices, vol. 3, no. 1, pp. 46-55, Apr. 2025. Available: https://matjournals.net/engineering/index.php/JAEED/article/view/1713
V. V. Godase, “A comprehensive study of revolutionizing EV charging with solar-powered wireless solutions,” Advance Research in Power Electronics and Devices, vol. 2, no. 1, pp. 23-37, Apr. 2025. Available: https://matjournals.net/engineering/index.php/ARPED/article/view/1752
V. Godase, A. Mulani, A. Pawar, and K. Sahani, “A comprehensive review on PIR sensor-based light automation systems,” International Journal of Image Processing and Smart Sensors, vol. 1, no. 1, pp. 22-29, May 2025. Available: https://matjournals.net/engineering/index.php/JoMMR/article/view/1888
Vaibhav Godase, “Optimized algorithm for face recognition using deepface and multi-task cascaded convolutional network (MTCNN),” Optimum Science Journal, no. 3, pp. 66–74, Jun. 2025, doi: https://doi.org/10.5281/zenodo.15341560
Sanket K Nagane, S. Pawar, and Prof. V. V. Godase, “Cinematica sentiment analysis,” Journal of Image Processing and Intelligent Remote Sensing, no. 23, pp. 27–32, May 2022, doi: https://doi.org/10.55529/jipirs.23.27.32
Godase, Vaibhav, Prashant Pawar, Sanket Nagane, and Sarita Kumbhar. “Automatic railway horn system using node MCU.” Journal of Control & Instrumentation 1 (2024): 11–19. Available: https://journals.stmjournals.com/joci/article=2024/view=150128/
V. Godase, Soham Modi, Vishal Misal, and Shraddha Kulkarni, “LoRaEdge-ESP32 synergy: Revolutionizing farm weather data collection with low-power, long-range IoT”, ARADC, pp. 1–11, Jul. 2025. Available; https://matjournals.net/engineering/index.php/ARADC/article/view/2155
S. K. A., A. Kathiravan, Praveen Mannam, D Akila, B. S. Kumar, and Vaibhav Vilas Godase, “Advanced neural network models for optimal energy management in microgrids with ıntegrated electric vehicles,” pp. 1869–1874, Apr. 2025, doi: https://doi.org/10.1109/ictmim65579.2025.10988248
V. Godase, “Smart energy management in manufacturing plants using PLC and SCADA,” Advance Research in Power Electronics and Devices, vol. 2, no. 2, pp. 14-24, Jul. 2025. Available: https://matjournals.net/engineering/index.php/ARPED/article/view/2137
V. Godase, “Navigating the digital battlefield: An in-depth analysis of cyber-attacks and cybercrime”, International Journal of Data Science, Bioinformatics and Cyber Security, vol. 1, no. 1, pp. 16-27, May 2025. Available:
https://matjournals.net/engineering/index.php/IJDSBCS/article/view/1896
S. Karve, S. Gangonda, G. Birajadar, V. Godase, R. Ghodake, and A. O. Mulani, “Optimized neural network for prediction of neurological disorders,” Communications in Computer and Information Science, pp. 183–191, 2025, doi: https://doi.org/10.1007/978-3-031-88762-8_18
