Design and Comparative Analysis of THz Antenna through Machine Learning for 6G Connectivity
Keywords:
6G, Decision Tree, KNN, Machine Learning, Random Forest, Return Loss, THz Antenna, XG-BoostAbstract
The rise of sixth-generation (6G) technology has become increasingly necessary to meet the growing demand for high-speed internet and the continuous advancements in technology. The development of an optimal antenna design is crucial to attain the required performance and capabilities. Traditional electromagnetic modeling approaches for antenna design are, however, time-consuming and computationally intensive requiring long simulation time and high-end computing systems. Therefore, Machine Learning (ML) technology can be utilized to deal with these limitations in the context of Terahertz (THz) antenna design, which has not been done before. The main objective of this work is to develop an antenna that operates in the THz Band, which is the essential 6G band for the future infrastructure revolution, and to predict and optimize the antenna's return loss using ML models like K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XG-Boost), Decision Tree, and Random Forest and Mean Squared Error (MSE) of 3.816. The findings show that all of these models perform accurately, particularly Random Forest having the highest accuracy of 82% in predicting the return loss. ML offers novel possibilities for the development of optimized and efficient 6G antennas for high-speed communication.
Downloads
References
S. Koenig, D. Lopez-Diaz, J. Antes, F. Boes, R. Henneberger, A. Leuther, A. Tessmann, R. Schmogrow, D. Hillerkuss, R. Palmer, T. Zwick, C. Koos, W. Freude, O. Ambacher, J. Leuthold, and I. Kallfass, “Wireless sub-THz communication system with high data rate,” Nature Photonics, vol. 7, no. 12, pp. 977–981, 2013.
Y. He, Y. Chen, L. Zhang, S. Wong, and Z. N. Chen, “An overview of terahertz antennas,” China Communications, vol. 17, no. 7, pp. 124–165, 2020, doi: https://doi.org/10.23919/J.CC.2020.07.011
V. Petrov, A. Pyattaev, D. Moltchanov, and Y. Koucheryavy, “Terahertz band communications: Applications, research challenges, and standardization activities,” IEEE Xplore, Oct. 01, 2016.
Z. R. M. Hajiyat, A. Ismail, A. Sali, and Mohd. N. Hamidon, “Antenna in 6G wireless communication system: Specifications, challenges, and research directions,” Optik, vol. 231, p. 166415, Apr. 2021, doi: https://doi.org/10.1016/j.ijleo.2021.166415.
M. H. Rahaman, A. Bandyopadhyay, S. Pal, and K. P. Ray, “Reviewing the Scope of THz Communication and a Technology Roadmap for Implementation,” IETE Technical Review, pp. 1–14, Jun. 2020, doi: https://doi.org/10.1080/02564602.2020.1771221.
R. Jain, K. Aole, S. Mittal, and P. Ranjan, “An Analysis on Wireless Communication in 6G THz Network and Their Challenges,” Terahertz Devices, Circuits and Systems, pp. 167–181, 2022, doi: https://doi.org/10.1007/978-981-19-4105-4_10.
A. Bandyopadhyay and A. Sengupta, “A Review of the Concept, Applications, and Implementation Issues of Terahertz Spectral Imaging Technique,” IETE Technical Review, vol. 39, no. 2, pp. 471–489, Jan. 2021, doi: https://doi.org/10.1080/02564602.2020.1865844.
B. Zhang, Y.-X. Guo, H. Zirath, and Y. P. Zhang, “Investigation on 3-D-Printing Technologies for Millimeter- Wave and Terahertz Applications,” Proceedings of the IEEE, vol. 105, no. 4, pp. 723–736, Apr. 2017, doi: https://doi.org/10.1109/jproc.2016.2639520.
V. Lubecke, K. Mizuno, and G. M. Rebeiz, “Micromachining for terahertz applications,” IEEE Transactions on Microwave Theory and Techniques, vol. 46, no. 11, pp. 1821–1831, Nov. 1998, doi: https://doi.org/10.1109/22.734493
H. M. E. Misilmani, T. Naous, and S. K. Al Khatib, “A review on the design and optimization of antennas using machine learning algorithms and techniques,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 30, no. 10, Jul. 2020, doi: https://doi.org/10.1002/mmce.22356.
Sadiq, M., bin Sulaiman, N., Isa, M.M. and Hamidon, M.N., 2022. A Review on Machine Learning in Smart Antenna: Methods and Techniques. TEM Journal, 11(2), p.695. Available: https://www.ceeol.com/search/article-detail?id=1045967.
Russell, Stuart J. Artificial intelligence a modern approach. Pearson Education, Inc., 2010
Q. Wu, H. Wang, and W. Hong, “Multistage Collaborative Machine Learning and its Application to Antenna Modeling and Optimization,” IEEE Transactions on Antennas and Propagation, vol. 68, no. 5, pp. 3397–3409, May 2020, doi: https://doi.org/10.1109/tap.2019.2963570
B. Liu, H. Aliakbarian, Z. Ma, G. A. E. Vandenbosch, G. Gielen, and P. Excell, “An Efficient Method for Antenna Design Optimization Based on Evolutionary Computation and Machine Learning Techniques,” IEEE Transactions on Antennas and Propagation, vol. 62, no. 1, pp. 7–18, Jan. 2014, doi: https://doi.org/10.1109/tap.2013.2283605
J. P. Jacobs, “Efficient Resonant Frequency Modeling for Dual-Band Microstrip Antennas by Gaussian Process Regression,” IEEE Antennas and Wireless Propagation Letters, vol. 14, pp. 337–341, 2015, doi: https://doi.org/10.1109/lawp.2014.2362937.
Y. Sharma, H. H. Zhang, and H. Xin, “Machine Learning Techniques for Optimizing Design of Double T-Shaped Monopole Antenna,” IEEE Transactions on Antennas and Propagation, vol. 68, no. 7, pp. 5658–5663, Jul. 2020, doi: 10.1109/tap.2020.2966051.
S. K. Goudos, P. D. Diamantoulakis, M. A. Matin, P. Sarigiannidis, S. Wan, and G. K. Karagiannidis, “Design of Antennas through Artificial Intelligence: State of the Art and Challenges,” IEEE Communications Magazine, vol. 60, no. 12, pp. 96–102, Dec. 2022, doi: https://doi.org/10.1109/MCOM.006.2200124.
D. Shi, C. Lian, K. Cui, Y. Chen, and X. Liu, “An Antenna Synthesis Method Based on Machine Learning,” IEEE Transactions on Antennas and Propagation, vol. 70, no. 7, pp. 4965–4976, Jul. 2022, doi: https://doi.org/10.1109/tap.2022.3182693
P. Ranjan, A. Maurya, H. Gupta, S. Yadav, and A. Sharma, "Ultra-Wideband CPW Fed Band-Notched Monopole Antenna Optimization Using Machine Learning," Progress In Electromagnetics Research M, Vol. 108, 27-38, 2022.
Mohammad Monirujjaman Khan, Md. Sazzad Hossain, P. Majumder, S. Akter, and Z. Salam, “A review on machine learning and deep learning for various antenna design 110 applications,” vol. 8, no. 4, pp. e09317–e09317, Apr. 2022, doi: https://doi.org/10.1016/j.heliyon.2022.e09317.
Ragedhaksha, Darshini, Shahil, and A. Nehru, “Deep learning-based real-world object detection and improved anomaly detection for surveillance videos,” Materials Today: Proceedings, Jul. 2021, doi: https://doi.org/10.1016/j.matpr.2021.07.064
Mohri, M., Rostamizadeh, A., & Talwalkar, A. Foundations of machine learning. MIT press. 2018.
Shalev-Shwartz, Shai, and Shai Ben-David. Understanding machine learning: From theory to algorithms. Cambridge University Press, 2014.
S. Pavithran, S. Viswasom, S. K. S, and A. J, “Designing of a 5G Multiband Antenna Using Decision Tree and Random Forest Regression Models,” IEEE Xplore, Aug. 01, 2021. https://ieeexplore.ieee.org/document/9566117.
K. Sharma and G. P. Pandey, “Efficient modelling of compact microstrip antenna using machine learning,” AEU - International Journal of Electronics and Communications, vol. 135, p. 153739, Jun. 2021.
S, V. Kumar, A. Pandey, A. Sharma, P. Ranjan, and R. Tripathi, “Machine Learning Assisted Optimization of Dielectric Resonator based mm-Wave MIMO Antenna for 5G Communication System,” Europe PMC, 2022.
R. Ramasamy, and M. A. Bennet, "An Efficient Antenna Parameters Estimation Using Machine Learning Algorithms," Progress In Electromagnetics Research C, Vol. 130, 169-181, 2023
H. Huang, X. Yang, and B. Wang, “Machine-Learning-Based Generative Optimization Method and Its Application to an Antenna Decoupling Design,” IEEE Transactions on Antennas and Propagation, vol. 71, no. 7, pp. 6243–6248, Jul. 2023, doi: https://doi.org/10.1109/tap.2023.3270716.
A. M. Montaser, “Machine Learning Based Design of Pattern Reconfigurable Antenna,” IEEE Access, vol. 11, pp. 33121–33133, Jan. 2023, doi:https://doi.org/10.1109/access.2023.3263581.
Md. Ashraful Haque et al., “Machine Learning-Based Technique for Resonance and Directivity Prediction of UMTS LTE Band Quasi Yagi Antenna,” Heliyon, vol. 9, no. 9, pp. e19548–e19548, Sep. 2023, doi: https://doi.org/10.1016/j.heliyon.2023.e19548.
T. S. Bird, “Definition and Misuse of Return Loss [Report of the Transactions Editor-in-Chief],” IEEE Antennas and Propagation Magazine, vol. 51, no. 2, pp. 166–167, Apr. 2009, doi: 10.1109/map.2009.5162049.
A. Abohmra, “Terahertz antenna design for future wireless communication,” theses.gla.ac.uk, 2022. Available: https://theses.gla.ac.uk/83007/.
“Ansys HFSS | 3D High Frequency Simulation Software,” www.ansys.com. https://www.ansys.com/en-in/products/electronics/ansys-hfss
“Training and Test Sets: Splitting Data | Machine Learning Crash Course,” Google Developers, 2019. https://developers.google.com/machine-learning/crash-course/training-and-test-sets/splitting-data
Python, “Python,” Python.org, 2019. https://www.python.org/
“Regression and Classification | Supervised Machine Learning - GeeksforGeeks,” GeeksforGeeks, Dec. 01, 2017. https://www.geeksforgeeks.org/regression-classification-supervised-machine-learning/
S. Wiyono, D. S. Wibowo, M. F. Hidayatullah, and D. Dairoh, “Comparative Study of KNN, SVM, and Decision Tree Algorithm for Student’s Performance Prediction,” International Journal of Computing Science and Applied Mathematics, vol. 6, no. 2, p. 50, Aug. 2020.
M. R. Khan, C. L. Zekios, S. Bhardwaj, and S. V. Georgakopoulos, “Performance of Random Forest Algorithm in High-Dimensional Surrogate Modeling of Antennas,” 2021 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (APS/URSI), Dec. 2021, doi: https://doi.org/10.1109/aps/ursi47566.2021.9703847.
W. T. Li, H. S. Tang, C. Cui, Y. Q. Hei, and X. W. Shi, “Efficient Online Data-Driven Enhanced-XGBoost Method for Antenna Optimization,” IEEE Transactions on Antennas and Propagation, vol. 70, no. 7, pp. 4953–4964, Jul. 2022, doi: 10.1109/TAP.2022.3157895.
L. Cui, Y. Zhang, R. Zhang, and Q. H. Liu, “A Modified Efficient KNN Method for Antenna Optimization and Design,” IEEE Transactions on Antennas and Propagation, vol. 68, no. 10, pp. 6858–6866, Oct. 2020, doi: https://doi.org/10.1109/tap.2020.3001743.
“Google Colaboratory,” colab.research.google.com. https://research.google.com/colaboratory
