volume | PIER Journals

Abstract

A smart antenna synthesis approach is described as automatically choosing the optimum antenna type and providing the best geometric characteristics under the demands of antenna performance. Different antenna performance characteristics are examined, and using decision tree classifier, the optimal antenna is suggested using an intelligent antenna selection model. Finally, the geometric characteristics of the antenna are given before the fuzzy inference system is developed by merging five primary learners to fully exploit the benefits of each type of learner. Rectangular patch antenna, pyramidal horn antenna, and helical antenna are the three types of antennas that are classified by a decision tree classifier, and the optimal antenna size parameters are determined using a fuzzy inference method. The performance of decision tree classifier measured using accuracy and FIS is measured using Mean Square Error (MSE) and MAPE. The system demonstrates excellent capability in parameter prediction with antenna categorization with a MAPE of less than 5.8% and accuracy over 99%achieved in our proposed method. The recommended methodology might be widely applied in actual smart antenna design.

1. Srivastava, A., H. Gupta, A. K. Dwivedi, K. K. V. Penmatsa, P. Ranjan, and A. Sharma, "Aperture coupled dielectric resonator antenna optimisation using machine learning techniques," AEU --- International Journal of Electronics and Communications, Vol. 154, 154302, 2022, ISSN 1434-8411.
doi:10.1016/j.aeue.2022.154302

2. Singh, O., M. R. Bharamagoudra, H. Gupta, et al. "Microstrip line fed dielectric resonator antenna optimization using machine learning algorithms," Sādhanā, Vol. 47, 226, 2022.
doi:10.1007/s12046-022-01989-x

3. Ashish, P. U. and S. H. Gupta, "Optimization of ultra-wide band antenna by selection of substrate material using artificial neural network," Appl. Phys. A, Vol. 128, 192, 2022.
doi:10.1007/s00339-022-05312-7

4. Nan, J., H. Xie, M. Gao, Y. Song, and W. Yang, "Design of UWB antenna based on improved deep belief network and extreme learning machine surrogate models," IEEE Access, Vol. 9, 126541-126549, 2021.
doi:10.1109/ACCESS.2021.3111902

5. Gao, J., Y. Tian, X. Zheng, and X. Chen, "Resonant frequency modeling of microwave antennas using Gaussian process based on semi supervised learning,", 2020.

6. Sharma, K. and G. P. Pandey, "Efficient modelling of compact microstrip antenna using machine learning," AEU --- International Journal of Electronics and Communications, Vol. 135, 153739, 2021.
doi:10.1016/j.aeue.2021.153739

7. Wu, Q., 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, 3397-3409, May 2020.
doi:10.1109/TAP.2019.2963570

8. Wu, Z., Y. Yang, and Z. Yao, "Multi-parameter modeling with ANN for antenna design," 2018 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting, 2381-2382, 2018.
doi:10.1109/APUSNCURSINRSM.2018.8608587

9. Kayabasi, A., "MLP and KNN algorithm model applications for determining the operating frequency of A-shaped patch antennas," International Journal of Intelligent Systems and Applications in Engineering, Vol. 5, No. 3, 154-157, 2017.
doi:10.18201/ijisae.2017531432

10. Abbasi Layegh, M., C. Ghobadi, and J. Nourinia, "The optimization design of a novel slotted microstrip patch antenna with multi-bands using adaptive network-based fuzzy inference system," Technologies, Vol. 5, 75, 2017.
doi:10.3390/technologies5040075

11. Dhaliwal, B. S. and S. S. Pattnaik, "Development of PSO-ANN ensemble hybrid algorithm and its application in compact crown circular fractal patch antenna design," Wireless Pers Commun., Vol. 96, 135-152, 2017.
doi:10.1007/s11277-017-4157-8

12. Kayabasi, A. and A. Akdagli, "Predicting the resonant frequency of E-shaped compact microstrip antennas by using anfis and SVM," Wireless Pers Commun., Vol. 82, 1893-1906, 2015.
doi:10.1007/s11277-015-2321-6

13. Chen, Y., J. Zhu, Y. Xie, N. Feng, and Q. H. Liu, "Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network," Nanoscale, Vol. 11, No. 19, 9749-9755, 2019.
doi:10.1039/C9NR01315F

14. Jacobs, J. P., "Efficient resonant frequency modeling for dual-band microstrip antennas by Gaussian process regression," IEEE Antennas and Wireless Propagation Letters, Vol. 14, 337-341, 2015.
doi:10.1109/LAWP.2014.2362937

15. Jain, S. K., A. Patnaik, and S. N. Sinha, "Design of custom-made stacked patch antennas: A machine learning approach," Int. J. Mach. Learn. & Cyber., Vol. 4, 189-194, 2013.
doi:10.1007/s13042-012-0084-x

16. Akdagli, A., A. Toktas, A. Kayabasi, and I. Develi, "An application of artificial neural network to compute the resonant frequency of E-shaped compact microstrip antennas," Journal of Electrical Engineering, Vol. 64, No. 5, 317-322, 2013.
doi:10.2478/jee-2013-0046

17. Nakmouche, M. F., A. M. Allam, D. E. Fawzy, and M. Abdalla, "Design and measurement of triple h-slotted DGS printed antenna with machine learning," Progress In Electromagnetics Research Letters, Vol. 101, 117-125, 2021.
doi:10.2528/PIERL21090501

18. Yin, J., Q. Wu, C. Yu, H. Wang, and W. Hong, "Low-sidelobe-level series-fed microstrip antenna array of unequal interelement spacing," IEEE Antennas and Wireless Propagation Letters, Vol. 16, 1695-1698, 2017.
doi:10.1109/LAWP.2017.2666427

19. Kaur, M. and J. S. Sivia, "Giuseppe peano and cantor set fractals based miniaturized hybrid fractal antenna for biomedical applications using artificial neural network and firefly algorithm," Int. J. RF Microwave Comput. Aided Eng., Vol. 30, No. 1, e22000, 2020.
doi:10.1002/mmce.22000

20. Pujara, D., A. Modi, N. Pisharody, and J. Mehta, "Predicting the performance of pyramidal and corrugated horn antennas using ANFIS," IEEE Antennas and Wireless Propagation Letters, Vol. 13, 293-296, 2014.
doi:10.1109/LAWP.2014.2305518

21. Shi, L. P., Q. H. Zhang, S. H. Zhang, C. Yi, and G. X. Liu, "Efficient graphene reconfigurable reflectarray antenna electromagnetic response prediction using deep learning," IEEE Access, Vol. 9, 22671-22678, 2021.
doi:10.1109/ACCESS.2021.3054944

22. Jagadeesh, V. K. and S. S. Kumar, "Design of normal mode helical antenna using neural networks," 2011 IEEE Applied Electromagnetics Conference, AEMC, 1-4, 2011.

23. Wan, G., M. Li, M. Zhang, L. Kang, and L. Xie, "A novel information fusion method of RFID strain sensor based on microstrip notch circuit," IEEE Transactions on Instrumentation and Measurement, Vol. 71, 1-10, Art No. 8002610, 2022.

24. Kim, Y., S. Keely, J. Ghosh, and H. Ling, "Application of artificial neural networks to broadband antenna design based on a parametric frequency model," IEEE Transactions on Antennas and Propagation, Vol. 55, No. 3, 669-674, Mar. 2007.
doi:10.1109/TAP.2007.891564

25. Hamrouni, C., A. Alutaybi, and S. Chaoui, "Various antenna structures performance analysis based fuzzy logic functions," International Journal of Advanced Computer Science and Applications, Vol. 13, No. 1, 2022.
doi:10.14569/IJACSA.2022.0130109

26. Karnaushenko, D. D., D. Karnaushenko, D. Makarov, and O. G. Schmidt, "Compact helical antenna for smart implant applications," NPG Asia Materials, Vol. 7, No. 6, e188-e188, 2015.
doi:10.1038/am.2015.53

27. Furnkranz, J., ``Decision tree," C. Sammut, G. I. Webb, eds., Encyclopedia of Machine Learning, Springer, Boston, MA, 2011.

28. Prayudani, S., A. Hizriadi, Y. Y. Lase, et al. "Analysis accuracy of forecasting measurement technique on random K nearest neighbor, (RKNN) using MAPE and MSE," Proc. J. Phys., Conf., Vol. 1361, No. 1, Art. No. 012089, 2019.

Mr. Rajendran Ramasamy

Dr. Maria Anto Bennet