Pinku Ranjan, Harshit Gupta, Swati Yadav, Anand Sharma

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Machine learning assisted optimization (MLAO) has become very important for improving the antenna design process because it consumes much less time than the traditional methods. These models' accountability can be checked by the accuracy metrics, which tell about the correctness of the predicted result. Machine learning (ML) methods, such as Gaussian Process Regression, Artificial Neural Networks (ANNs), and Support Vector Machine (SVM), are used to simulate the antenna model to predict the reflection coefficient faster. This paper presents the optimization of Hybrid Dielectric Resonator Antenna (DRA) using machine learning models. Several regression models are applied to the dataset for optimization, and the best results are obtained using a random forest regression model with the accuracy of 97%. Additionally, the effectiveness of machine learning based antenna design is demonstrated through comparison with conventional design methods.


Dielectric Resonator Antenna, Machine Learning, Gaussian Process Regression, ANNs, SVM

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Q. Wu, Y. Cao, H. Wang and W. Hong, "Machine-learning-assisted optimization and its application to antenna designs: Opportunities and challenges", China Commun., vol. 17, pp. 152-164, 2020.

G. K. Uyanik and N. Guler, "A Study on Multiple Linear Regression Analysis", In Proceedings of the 4th International Conference on New Horizons in Education, 2013, pp. 1-6.

K. C. Lee, "Application of neural network and its extension of derivative to scattering from a nonlinearly loaded antenna", IEEE Trans. Antennas Propag., vol. 55, pp. 990-993, 2007.

K. C. Lee and T. N. Lin, "Application of neural network to analyses of nonlinearly loaded antenna arrays including mutual coupling effects", IEEE Trans. Antennas Propag., vol. 53, pp. 1126-1132, 2005.

Y. Rahmat-Samii, J. M. Kovitz and H. Rajagopalan, "Nature-inspired optimization techniques in communication antenna designs", Proc. IEEE, vol. 100, pp. 2132–2144, 2012.

W.-Q. Wang, H. Shao and J. Cai, "MIMO Antenna Array Design with Polynomial Factorization", Int. J. Antennas Propag., vol. 2013, p. 358413, 2013.

A. Sharma, G. Das, S. Gupta and R. K. Gangwar, "Quad-band quad-sense circularly polarized dielectric resonatorantenna for GPS/CNSS/WLAN/WiMAX applications", IEEE Antennas Propag. Mag., vol. 60, pp. 57-65, 2018.

A. Gupta and R. K. Gangwar, "Hybrid rectangular dielectric resonator antenna for multiband applications", IETE Tech. Rev., vol. 37, pp. 83-90, 2020.

A. Sharma, P. Ranjan and Sikandar, "Dual Band Ring Shaped Dielectric Resonator Based Radiator with Left and Right Handed Sense Circularly Polarized Features", IETE Tech. Rev., vol 38, pp. 511-519, 2020.

A. K. Dwivedi, A. Sharma and P. Ranjan, "Dual-Band Modified Rectangular Shaped Dielectric Resonator Antenna with Diversified Polarization Feature", Int. J. Circuit Theory Appl., vol. 49, pp. 3434-3442, 2021.

T. Suryakanthi, "Evaluating the Impact of GINI Index and Information Gain on Classification using Decision Tree Classifier Algorithm", Int. J. Adv. Comput. Sci. Appl., vol. 11, pp. 612-619, 2020.

Y. Sharma, H. H. Zhang and H. Xin, "Machine Learning Techniques for Optimizing Design of Double T-shaped Monopole Antenna", IEEE Trans. Antennas Propag., vol. 68, pp. 5658-5663, 2020.

J. Gao, Y. Tian and X. Chen, "Antenna Optimization Based on Co-Training Algorithm of Gaussian Process and Support Vector Machine", IEEE Access, vol. 8, pp. 211380-211390, 2020.

J. P. Jacob, "Efficient resonant frequency modeling for dual-band microstrip antennas by Gaussian process regression", IEEE Antennas Wirel. Propag. Lett., vol. 14, pp. 337-341, 2014.

P. Burrascano, S. Fiori and M. Mongiardo, "A review of artificial neural networks applications in microwave computer‐aided design", Int. J. RF Microw. C. E., invited article, vol. 9, pp. 158-174, 1999.

G. Min and Y. Feng, "Calculation of the characteristic impedance of TEM horn antenna using support vector machine", In Proceedings of the International Conference on Microwave and Millimeter Wave Technology, 2010, pp. 895-897.

J. Gao, Y. Tian, X. Zheng, X. Chen and M. Mrugalski, "Resonant frequency modeling of microwave antennas using gaussian process based on semisupervised learning", Complexity, vol. 2020, p. 3485469, 2020.

P. Ranjan, A. Maurya, H. Gupta, S. Yadav and A. Sharma, "Ultra-Wideband CPW Fed Band-Notched Monopole Antenna Optimization Using Machine Learning", Prog. Electromagn. Res. M, vol. 108, pp. 27-39, 2022.


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