Predictive Modeling of Microstrip Antenna Slot Dimensions Using Random Forest Regression
DOI:
https://doi.org/10.51903/elkom.v18i1.2915Keywords:
Machine Learning, Microstrip Antenna , Prediction, Random Forest, RegressionAbstract
This study presents a machine learning approach for predicting the dimensions of microstrip antenna slots based on antenna performance parameters such as frequency, gain, directivity, return loss (S11), radiation efficiency, and VSWR. A two-phase methodology was employed. In the first phase, ten regression algorithms were evaluated, and Random Forest was identified as the most effective model based on Mean Absolute Error (MAE) and R-squared (R²) scores. In the second phase, hyperparameter tuning was conducted using Grid Search to further improve the model’s performance. The optimized Random Forest model demonstrated consistent improvements in predictive accuracy, with R² values increasing across all output variables. These results indicate that the combination of regression-based modeling and systematic hyperparameter tuning is effective for capturing complex relationships in antenna design tasks. The proposed approach offers a promising data-driven alternative for geometric prediction in microstrip antenna development, particularly when analytical models are insufficient.
References
[1] A. Nella and A. S. Gandhi, “A survey on microstrip antennas for portable wireless communication system applications,” in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017, pp. 2156–2165. doi: 10.1109/ICACCI.2017.8126165.
[2] S. Virnave, “Design of Antenna for Multipurpose Wireless Communication: Microstrip Antenna,” International Journal of Science and Research (IJSR), vol. 13, no. 5, pp. 869–873, May 2024, doi: 10.21275/sr24514141704.
[3] M. M. Amrita and M. Iyengar, “Microstrip Antennas For Wireless Communication,” Sep. 2019, doi: http://dx.doi.org/10.2139/ssrn.3456925.
[4] L. A. de Asis, “Multi Purpose Wireless Communication Antenna Design: Microstrip Antenna,” Journal of Research in Science and Engineering, vol. 6, no. 12, pp. 8–12, Dec. 2024, doi: 10.53469/jrse.2024.06(12).02.
[5] K. R. Urgunde, “A review on gain & bandwidth enhancement techniques of microstrip patch antenna,” Int J Eng Sci Res Technol, pp. 684–687, 2014.
[6] S. Ghosh, A. Chakraborty, and S. Chakraborty, “Optimized design of wide band polarization diversified compact microstrip transponder antenna for satellite communication,” Microw Opt Technol Lett, vol. 65, no. 7, pp. 2066–2075, 2023, doi: https://doi.org/10.1002/mop.33669.
[7] Z. Rasin and N. H. H. Khamis, “Proximity coupled stacked configuration for wideband microstrip antenna,” in 2007 Asia-Pacific Conference on Applied Electromagnetics, 2007, pp. 1–4. doi: 10.1109/APACE.2007.4603938.
[8] R. Dass, A. Kumar, N. Kumar, and R. Yadav, “MICROSTRIP ANTENNA: DESIGN ASPECTS,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 1, no. 5, pp. 130–133, Dec. 2012, [Online]. Available: http://warse.org/pdfs/ijatcse01152012.pdf
[9] P. L. Prasanna Kumar, R. Padmasree, K. Kiran, and B. Sudheer, “Machine learning-driven design and performance analysis of microstrip antennas for sub-6 GHz/mm Wave 5G networks,” International Journal of Informatics and Communication Technology (IJ-ICT), vol. 13, no. 3, p. 462, Dec. 2024, doi: 10.11591/ijict.v13i3.pp462-469.
[10] K. Muthumeenakshi and S. Radha, “Comprehensive Analysis on the Performance of Antenna Design Using Machine Learning Techniques,” 2023, pp. 231–241. doi: 10.1007/978-981-99-1410-4_19.
[11] C.-H. Huang, H.-H. Tsao, C.-C. Hsu, A. Ali, and S.-Y. Liao, “Antenna Structure Prediction and Optimization Based on Machine Learning and Grid Search,” in 2024 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition (iWEM), IEEE, Jul. 2024, pp. 1–3. doi: 10.1109/iWEM59914.2024.10649305.
[12] S. Guntupalli, M. S. Alam, and B. Jackson, “Enhanced Antenna Design Accuracy Using a Hybrid Deep Learning Model,” in 2024 IEEE Latin-American Conference on Communications (LATINCOM), IEEE, Nov. 2024, pp. 1–6. doi: 10.1109/LATINCOM62985.2024.10770659.
[13] M. O. Akinsolu, K. K. Mistry, B. Liu, P. I. Lazaridis, and P. Excell, “Machine Learning-assisted Antenna Design optimization: A Review and the State-of-the-art,” in 14th European Conference on Antennas and Propagation (EuCAP), 2020.
[14] K. Raveendra, “Enhancing 5G Sub-3.5 GHz Networks through Optimization of Microstrip Antenna Arrays with Sequential Rotation-Based MIMO using Machine Learning Techniques,” Journal of Electrical Systems, vol. 20, no. 2s, pp. 370–379, Apr. 2024, doi: 10.52783/jes.1149.
[15] Md. F. H. Mihad, M. M. Islam, and F. H. Sojib, “Antenna Parameters Dataset,” 2024, Mendeley Data: 2.
