volume | PIER Journals

Abstract

To meet 5G requirements, designing an optimal antenna is challenging due to numerous design factors. Conventional electromagnetic modeling simulators require excessive time and processing power during the antenna design process. Machine learning (ML), an innovative technology, can be used in the domain of antenna design with favorable performance and can resolve problems that the previous conventional methods cannot. The main goal of this work is to create an antenna that operates at 28 GHz, which is a significant 5G band for the 5G futuristic infrastructure revolution, and to predict the return loss of an antenna using some machine learning models like K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XG-Boost), Decision Tree (DT) and Random Forest (RF). On comparing results, all models perform well with over 83% accuracy. However, the Random Forest model predicts return loss with higher accuracy at 90% and lower MSE and MAE values of 1.99 and 0.827, respectively. Moreover, this antenna holds potential for 5G applications and can be efficiently optimized using a machine learning approach, saving valuable time.

1. Balanis, C. A., Antenna Theory Analysis, and Design, Wiley, Hoboken, New Jersey, 2016.

2. Xu, F., Y. Lin, J. Huang, et al. "Big data driven mobile traffic understanding and forecasting: A time series approach," IEEE Transactions on Services Computing, Vol. 9, No. 5, 796-805, Sep. 2016.
doi:10.1109/TSC.2016.2599878

3. He, Y., Y. Chen, L. Zhang, S. Wong, and Z. N. Chen, "An overview of terahertz antennas," China Communications, Vol. 17, No. 7, 124-165, 2020.
doi:10.23919/J.CC.2020.07.011

4. Jain, R., K. Aole, S. Mittal, and P. Ranjan, "An analysis on wireless communication in 6G THz network and their challenges," Terahertz Devices, Circuits and Systems, 167-181, 2022.
doi:10.1007/978-981-19-4105-4_10

5. El Misilmani, H. M. and T. Naous, "Machine learning in antenna design: An overview on machine learning concept and algorithms," 2019 International Conference on High Performance Computing & Simulation (HPCS), IEEE, Dublin, Ireland, Jul. 1, 2019.

6. Jafarieh, A., M. Nouri, and H. Behroozi, "Optimized 5G-MMW compact Yagi-Uda antenna based on machine learning methodology," 2021 29th Iranian Conference on Electrical Engineering (ICEE), IEEE, Tehran, Iran, May 1, 2021.

7. Zhang, L., L. Chen, Z. Yuan, and S. Lan, "Optimization of a metasurface antenna composed of dual T-shaped antenna elements based on machine learning," 2021 International Symposium on Antennas and Propagation (ISAP), IEEE, Taipei, Taiwan, Oct. 1, 2021.

8. Indharapu, S. S., A. N. Caruso, and K. C. Durbhakula, "Supervised machine learning model for accurate output prediction of various antenna designs," 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), IEEE, Denver, CO, USA, Jul. 1, 2022.

9. Goudos, S. K., 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, 96-102, Dec. 2022.
doi:10.1109/MCOM.006.2200124

10. Kumar, S. V., 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.

11. Ramasamy, R. and M. A. Bennet, "An efficient antenna parameters estimation using machine learning algorithms," Progress In Electromagnetics Research C, Vol. 130, 169-181, 2023.
doi:10.2528/PIERC22121004

12. Bird, T. S., "Definition and misuse of return loss [Report of the transactions editor-in-chief]," IEEE Antennas and Propagation Magazine, Vol. 51, No. 2, 166-167, Apr. 2009.
doi:10.1109/MAP.2009.5162049

13. Pavithran, S., S. Viswasom, S. Santhosh Kumar, and J. Asha, "Designing of a 5G multiband antenna using decision tree and random forest regression models," 2021 8th International Conference on Signal Processing and Integrated Networks (SPIN), IEEE, Noida, India, Aug. 1, 2021.

14. Kurniawati, N., D. Novita Nurmala Putri, and Y. Kurnia Ningsih, "Random forest regression for predicting metamaterial antenna parameters," 2020 2nd International Conference on Industrial Electrical and Electronics (ICIEE), IEEE, Lombok, Indonesia, Oct. 1, 2020.

15. Li, W. T., 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, 4953-4964, Jul. 2022.
doi:10.1109/TAP.2022.3157895

16. Cui, L., 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, 6858-6866, Oct. 2020.
doi:10.1109/TAP.2020.3001743

17. Wiyono, S., 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, 50, Aug. 2020.
doi:10.12962/j24775401.v6i2.4360

18. Jain, R., P. Ranjan, P. K. Singhal, and V. V. Thakare, "Estimation of S₁₁ values of patch antenna using various machine learning models," 2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE, Gwalior, India, Dec. 1, 2022.

19. Ranjan, P., 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.
doi:10.2528/PIERM21122802

20. 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, Jun. 2021.
doi:10.1016/j.aeue.2021.153739

Mr. Rachit Jain

Dr. Vandana Vikas Thakery

Prof. Pramod Kumar Singhal