Search search
Publication Date
to
Vol. 118
Latest Volume
All Volumes
PIERM 139 PIERM 138 PIERM 137 PIERM 136 PIERM 135 PIERM 134 PIERM 133 PIERM 132 PIERM 131 PIERM 130 PIERM 129 PIERM 128 PIERM 127 PIERM 126 PIERM 125 PIERM 124 PIERM 123 PIERM 122 PIERM 121 PIERM 120 PIERM 119 PIERM 118 PIERM 117 PIERM 116 PIERM 115 PIERM 114 PIERM 113 PIERM 112 PIERM 111 PIERM 110 PIERM 109 PIERM 108 PIERM 107 PIERM 106 PIERM 105 PIERM 104 PIERM 103 PIERM 102 PIERM 101 PIERM 100 PIERM 99 PIERM 98 PIERM 97 PIERM 96 PIERM 95 PIERM 94 PIERM 93 PIERM 92 PIERM 91 PIERM 90 PIERM 89 PIERM 88 PIERM 87 PIERM 86 PIERM 85 PIERM 84 PIERM 83 PIERM 82 PIERM 81 PIERM 80 PIERM 79 PIERM 78 PIERM 77 PIERM 76 PIERM 75 PIERM 74 PIERM 73 PIERM 72 PIERM 71 PIERM 70 PIERM 69 PIERM 68 PIERM 67 PIERM 66 PIERM 65 PIERM 64 PIERM 63 PIERM 62 PIERM 61 PIERM 60 PIERM 59 PIERM 58 PIERM 57 PIERM 56 PIERM 55 PIERM 54 PIERM 53 PIERM 52 PIERM 51 PIERM 50 PIERM 49 PIERM 48 PIERM 47 PIERM 46 PIERM 45 PIERM 44 PIERM 43 PIERM 42 PIERM 41 PIERM 40 PIERM 39 PIERM 38 PIERM 37 PIERM 36 PIERM 35 PIERM 34 PIERM 33 PIERM 32 PIERM 31 PIERM 30 PIERM 29 PIERM 28 PIERM 27 PIERM 26 PIERM 25 PIERM 24 PIERM 23 PIERM 22 PIERM 21 PIERM 20 PIERM 19 PIERM 18 PIERM 17 PIERM 16 PIERM 14 PIERM 13 PIERM 12 PIERM 11 PIERM 10 PIERM 9 PIERM 8 PIERM 7 PIERM 6 PIERM 5 PIERM 4 PIERM 3 PIERM 2 PIERM 1
All Journals
PIER PIER B PIER C PIER M PIER Letters PIER Photonics
2023-09-05
Employing Machine Learning Models to Predict Return Loss Precisely in 5G Antenna
By
Rachit Jain,

    Mr. Rachit Jain

    Department of Electronics Engineering

    Madhav Institute of Technology & Science

    India

    Homepage

Vandana Vikas Thakery,

    Dr. Vandana Vikas Thakery

    Department of Electronics and Communication

    Madhav Institute of Technology and Science Gwalior

    India

    Homepage

Pramod Kumar Singhal

    Prof. Pramod Kumar Singhal

    Department of Electronics Engineering

    Madhav Institute of Technology & Science

    India

    Homepage

Progress In Electromagnetics Research M, Vol. 118, 151-161, 2023
doi:10.2528/PIERM23062505 | Google Scholar

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.
Download Full Article (2206) View Full Article volume
Citation
Rachit Jain, Vandana Vikas Thakery, and Pramod Kumar Singhal, "Employing Machine Learning Models to Predict Return Loss Precisely in 5G Antenna," Progress In Electromagnetics Research M, Vol. 118, 151-161, 2023.
doi:10.2528/PIERM23062505
References

1. Balanis, C. A., Antenna Theory Analysis, and Design, Wiley, 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        Google Scholar

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        Google Scholar

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        Google Scholar

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.        Google Scholar

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.        Google Scholar

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.        Google Scholar

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.        Google Scholar

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        Google Scholar

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.        Google Scholar

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        Google Scholar

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        Google Scholar

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.        Google Scholar

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.        Google Scholar

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        Google Scholar

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        Google Scholar

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        Google Scholar

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.        Google Scholar

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        Google Scholar

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        Google Scholar

Download Full Article (2206) View Full Article volume


The EM Academy piers.org jpier.org Who's Who in EM
Copyright © 2022 The Electromagnetics Academy. All Rights Reserved.