Design of Ultra-Wideband Antenna Based on Gaussian Process Regression and Genetic Algorithms

Authors

  • Da Li Mi Shaoxing Integrated Circuit Institute & School of Electronics and Information Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
  • Xi Wang Dai Shaoxing Integrated Circuit Institute & School of Electronics and Information Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China, State Key Laboratory of Millimeter Waves Southeast University, Nanjing 210096, China
  • Jun Shi Zhao Shaoxing Integrated Circuit Institute & School of Electronics and Information Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
  • Ze Li Shaoxing Integrated Circuit Institute & School of Electronics and Information Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
  • Gang Li Hubei Key Laboratory of Low Dimensional Optoelectronic Materials and Devices Hubei University of Arts and Science, Xiangyang 441053, China
  • Hui Hong Shaoxing Integrated Circuit Institute & School of Electronics and Information Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China

DOI:

https://doi.org/10.13052/2025.ACES.J.401204

Keywords:

Genetic Algorithms (GA), Gaussian Process Regression (GPR), Ultra-wideband (UWB) antenna

Abstract

Gaussian Process Regression (GPR) algorithm and Genetic Algorithms (GA) are used to design a scheme suitable for antenna optimization in this paper. GPR algorithms are used to build so-called surrogate models, or machine learning models, to replace full-wave simulation calculations to save time. GA is used to find the optimal solution that satisfies the optimization objective. The optimal solution obtained by GA is re-calculated with full-wave electromagnetic simulation. The surrogate model can be updated with new data when the full-wave simulation results don’t meet the target. An ultra-wideband (UWB) antenna is designed by using this optimization scheme. Six structural parameters of the UWB antenna are used to optimize the design, and a total of 10 groups are used to train the surrogate model. Finally, the optimization is completed through 7 iterations. Finally, the UWB antenna is analyzed, fabricated and tested, which shows an operating frequency band of 2.95–11.43 GHz, and a physical size of 30×27×1.6 mm3.

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Author Biographies

Da Li Mi, Shaoxing Integrated Circuit Institute & School of Electronics and Information Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China

Da Li Mi was born in Haining, Zhejiang, China. He received his master’s degree in electronic information from Hangzhou Dianzi University, Hangzhou, Zhejiang, in 2023. His current interests include artificial intelligence, antennas, and electromagnetic compatibility.

Xi Wang Dai, Shaoxing Integrated Circuit Institute & School of Electronics and Information Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China, State Key Laboratory of Millimeter Waves Southeast University, Nanjing 210096, China

Xi Wang Dai was born in Caoxian, Shandong, China. He received the B.S. and M.S. degrees in Electronic Engineering from Xidian University, Xi’an, Shanxi, in 2005 and 2008, and he received the Ph.D. degree in Electromagnetic Fields and Microwave Technology from Xidian University in 2014. From March 2008 to August 2011, he worked at Guangdong Huisu Corporation as a manager of the antenna department. He currently works at Hangzhou Dianzi University, Hangzhou. His current research interests involve metamaterials, AI-assisted design antenna, MIMO antenna and low-profile antenna.

Jun Shi Zhao, Shaoxing Integrated Circuit Institute & School of Electronics and Information Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China

Jun Shi Zhao was born in Zhoukou, Henan, China. He received the B.S. degree in Electronic Information Engineering from Xi’an Shiyou University, Xi’an, Shanxi, in 2023. He is currently pursuing the master’s degree with Hangzhou Dianzi University, Hangzhou, Zhejiang. His current research interests include broadband antenna and microstrip antenna.

Ze Li, Shaoxing Integrated Circuit Institute & School of Electronics and Information Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China

Ze Li was born in Wenzhou, Zhejiang, China, in 2001. He received the B.S. degree in electronic information engineering from Hangzhou Dianzi University Information Engineering College, Hangzhou, in 2023. He is currently pursuing the M.S. degree in the Hangzhou Dianzi University, Hangzhou. His current research interests include orbital angular momentum and metasurface antenna.

Gang Li, Hubei Key Laboratory of Low Dimensional Optoelectronic Materials and Devices Hubei University of Arts and Science, Xiangyang 441053, China

Gang Li was born in Suizhou, Hubei, China. He received the B.S. degrees in electronic engineering from Xidian University, Xi’an, Shaanxi, in 2005, and the Ph.D. degree in electromagnetic fields and microwave technology at Xidian University in 2009. From May 2010 to August 2012, he worked at Huawei Technologies Co. Ltd. as an EMC Engineer. He currently works at Hubei University of Arts and Science, Xiangyang. His current research interests involve broadband antenna, RCS and metasurface.

Hui Hong, Shaoxing Integrated Circuit Institute & School of Electronics and Information Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China

Hui Hong received the Ph.D. degree in electronic science and technology from the College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, China, in 2007. Currently, he is a Full Professor with the School of Electronics and Information Engineering, Hangzhou Dianzi University, Hangzhou. His research interests include brain–computer interface, microsystem integration, and biological signal processing.

References

J. Y. Park and J. Choo, “Zigzag antenna design based on machine learning,” Applied Computational Electromagnetics Society (ACES) Journal, vol. 40, no. 01, pp. 20–25, Jan. 2025.

J. Dou, “Convolutional neural networks aided reinforcement learning for accelerated optimization of antenna topology,” Applied Computational Electromagnetics Society (ACES) Journal, vol. 40, no. 01, pp. 35–41, Jan. 2025.

Y. Chen, V. Demir, S. Bhupatiraju, A. Z. Elsherbeni, J. Gavilan, and K. Stoynov, “Prediction of antenna performance based on scalable data-informed machine learning methods,” Applied Computational Electromagnetics Society (ACES) Journal, vol. 39, no. 04, pp. 275–290, Apr. 2024.

E. Demircioglu, M. H. Sazlı, S. Taha İmeci, and O. Sengul, “Soft computing techniques on multi-resonant antenna synthesis and analysis,” Microwave and Optical Technology Letters, vol. 55, no. 11 pp. 2643–2648, Nov. 2013.

L. W. Mou, Y. J. Cheng, Y. F. Wu, M. H. Zhao, and H. N. Yang, “Design for array-fed beam-scanning reflector antennas with maximum radiated power efficiency based on near-field pattern synthesis by support vector machine,” IEEE Transactions on Antennas and Propagation, vol. 70, no. 7, pp. 5035–5043, July 2022.

D. R. Prado, P. Naseri, J. A. López-Fernández, S. V. Hum, and M. Arrebola, “Support vector regression-enabled optimization strategy of dual circularly-polarized shaped-beam reflectarray with improved cross- polarization performance,” IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 497–507, Jan. 2023.

X. W. Dai, D. L. Mi, H. T. Wu, and Y. H. Zhang, “Design of compact patch antenna based on support vector regression,” Radioengineering, vol. 31, no. 3, pp. 339–345, Sep. 2022.

S. Koziel, N. Çalık, P. Mahouti, and M. A. Belen, “Accurate modeling of antenna structures by means of domain confinement and pyramidal deep neural networks,” IEEE Transactions on Antennas and Propagation, vol. 70, no. 3, pp. 2174–2188, Mar. 2022.

A. M. Montaser and K. R. Mahmoud, “Deep learning-based antenna design and beam-steering capabilities for millimeter-wave applications,” IEEE Access, vol. 9, pp. 145583–145591, 2021.

L. Cui, 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, pp. 6858–6866, Oct. 2020.

J. Jacobs and J. de Villiers, “Modeling of planar dual-band microstrip patch antenna using Gaussian process regression,” in The 3rd European Wireless Technology Conference, Paris, France, pp. 253–256, 2010.

J. P. Jacobs, “Efficient resonant frequency modeling for dual-band microstrip antennas by Gaussian process regression,” IEEE Antennas and Wireless Propagation Letters, vol. 14, pp. 337–341, 2015.

B. Woosley, J. Twigg, J. G. Rogers, and F. Dagefu, “Learning antenna radiation patterns through Gaussian process regression,” in 2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI), Denver, CO, USA, pp. 637–638, 2022.

H. Ibili, A. C. Gungor, M. Maciejewski, J. Smajic, and J. Leuthold, “Genetic algorithm-based geometry optimization of terahertz plasmonic modulator antennas,” in 2022 23rd International Conference on the Computation of Electromagnetic Fields (COMPUMAG), Cancun, Mexico, pp. 1–4, 2022.

K. Choi, D.-H. Jang, S.-I. Kang, J.-H. Lee, T.-K. Chung, and H.-S. Kim, “Hybrid algorithm combing genetic algorithm with evolution strategy for antenna design,” in IEEE Transactions on Magnetics, vol. 52, no. 3, pp. 1–4, Mar. 2016.

A. J. Kerkhoff and H. Ling, “Design of a band-notched planar monopole antenna using genetic algorithm optimization,” IEEE Transactions on Antennas and Propagation, vol. 55, no. 3, pp. 604–610, Mar. 2007.

X.W. Dai, W. H. Hu, Y. H. Fu, J. Liu, and Z. X. Chen, “Automatic design of microwave filters by using relational induction neural networks,” Int. J. Numer. Model, vol. 36, no. 6, e3102, 2023.

J. P. Jacobs and J. P. de Villiers, “Gaussian-process-regression-based design of ultrawide-band and dual-band CPW-FED slot antennas,” Journal of Electromagnetic Waves and Applications, vol. 24, no. 13, pp. 1763–1772, 2012.

A. Pietrenko-Dabrowska, S. Koziel, and M. Al-Hasan, “Cost-efficient bi-layer modeling of antenna input characteristics using gradient Kriging surrogates,” IEEE Access, vol. 8, pp. 140831–140839, 2020.

A. Pietrenko-Dabrowska and S. Koziel, “Simulation-driven antenna modeling by means of response features and confined domains of reduced dimensionality,” IEEE Access, vol. 8, pp. 228942–228954, 2020.

S. Koziel and A. Pietrenko-Dabrowska, “Reliable data-driven modeling of high-frequency structures by means of nested Kriging with enhanced design of experiments,” Engineering Computations, vol. 36, no. 7, pp. 2293–2308, 2019.

A. Pietrenko-Dabrowska and S. Koziel, “Reliable surrogate modeling of antenna input characteristics by means of domain confinement and principal components,” Electronics, vol. 9, no. 5, p. 877, 2020.

T. O. Kvålseth, “Cautionary note about R2,” The American Statistician, vol. 39, no. 4, pp. 279–285, 1985.

M. N. Hasan and M. Seo, “A planar 3.4–9 GHz UWB monopole antenna,” in 2018 International Symposium on Antennas and Propagation (ISAP), Busan, Korea (South), pp. 1–2, 2018.

Y. Wang and W. Dou, “A low profile UWB antenna on a large flat conductor,” in 2021 International Symposium on Antennas and Propagation (ISAP), Taipei, Taiwan, pp. 1–2, 2021.

Y.-Y. Liu and Z.-H. Tu, “Compact differential band-notched stepped-slot UWB-MIMO antenna with common-mode suppression,” IEEE Antennas and Wireless Propagation Letters, vol. 16, pp. 593–596, 2017.

P. M. Paul, K. Kandasamy, M. S. Sharawi, and B. Majumder, “Dispersion-engineered transmission line loaded slot antenna for UWB applications,” IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 2, pp. 323–327, Feb. 2019.

B. Tutuncu, H. Torpi, and S. Taha Imeci, “Directivity improvement of microstrip antenna by inverse refraction metamaterial,” Journal of Engineering Research, vol. 7, no. 4, pp. 151–164, Dec. 2019.

S. Salihovic and S. Taha Imeci, “A stub microstrip patch antenna for sub 6 GHz – 5G applications,” Heritage and Sustainable Development Original Research, vol. 5, no. 1, pp. 99–106, June 2023.

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Published

2025-12-30

How to Cite

[1]
D. L. . Mi, X. W. . Dai, J. S. . Zhao, Z. . Li, G. . Li, and H. . Hong, “Design of Ultra-Wideband Antenna Based on Gaussian Process Regression and Genetic Algorithms”, ACES Journal, vol. 40, no. 12, pp. 1169–1177, Dec. 2025.

Issue

2025: Vol 40 Iss 12

Section

General Submission

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