INTELLIGENT MATCHING TECHNIQUE FOR FLEXIBLE ANTENNAS

Olena Semenova

semenova.o.o@vntu.edu.ua
Vinnytsia National Technical University (Ukraine)
https://orcid.org/0000-0001-5312-9148

Andriy Semenov


Vinnytsia National Technical University (Ukraine)
https://orcid.org/0000-0001-9580-6602

Stefan Meulesteen


Montr B.V. (Netherlands)
https://orcid.org/0009-0004-1364-1277

Natalia Kryvinska


Comenius University in Bratislava (Slovakia)
https://orcid.org/0000-0003-3678-9229

Hanna Pastushenko


Vinnytsia National Technical University (Ukraine)
https://orcid.org/0009-0008-1736-0981

Abstract

Flexible antennas have revolutionized the wireless communication as integral components of modern smart devices. Their unique properties are design flexibility, enhanced performance, and seamless implementation in smart devices. However, when designing antennas, multiple conflicting objectives often need to be considered simultaneously. Incorporating artificial neural networks into optimization strategies has shown promising results in antenna design problems. Neural networks can adapt to different and changeable requirements and constraints. That is why they are valuable tools for customizing antennas to specific operating conditions. The utilization of artificial neural networks for the design of flexible antennas enables researchers to expand the design space, optimize antenna characteristics with greater efficiency, and identify innovative solutions that may not be apparent through traditional design methods. In this study, the authors propose to determine required parameters and characteristics of flexible antennas by using Artificial Intelligence techniques, namely fuzzy logic, neural networks, and genetic algorithms. A matching technique based on neural network for designing flexible antennas has been elaborated. A neural network was developed. To train the neural network, several samples of flexible antenna were manufactured and tested. The developed neural network was simulated. Finally, the obtained flexible antenna was tested.


Keywords:

flexible antenna, wearable device, neural network

[1] Aggarwal C. C.: Neural Networks and Deep Learning. Springer International Publishing, 2023 [https://doi.org/10.1007/978-3-031-29642-0].
  Google Scholar

[2] Al-Haddad M. A. S. M., Jamel N., Nordin A. N.: Flexible Antenna: A Review of Design, Materials, Fabrication, and Applications. Journal of Physics: Conference Series 1878(1), 2021, 012068 [https://doi.org/10.1088/1742-6596/1878/1/012068].
  Google Scholar

[3] Arunprasad V., Gupta B., Karthikeyan T., Ponnusamy M.: Hybrid neuro-fuzzy-genetic algorithms for optimal control of autonomous systems. ICTACT Journal on Soft Computing 13(4), 2023, 3015–3020 [https://doi.org/10.21917/ijsc.2023.0424].
  Google Scholar

[4] Bai Z.: Research on Application of Artificial Intelligence in Communication Network. Journal of Physics: Conference Series 2209(1), 2022, 012014 [https://doi.org/10.1088/1742-6596/2209/1/012014].
  Google Scholar

[5] Bhalke D., Paikrao P. D., Anguera J.: Deep Learning-based Beamforming Approach Incorporating Linear Antenna Arrays. Journal of Telecommunications and Information Technology 2(2), 2024, 66–70 [https://doi.org/10.26636/jtit.2024.2.1530].
  Google Scholar

[6] Hamrouni C., Alutaybi A., Chaoui S.: Various Antenna Structures Performance Analysis based Fuzzy Logic Functions. International Journal of Advanced Computer Science and Applications 13(1), 2022 [https://doi.org/10.14569/ijacsa.2022.0130109].
  Google Scholar

[7] Ishaque M., Johar M. G. M., Khatibi A., Yamin M.: A novel hybrid technique using fuzzy logic, neural networks and genetic algorithm for intrusion detection system. Measurement: Sensors 30, 2023, 100933 [https://doi.org/10.1016/j.measen.2023.100933].
  Google Scholar

[8] Islamov I.: Optimization of Broadband Microstrip Antenna Device for 5G Wireless Communication Systems. Transport and Telecommunication Journal 24(4), 2023, 409–422 [https://doi.org/10.2478/ttj-2023-0032].
  Google Scholar

[9] Kahraman C., Onar S., Oztaysi B., Cebi S.: ‎Role of Fuzzy Sets on Artificial Intelligence Methods‎: ‎A literature Review. Transactions on Fuzzy Sets and Systems 1(2), 2023, 158-178 [https://doi.org/10.30495/tfss.2023.1976303.1060].
  Google Scholar

[10] Kayabasi A.: Triangular Ring Patch Antenna Analysis: Neuro-Fuzzy Model for Estimating of the Operating Frequency. ACES Journal 36(11), 2021, 1412–1417 [https://doi.org/10.13052/2021.aces.j.361104].
  Google Scholar

[11] Kirtania S. G., Elger A. W., Hasan Md. R., Wisniewska A., Sekhar K., Karacolak T., Sekhar P. K.: Flexible Antennas: A Review. Micromachines 11(9), 2020, 847 [https://doi.org/10.3390/mi11090847].
  Google Scholar

[12] Korkmaz S., Alibakhshikenari M., Kouhalvandi L.: A Framework for Optimizing Antenna Through Genetic Algorithm-Based Neural Network. Acta Marisiensis. Seria Technologica 20(1), 2023, 49–53 [https://doi.org/10.2478/amset-2023-0009].
  Google Scholar

[13] Kushwah V. S., Tomar G. S.: Design and Analysis of Microstrip Patch Antennas Using Artificial Neural Network. Trends in Research on Microstrip Antennas. InTech, 2017 [https://doi.org/10.5772/intechopen.69522].
  Google Scholar

[14] Lahiani M. A., Raida Z., Veselý J., Olivová J.: Pre-Design of Multi-Band Planar Antennas by Artificial Neural Networks. Electronics 12(6), 2023, 1345 [https://doi.org/10.3390/electronics12061345].
  Google Scholar

[15] Meulesteen S., Semenov A.O., Semenova O., Koval K., Datsiuk D., Fomenko H.: Cellular Lifesaving Flexible Device. 5th International Conference on Nanotechnologies and Biomedical Engineering ICNBME-2021 [https://doi.org/10.1007/978-3-030-92328-0_50].
  Google Scholar

[16] Mohamed N.: Importance of Artificial Intelligence in Neural Network through using MediaPipe. 6th International Conference on Electronics, Communication and Aerospace Technology ICECA, 2022 [https://doi.org/10.1109/iceca55336.2022.10009513].
  Google Scholar

[17] Naganaik M.: Design of Printed Antennas Using Hybrid Soft Computing Methods. International Journal for Research in Applied Science and Engineering Technology – IJRASET 6(4), 2018 [https://doi.org/10.22214/ijraset.2018.4704].
  Google Scholar

[18] Panagiotou S. C., Thomopoulos S. C. A., Capsalis C. N.: Genetic Algorithms in Antennas and Smart Antennas Design Overview: Two Novel Antenna Systems for Triband GNSS Applications and a Circular Switched Parasitic Array for WiMax Applications Developments with the Use of Genetic Algorithms. International Journal of Antennas and Propagation 2014, 729208 [https://doi.org/10.1155/2014/729208].
  Google Scholar

[19] Ramasamy R., Anto Bennet M.: An Efficient Antenna Parameters Estimation Using Machine Learning Algorithms. Progress in Electromagnetics Research C 130, 2023, 169–181 [https://doi.org/10.2528/pierc22121004].
  Google Scholar

[20] Saçın E. S., Durgun A. C.: Neural Network Modeling of Antennas on Package for 5G Applications. 17th European Conference on Antennas and Propagation (EuCAP), Florence, Italy, 2023, 1–5 [https://doi.org/10.23919/EuCAP57121.2023.10133407].
  Google Scholar

[21] Samantaray B., Das K. K., Roy J. S.: Designing Smart Antennas Using Machine Learning Algorithms. Journal of Telecommunications and Information Technology 4, 2023, 46–52 [https://doi.org/10.26636/jtit.2023.4.1329].
  Google Scholar

[22] Semenov A., Pastushenko A., Semenova O., Koval K.: Flexible Antenna for LTE-M1 Wearables. Physical and technological problems of transmission, processing and storage of information in infocommunication systems. IX International Scientific Practical Conference Physical and Technological Problems of Transmission, Processing and Storage of Information in Infocommunication Systems, Chernivtsi-Suceava 2021, 49–50.
  Google Scholar

[23] Semenov A., Semenova O., Meulesteen S.: Flexible Antenna for Cellular IoT Device. IEEE 2nd Ukrainian Microwave Week UkrMW, Ukraine 2022, 293–298 [https://doi.org/10.1109/UkrMW58013.2022.10037036].
  Google Scholar

[24] Semenov A., Semenova O., Meulesteen S., Koval K., Datsiuk D., Fomenko H., Ageyev D.: Cellular IoT Personal Health and Safety Monitoring. IEEE 9th International Conference on Problems of Infocommunications, Science and Technology PIC S&T 2022
  Google Scholar

[https://doi.org/10.1109/picst57299.2022.10238557].
  Google Scholar

[25] Semenov A., Semenova O., Pinaiev B., Kulias R., Shpylovyi O.: Development of a flexible antenna-wristband for wearable wrist-worn infocommunication devices of the LTE standard. Technology audit and production reserves 3(1), 2022, 20–26 [https://doi.org/10.15587/2706-5448.2022.261718].
  Google Scholar

[26] Sharma K., Pandey G. P.: Designing a Compact Microstrip Antenna Using the Machine Learning Approach. Journal of Telecommunications and Information Technology 4, 2020, 44–52
  Google Scholar

[https://doi.org/10.26636/jtit.2020.143520].
  Google Scholar

[27] da Silva I. N., Hernane Spatti D., Andrade Flauzino R., Liboni L. H. B., dos Reis Alves S. F.: Artificial Neural Networks. Springer International Publishing, 2017 [https://doi.org/10.1007/978-3-319-43162-8].
  Google Scholar

[28] Singh P., Panda S. S., Dash J. C., Riscob B., Pathak S. K., Hegde R. S.: Rapid Multi-Objective Inverse Design of Antenna Via Deep Neural Network Surrogate-Driven Evolutionary Optimization. TechRxiv. June 03, 2024 [https://doi.org/10.36227/techrxiv.171742511.11489750/v1].
  Google Scholar

[29] Sohail A.: Genetic Algorithms in the Fields of Artificial Intelligence and Data Sciences. Annals of Data Science 10(4), 2021, 1007–1018 [https://doi.org/10.1007/s40745-021-00354-9].
  Google Scholar

[30] Wang Z., Qin J., Hu Z., He J., Tang, D.: Multi-Objective Antenna Design Based on BP Neural Network Surrogate Model Optimized by Improved Sparrow Search Algorithm. Applied Sciences 12(24), 2022, 12543 [https://doi.org/10.3390/app122412543].
  Google Scholar

[31] Zhang X.-S.: Neural Networks in Optimization. In: Zhang X.-S.: Nonconvex Optimization and Its Applications. Springer US, New York 2013 [https://doi.org/10.1007/978-1-4757-3167-5].
  Google Scholar

[32] Zhang X.-Y., Tian Y.-B., Zheng X.: Antenna Optimization Design Based on Deep Gaussian Process Model. International Journal of Antennas and Propagation 2020, 2154928 [https://doi.org/10.1155/2020/2154928].
  Google Scholar

Download


Published
2024-12-21

Cited by

Semenova, O., Semenov, A., Meulesteen, S., Kryvinska, N., & Pastushenko, H. (2024). INTELLIGENT MATCHING TECHNIQUE FOR FLEXIBLE ANTENNAS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(4), 16–22. https://doi.org/10.35784/iapgos.6500

Authors

Olena Semenova 
semenova.o.o@vntu.edu.ua
Vinnytsia National Technical University Ukraine
https://orcid.org/0000-0001-5312-9148

Authors

Andriy Semenov 

Vinnytsia National Technical University Ukraine
https://orcid.org/0000-0001-9580-6602

Authors

Stefan Meulesteen 

Montr B.V. Netherlands
https://orcid.org/0009-0004-1364-1277

Authors

Natalia Kryvinska 

Comenius University in Bratislava Slovakia
https://orcid.org/0000-0003-3678-9229

Authors

Hanna Pastushenko 

Vinnytsia National Technical University Ukraine
https://orcid.org/0009-0008-1736-0981

Statistics

Abstract views: 0
PDF downloads: 0


License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.