Araştırma Makalesi

Machine Learning Predictive Wi-Fi Antenna Design for Wireless Communication Technologies

Cilt: 2 Sayı: 2 26 Aralık 2022
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Machine Learning Predictive Wi-Fi Antenna Design for Wireless Communication Technologies

Öz

The use of the Internet has become indispensable with the developing technology. Providing access to the internet, which is the most important part of technology, has become one of the greatest needs. Antennas are the most important stage to access the Internet. Recently, development studies have increased considerably for antennas, which are the first step of wireless communication technology. In this study, a Wi-Fi antenna design with 2.4 GHz operating frequency that can be used in wireless communication technology has been realized. Microstrip antenna has been chosen to meet the necessary needs. While designing the antenna, CST Microwave Studio program has been used and numerical calculations have been made. After the antenna design has been made, machine learning support has been provided. The most effective lengths of the antenna have been determined and parametric analysis has been performed. The results of the selected algorithms have been observed. Based on these results, the algorithm that made the closest prediction has been determined. As a result of this study, an antenna design with a return loss value of 21 dB and a bandwidth value operating between 2.37 GHz - 2.42 GHz frequencies has been carried out. The designed antenna is acceptable according to IEEE 802.11 standards. Decision Tree gave the best prediction result according to machine learning algorithms.

Anahtar Kelimeler

Destekleyen Kurum

TUBITAK

Proje Numarası

1919B012102519

Teşekkür

This study has been carried out using the laboratory facilities of İzmir Katip Celebi University Smart Factory Systems Application and Research Center (AFSUAM). This study is supported by TUBITAK 2209-A University Students Research Projects Support Program within the scope of project numbered 1919B012102519.

Kaynakça

  1. Montero-de-Paz, J., Oprea, I., Rymanov, V., Babiel, S., García-Muñoz, L. E., Lisauskas, A., ... & Carpintero, G. (2013). Compact modules for wireless communication systems in the E-band (71–76 GHz). Journal of Infrared, Millimeter, and Terahertz Waves, 34(3), 251-266.
  2. Rymanov, V., Palandöken, M., Lutzmann, S., Bouhlal, B., Tekin, T., & Stöhr, A. (2012, September). Integrated photonic 71–76 GHz transmitter module employing high linearity double mushroom-type 1.55 μm waveguide photodiodes. In 2012 IEEE International Topical Meeting on Microwave Photonics (pp. 253-256). IEEE.
  3. Palandöken, M., Rymanov, V., Stöhr, A., & Tekin, T. (2012, August). Compact metamaterial-based bias tee design for 1.55 μm waveguide-photodiode based 71–76GHz wireless transmitter. In Progress in Electromagnetics Research Symposium, PIERS.
  4. Nasimuddin, N. , (Ed.). (2011). Microstrip Antennas. IntechOpen. https://doi.org/10.5772/609
  5. Palandoken, M. (2012). Metamaterial-based compact filter design. In Metamaterial. IntechOpen.
  6. H. H. M. Ghouz, M. F. Abo Sree and M. Aly Ibrahim, "Novel Wideband Microstrip Monopole Antenna Designs for WiFi/LTE/WiMax Devices," in IEEE Access, vol. 8, pp. 9532-9539, 2020, doi: 10.1109/ACCESS.2019.2963644.
  7. Patel, R.H., Upadhyaya, T.K., Desai, A.H., & Palandoken, M. (2019). Low Profile Multiband Meander Antenna for LTE/WiMAX/WLAN and INSAT-C Application. AEU - International Journal of Electronics and Communications.
  8. Wegmuller, M., Von Der Weid, J. P., Oberson, P., & Gisin, N. (2000, January). High resolution fiber distributed measurements with coherent OFDR. In Proc. ECOC’00 (Vol. 11, No. 4, p. 109).

Ayrıntılar

Birincil Dil

İngilizce

Konular

Yapay Zeka

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

26 Aralık 2022

Gönderilme Tarihi

29 Temmuz 2022

Kabul Tarihi

26 Ekim 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 2 Sayı: 2

Kaynak Göster

APA
Demırbas, G. (2022). Machine Learning Predictive Wi-Fi Antenna Design for Wireless Communication Technologies. Journal of Artificial Intelligence and Data Science, 2(2), 82-86. https://izlik.org/JA74BK39FE
AMA
1.Demırbas G. Machine Learning Predictive Wi-Fi Antenna Design for Wireless Communication Technologies. Journal of Artificial Intelligence and Data Science. 2022;2(2):82-86. https://izlik.org/JA74BK39FE
Chicago
Demırbas, Gokcen. 2022. “Machine Learning Predictive Wi-Fi Antenna Design for Wireless Communication Technologies”. Journal of Artificial Intelligence and Data Science 2 (2): 82-86. https://izlik.org/JA74BK39FE.
EndNote
Demırbas G (01 Aralık 2022) Machine Learning Predictive Wi-Fi Antenna Design for Wireless Communication Technologies. Journal of Artificial Intelligence and Data Science 2 2 82–86.
IEEE
[1]G. Demırbas, “Machine Learning Predictive Wi-Fi Antenna Design for Wireless Communication Technologies”, Journal of Artificial Intelligence and Data Science, c. 2, sy 2, ss. 82–86, Ara. 2022, [çevrimiçi]. Erişim adresi: https://izlik.org/JA74BK39FE
ISNAD
Demırbas, Gokcen. “Machine Learning Predictive Wi-Fi Antenna Design for Wireless Communication Technologies”. Journal of Artificial Intelligence and Data Science 2/2 (01 Aralık 2022): 82-86. https://izlik.org/JA74BK39FE.
JAMA
1.Demırbas G. Machine Learning Predictive Wi-Fi Antenna Design for Wireless Communication Technologies. Journal of Artificial Intelligence and Data Science. 2022;2:82–86.
MLA
Demırbas, Gokcen. “Machine Learning Predictive Wi-Fi Antenna Design for Wireless Communication Technologies”. Journal of Artificial Intelligence and Data Science, c. 2, sy 2, Aralık 2022, ss. 82-86, https://izlik.org/JA74BK39FE.
Vancouver
1.Gokcen Demırbas. Machine Learning Predictive Wi-Fi Antenna Design for Wireless Communication Technologies. Journal of Artificial Intelligence and Data Science [Internet]. 01 Aralık 2022;2(2):82-6. Erişim adresi: https://izlik.org/JA74BK39FE