Araştırma Makalesi

Machine Learning Based High Gain Wireless Antenna Design Operating at 5.2GHz Frequency

Cilt: 2 Sayı: 2 26 Aralık 2022
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Machine Learning Based High Gain Wireless Antenna Design Operating at 5.2GHz Frequency

Öz

When it comes to 3G, 4G, and the latest technology 5G, the relevance of wireless communication is growing day by day in the digitalizing and developing globe. The increased usage of wireless communication has highlighted the necessity for wireless communication equipment. When previous elements failed to match the demands, the demand for a superior version arose. WLAN networks are one of the most extensively utilized types of wireless communication. The most crucial components for data flow in this system are antennas. To address the demands and make appropriate transfers, several studies have been conducted. The frequency of the planned antenna is 5.2 GHz. With the integration of machine learning, the prediction system has been activated. It is planned that multiband and wideband antenna designs will be developed to facilitate wireless communications in this vast frequency range. The major goal of this article is to build a single antenna that can operate across several frequency ranges rather than multiple antennas that operate at various frequencies. About 0.33 is reserved for forecasting. An analysis was made on 500 iterations of 3 parameters. The FR-4 substrate with a dielectric coefficient of 4.3 is utilized in this antenna design. Copper have been utilizing as the material for the ground and patch components. 5.2 GHz working frequency, the return loss is 31.24 dB, bandwidth range is 4.9-5.38, gain is 3.57 dB.

Anahtar Kelimeler

Teşekkür

This study has been carried out using the laboratory facilities of İzmir Katip Celebi University Smart Factory Systems Application and Research Center (AFSUAM).

Kaynakça

  1. [1] Montero-de-Paz, Javier, et al. "Compact modules for wireless communication systems in the E-band (71–76 GHz)." Journal of Infrared, Millimeter, and Terahertz Waves 34.3 (2013): 251-266.
  2. [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. [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. [4] Palandöken, M., & Sondas, A. (2014). Compact Metamaterial Based Bandstop Filter. Microwave Journal, 57(10). [5] BAYTÖRE, C., GÖÇEN, C., PALANDÖKEN, M., Kaya, A., & ZORAL, E. Y. (2019). Compact metal-plate slotted WLAN-WIMAX antenna design with USB Wi-Fi adapter application. Turkish Journal of Electrical Engineering & Computer Sciences, 27(6), 4403-4417.
  5. [6] Demırbas, G. & Akar, E. (2022). Design and Interpretation of Microstrip Patch Antenna Operating at 2.4GHz for Wireless WI-FI Application. Avrupa Bilim ve Teknoloji Dergisi , Ejosat Special Issue 2022 (ICAENS-1) , 672-675 . DOI: 10.31590/ejosat.1084151
  6. [7] Akar, E., Akdag, I., & Gocen, C. (2022). Wi-Fi Antenna Design For E-Health Kit Based Biotelemetry Module. ICONTECH INTERNATIONAL JOURNAL, 6(1), 63-67. https://doi.org/10.46291/ICONTECHvol6iss1pp63-67
  7. [8] Demirbas, G., Gocen, C., & Akdag, I. (2022). Micro-strip Patch 2.4 GHz Wi-Fi Antenna Design For WLAN 4G- 5G Application . ICONTECH INTERNATIONAL JOURNAL, 6(1), 68-72. https://doi.org/10.46291/ICONTECHvol6iss1pp68-72
  8. [9] Gocen, C., & Palandoken, M. (2021). Machine Learning Assisted Novel Microwave Sensor Design for Dielectric Parameter Characterization of Water-Ethanol Mixture. IEEE Sensors Journal.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Yapay Zeka

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

26 Aralık 2022

Gönderilme Tarihi

29 Temmuz 2022

Kabul Tarihi

7 Eylül 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 2 Sayı: 2

Kaynak Göster

APA
Akar, E. (2022). Machine Learning Based High Gain Wireless Antenna Design Operating at 5.2GHz Frequency. Journal of Artificial Intelligence and Data Science, 2(2), 94-98. https://izlik.org/JA73YD57SK
AMA
1.Akar E. Machine Learning Based High Gain Wireless Antenna Design Operating at 5.2GHz Frequency. Journal of Artificial Intelligence and Data Science. 2022;2(2):94-98. https://izlik.org/JA73YD57SK
Chicago
Akar, Ekrem. 2022. “Machine Learning Based High Gain Wireless Antenna Design Operating at 5.2GHz Frequency”. Journal of Artificial Intelligence and Data Science 2 (2): 94-98. https://izlik.org/JA73YD57SK.
EndNote
Akar E (01 Aralık 2022) Machine Learning Based High Gain Wireless Antenna Design Operating at 5.2GHz Frequency. Journal of Artificial Intelligence and Data Science 2 2 94–98.
IEEE
[1]E. Akar, “Machine Learning Based High Gain Wireless Antenna Design Operating at 5.2GHz Frequency”, Journal of Artificial Intelligence and Data Science, c. 2, sy 2, ss. 94–98, Ara. 2022, [çevrimiçi]. Erişim adresi: https://izlik.org/JA73YD57SK
ISNAD
Akar, Ekrem. “Machine Learning Based High Gain Wireless Antenna Design Operating at 5.2GHz Frequency”. Journal of Artificial Intelligence and Data Science 2/2 (01 Aralık 2022): 94-98. https://izlik.org/JA73YD57SK.
JAMA
1.Akar E. Machine Learning Based High Gain Wireless Antenna Design Operating at 5.2GHz Frequency. Journal of Artificial Intelligence and Data Science. 2022;2:94–98.
MLA
Akar, Ekrem. “Machine Learning Based High Gain Wireless Antenna Design Operating at 5.2GHz Frequency”. Journal of Artificial Intelligence and Data Science, c. 2, sy 2, Aralık 2022, ss. 94-98, https://izlik.org/JA73YD57SK.
Vancouver
1.Ekrem Akar. Machine Learning Based High Gain Wireless Antenna Design Operating at 5.2GHz Frequency. Journal of Artificial Intelligence and Data Science [Internet]. 01 Aralık 2022;2(2):94-8. Erişim adresi: https://izlik.org/JA73YD57SK