Citation

Aliyu Babale, Suleiman and Tan, Kim Geok and Abdul Rahim, Sharul Kamal and Chia, Pao Liew and Musa, Umar and Hamza, Mukhtar Fatihu and Bakhuraisa, Yaser A. and Li, Li Lim (2024) Machine Learning-Based Optimized 3G/LTE/5G Planar Wideband Antenna With Tri-Bands Filtering Notches. IEEE Access, 12. pp. 80669-80686. ISSN 2169-3536

Text Machine Learning-Based Optimized 3G_LTE_5G Planar Wideband Antenna With Tri-Bands Filtering Notches.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

A multiband microstrip-fed wideband (WB) antenna with filtering notches is described for 3G/4G/5G applications. The proposed antenna comprises three notch bands by etching a modified inverted U-slot, a square ring slot, and an interdigital inductor slot on the patch element and feedline. The antenna resonates at 1.9, 2.3, and 3.5 GHz due to the resonant components’ mutual interaction, which eliminates interference at other frequencies. The antenna’s measured S11 are 18.79 dB at 1.9 GHz, −24.8 dB at 2.4 GHz, and −40.6 dB at 3.5 GHz, showing a multiband function with a band-reject level of 0.4 dB. The VSWR is less than two at all resonant frequencies. The effects of altering the notch dimensions on the S11 and VSWR were explored. The antenna was developed and tested using a Rogers RT/Duroid 5880 substrate. The agreement between measured and simulated results was satisfactory. The S11 result was validated using the ADS schematic and machine learning techniques. The proposed triband-notched antenna offers encouraging results, with radiation patterns exhibiting omnidirectional characteristics and effective performance within the required frequencies. Current distribution analysis reveals how notches disrupt surface current and lower radiation at specific frequencies. The antenna’s gain and efficiency performed satisfactorily in the stated frequency ranges.

Item Type:	Article
Uncontrolled Keywords:	Wideband antenna
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7871 Electronics--Materials
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	03 Jul 2024 02:15
Last Modified:	03 Jul 2024 02:15
URII:	http://shdl.mmu.edu.my/id/eprint/12570

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