Obtaining wide bandwidth with higher-order TM modes merging in a graphene-based logarithmic antenna for THz sensing applications

Yadav, Reena; Pandey, V. S.; Kumar, Sandeep; Gotra, Shailza

doi:10.1016/j.micrna.2022.207344

Cited by 4 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The antenna is a Yagi-Uda antenna, consists of five metallic cylindrical dielectric resonator (CDR) connected to the Reflector, Dipole and Directors parasitically for increasing the gain, while three stubs are connected to it at its backside with ground plane. The stubs at the backside connected to the Ground are in work for the bandwidth improvements [21]. The antenna is designed on a FR-4 (Lossy) substrate (ε r = 4.…”

Section: Design Methodologymentioning

confidence: 99%

Machine Learning-Based Approach for bandwidth and frequency Prediction for N77 band 5G Antenna

Ashraful Haque,

Afzalur Rahman,

Salem Al-Bawri

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

Yagi antennas are useful for wireless communications because of thedirectional gain they provide, allowing the antenna to concentrate the signal in eitherthe transmission or reception direction. It is built on a substrate made of FR-4,this antenna has a return loss of -46.85 dB at 3.6 GHz and a bandwidth of 3.3-4.2 GHz within a -10 dB range, making it ideal for use in the n77 bands. Notonly is it small, with a size of 0.642λ0×0.583λ0, but it also has a maximum gainof 7.95 dB and a maximum directivity of 8.58 dB. This study investigates severalapproaches to estimating the performance of an antenna. These approaches includesimulation with a variety of software tools, including as CST, HFSS, and AltairFeko; curve fitting technology; and the RLC equivalent circuit model. After that,simulation with CST MWS is used to collect a large amount of data samples, andthen supervised regression machine learning (ML) methods are used to determine theresonance frequency and bandwidth of the antenna. When it comes to predictingbandwidth and frequency, Random Forest Regression demonstrates an exceptional levelof performance, particularly when comparing with the results produced by curve-fittingtools, neural networks, and regression machine learning models. When all of theseconsiderations are taken into account, it is clear that the antenna is an outstandingoption for the n77 band of a 5G communication system.

show abstract

Section: Design Methodologymentioning

confidence: 99%