Machine learning-based technique for gain and resonance prediction of mid band 5G Yagi antenna

Abstract

In this study, we present our findings from investigating the use of a machine learning (ML) technique to improve the performance of Quasi-Yagi–Uda antennas operating in the n78 band for 5G applications. This research study investigates several techniques, such as simulation, measurement, and an RLC equivalent circuit model, to evaluate the performance of an antenna. In this investigation, the CST modelling tools are used to develop a high-gain, low-return-loss Yagi–Uda antenna for the 5G communication system. When considering the antenna’s operating frequency, its dimensions are $${0.642}\lambda _0\times {0.583}\lambda _0$$ 0.642 λ 0 × 0.583 λ 0 . The antenna has an operating frequency of 3.5 GHz, a return loss of $$-43.45$$ - 43.45 dB, a bandwidth of 520 MHz, a maximum gain of 6.57 dB, and an efficiency of almost 97%. The impedance analysis tools in CST Studio’s simulation and circuit design tools in Agilent ADS software are used to derive the antenna’s equivalent circuit (RLC). We use supervised regression ML method to create an accurate prediction of the frequency and gain of the antenna. Machine learning models can be evaluated using a variety of measures, including variance score, R square, mean square error, mean absolute error, root mean square error, and mean squared logarithmic error. Among the nine ML models, the prediction result of Linear Regression is superior to other ML models for resonant frequency prediction, and Gaussian Process Regression shows an extraordinary performance for gain prediction. R-square and var score represents the accuracy of the prediction, which is close to 99% for both frequency and gain prediction. Considering these factors, the antenna can be deemed an excellent choice for the n78 band of a 5G communication system.

Design and optimization of a Quasi‐Yagi antenna for 5G communications

“…Huang et al, 13 Elahi et al, 14 Raad, 15 Saadh et al, 16 and Haque et al 17 are used to highlight the significance of the present research with respect to the state‐of‐the‐art Yagi‐Uda antennas and the findings are consolidated in Table 4. Foremost, the antenna development reported in this paper makes use of an optimization algorithm with higher precision compared with Elahi et al, 14 Raad, 15 and Saadh et al 16 Though the antennas presented in Elahi et al, 14 Saadh et al, 16 and Haque et al 17 have higher bandwidth, they are developed on thick substrate with non‐conformal properties. Further, the research in Raad 15 demonstrates a Yagi‐Uda antenna using Kapton Polyimide material with flexible property, the antenna is limited by larger profile size and single frequency operation.…”

A deep learning method for empirical spectral prediction and inverse design of all-optical nonlinear plasmonic ring resonator switches

“…We used the mean squared logarithmic error (MSLE) function for error estimation, which offers advantages over mean squared error (MSE) by providing improved accuracy for positive and continuous predicted values 86 . Mathematically, it can be defined as 87 : …”

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

“…Antenna characteristics like resonant frequency, impedance, gain, and radiation patterns can be predicted with the help of artificial neural networks (ANNs). An antenna's performance in different contexts is largely dependent on these defining traits [36]. An ANN-based strategy models the antenna parameters as functions of several input factors, including the antenna's geometry, the conductor and dielectric materials, and the working frequency [37].…”