Haque Md Ashraful, Saha Dipon, Al-Bawri Samir Salem, Paul Liton Chandra, Rahman Md Afzalur, Alshanketi Faisal, Alhazmi Ali, Rambe Ali Hanafiah, Zakariya M A, Ba Hashwan Saeed S
Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 32610, Perak, Malaysia.
Department of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1341, Bangladesh.
Heliyon. 2023 Sep 1;9(9):e19548. doi: 10.1016/j.heliyon.2023.e19548. eCollection 2023 Sep.
In this study, we have presented our findings on the deployment of a machine learning (ML) technique to enhance the performance of LTE applications employing quasi-Yagi-Uda antennas at 2100 MHz UMTS band. A number of techniques, including simulation, measurement, and a model of an RLC-equivalent circuit, are discussed in this article as ways to assess an antenna's suitability for the intended applications. The CST simulation gives the suggested antenna a reflection coefficient of -38.40 dB at 2.1 GHz and a bandwidth of 357 MHz (1.95 GHz-2.31 GHz) at a -10 dB level. With a dimension of 0.5350.714, it is not only compact but also features a maximum gain of 6.9 dB, a maximum directivity of 7.67, VSWR of 1.001 at center frequency and a maximum efficiency of 89.9%. The antenna is made of a low-cost substrate, FR4. The RLC circuit, sometimes referred to as the lumped element model, exhibits characteristics that are sufficiently similar to those of the proposed Yagi antenna. We use yet another supervised regression machine learning (ML) technique to create an exact forecast of the antenna's frequency and directivity. The performance of machine learning (ML) models can be evaluated using a variety of metrics, including the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE). Out of the seven ML models, the linear regression (LR) model has the lowest error and maximum accuracy when predicting directivity, whereas the ridge regression (RR) model performs the best when predicting frequency. The proposed antenna is a strong candidate for the intended UMTS LTE applications, as shown by the modeling results from CST and ADS, as well as the measured and forecasted outcomes from machine learning techniques.
在本研究中，我们展示了关于部署机器学习（ML）技术以提升在2100 MHz UMTS频段采用准八木 - 宇田天线的LTE应用性能的研究结果。本文讨论了多种技术，包括仿真、测量以及RLC等效电路模型，作为评估天线是否适用于预期应用的方法。CST仿真显示，所建议的天线在2.1 GHz时的反射系数为 -38.40 dB，在 -10 dB水平下的带宽为357 MHz（1.95 GHz - 2.31 GHz）。其尺寸为0.535×0.714，不仅紧凑，而且具有6.9 dB的最大增益、7.67的最大方向性、中心频率处1.001的电压驻波比以及89.9%的最大效率。该天线由低成本基板FR4制成。RLC电路，有时也称为集总元件模型，其特性与所提出的八木天线的特性足够相似。我们还使用了另一种监督回归机器学习（ML）技术来精确预测天线的频率和方向性。机器学习（ML）模型的性能可以使用多种指标进行评估，包括方差得分、R平方、均方误差（MSE）、平均绝对误差（MAE）、均方根误差（RMSE）和均方对数误差（MSLE）。在七个ML模型中，线性回归（LR）模型在预测方向性时误差最低且准确性最高，而岭回归（RR）模型在预测频率时表现最佳。CST和ADS的建模结果以及机器学习技术的测量和预测结果表明，所提出的天线是预期的UMTS LTE应用的有力候选者。
