Browsing by Author "Abdulkawi, Wazie M."

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Broadband high gain performance MIMO antenna array for 5 G mm-wave applications-based gain prediction using machine learning approach
(Scopus, 2024-10-24) Haque, Md. Ashraful; Ahammed, Md Sharif; Ananta, Redwan A.; Aljaloud, Khaled; Jizat, Noorlindawaty Md; Abdulkawi, Wazie M.; Nahin, Kamal Hossain; -Bawri, Samir Salem Al
This paper presents the findings about implementing a machine learning (ML) technique to optimize the performance of 5 G mm wave applications utilizing multiple-input multiple-output (MIMO) antennas operating at the 28 GHz frequency band. This article examines various methodologies, including simulation, measurement, and the utilization of an RLC-equivalent circuit model, to evaluate the appropriateness of an antenna for its intended applications. In addition to its compact dimensions, the proposed design exhibits a maximum gain of 10.34 dBi, superior isolation exceeding 26 dB, and a broad bandwidth of 16.56 % Centered at 28 GHz and spanning from 25.905 to 30.544 GHz. Another supervised regression machine learning technique is utilized to predict the antenna's gain accurately. Machine learning (ML) models can be assessed by several measures, such as the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and Mean Absolute Percentage Error (MAPE). Among the six machine learning models considered, it is seen that the Gaussian Process Regression (GPR) model exhibits the lowest error and achieves the highest level of accuracy in forecasting gain. The antenna under consideration has promising qualities for its intended use in high-band 5 G applications. This is evidenced by the modelling findings obtained from Computer Simulation Technology (CST) and Advanced Design System (ADS)and the measured and projected results derived using machine learning methodologies.
Broadband High Gain Performance MIMO Antenna Array for 5 g mm-wave Applications-based Gain Prediction Using Machine Learning Approach
(Elsevier, 2024-08-14) Haque, Md. Ashraful; Ahammed, Md Sharif; Ananta, Redwan A.; Aljaloud, Khaled; Jizat, Noorlindawaty Md; Abdulkawi, Wazie M.; Nahin, Kamal Hossain; AlBawri, Samir Salem
This paper presents the findings about implementing a machine learning (ML) technique to optimize the performance of 5 G mm wave applications utilizing multiple-input multiple-output (MIMO) antennas operating at the 28 GHz frequency band. This article examines various methodologies, including simulation, measurement, and the utilization of an RLC-equivalent circuit model, to evaluate the appropriateness of an antenna for its intended applications. In addition to its compact dimensions, the proposed design exhibits a maximum gain of 10.34 dBi, superior isolation exceeding 26 dB, and a broad bandwidth of 16.56 % Centered at 28 GHz and spanning from 25.905 to 30.544 GHz. Another supervised regression machine learning technique is utilized to predict the antenna's gain accurately. Machine learning (ML) models can be assessed by several measures, such as the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and Mean Absolute Percentage Error (MAPE). Among the six machine learning models considered, it is seen that the Gaussian Process Regression (GPR) model exhibits the lowest error and achieves the highest level of accuracy in forecasting gain. The antenna under consideration has promising qualities for its intended use in high-band 5 G applications. This is evidenced by the modelling findings obtained from Computer Simulation Technology (CST) and Advanced Design System (ADS)and the measured and projected results derived using machine learning methodologies.
Machine Learning-Based Technique for Gain and Resonance Prediction of Mid Band 5G Yagi Antenna
(Springer, 2023-08-03) Haque, Md. Ashraful; Rahman, Md Afzalur; Al‑Bawri, Samir Salem; Yusoff, Zubaida; Sharker, Adiba Haque; Abdulkawi, Wazie M.; Saha, Dipon; Paul, Liton Chandra; Zakariya, M. A.
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 0.642λ0×0.583λ0. The antenna has an operating frequency of 3.5 GHz, a return loss of −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.
Miniaturized tri-band integrated microwave and millimeter-wave MIMO antenna loaded with metamaterial for 5G IoT applications
(2024-12-15) Afzalur Rahman, Md; Al-Bawri, Samir Salem; Abdulkawi, Wazie M.; Tariqul Islam, Mohammad
This study presents a revolutionary tri-band combined microwave (MW) and millimeter wave (MMW) multiple-input-multiple-output (MIMO) antenna. The antenna is built on a Rogers RT-5880 substrate with dimensions of 32 × 32 × 1.6 mm³. This integration increased the isolation from 18 dB to 27 dB in the MW band and 28 dB–40 dB in the MMW band. The effective bandwidths for the MW and MMW bands were initially 3.3–3.7 GHz, 5.45–9.2 GHz, and 24–28 GHz, respectively. The MTM increased these bandwidths to 2.6–4.1 GHz, 5.3–11.3 GHz for the MW band, and 22.5–29.3 GHz for the MMW band. In addition, the realized gain increased significantly, from 1.5 dBi, 3.5 dBi, and 6.5 dBi to 4.5 dBi, 6.1 dBi, and 11 dBi at 3.5 GHz, 6.9 GHz, and 28 GHz. The antenna's overall efficiency increased by 10–12 % across all operational bandwidths. The suggested antenna has excellent diversity performance, with an envelope correlation coefficient of less than 0.0002, a diversity gain greater than 9.998, and a channel capacity loss of less than 0.12 bits/s/Hz. The proposed antenna addresses the increasing demand for IoT devices by efficiently supporting both MW and MMW frequency bands, offering high performance in wireless communication and modern infrastructures. The proposed design not only improves connectivity and efficiency in IoT devices but also supports the Sustainable Development Goals (SDGs) by contributing to the development of smart city infrastructure (SDG 11) and fostering innovation and sustainable industrial practices (SDG 9), thereby promoting sustainable development and industrial innovation.
Quasi-Yagi Antenna Design for LTE Applications and Prediction of Gain and Directivity Using Machine Learning Approaches
(Elsevier, 2023-09-01) Haque, Md. Ashraful; Zakariya, M.A.; Al-Bawri, Samir Salem; Yusoff, Zubaida; Islam, Mirajul; Saha, Dipon; Abdulkawi, Wazie M.; Rahman, Md Afzalur; Paul, Liton Chandra
In recent years, improvements in wireless communication have led to the development of microstrip or patch antennas. The article discusses using simulation, measurement, an RLC equivalent circuit model, and machine learning to assess antenna performance. The antenna's dimensions are 1.01 with respect to the lowest operating frequency, the maximum achieved gain is 6.76 dB, the maximum directivity is 8.21 dBi, and the maximum efficiency is 83.05%. The prototype's measured return loss is compared to CST and ADS simulations. The prediction of gain and directivity of the antenna is determined using a different supervised regression machine learning (ML) method. The performance of ML models is measured by the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE), etc. With errors of less than unity and an accuracy of roughly 98%, Ridge regression gain prediction outperforms the other seven ML models. Gaussian process regression is the best method for predicting directivity. Finally, modeling results from CST and ADS, as well as measured and anticipated results from machine learning, reveal that the suggested antenna is a good candidate for LTE.