Browsing by Author "Ananta, Redwan A."

Now showing 1 - 5 of 5

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 novel-shaped THz MIMO antenna with a slotted ground plane for future 6G applications
(2024-12-31) Ashraful Haque, Md; Nahin, Kamal Hossain; Nirob, Jamal Hossain; Ananta, Redwan A.; Sawaran Singh, Narinderjit Singh; Chandra Paul, Liton; D. Algarni, Abeer; ElAffendi, Mohammed; A. Ateya, Abdelhamied
This study discusses the results of using a regression machine learning technique to improve the performance of 6G applications that use multiple-input multiple-output (MIMO) antennas operating at the terahertz (THz) frequency band. This research evaluates an antenna’s performance using various methodologies, such as simulation and RLC equivalent circuit models. The suggested design has a broad bandwidth of 2.5 THz and spans from 6.2 to 8.7 GHz, a maximum gain of 14.59 dB, and small dimensions (100 × 300) µm2. It also has outstanding isolation exceeding − 31 dB with 96% efficiency. The ADS allowed us to confirm the accuracy of the CST results by creating a simulated version of the same RLC circuit. Reflection coefficients obtained from the CST and ADS simulators are similar. The supervised regression ML approach is employed accurately to predict the antenna’s potential gain. Several metrics, such as the variance score, R square, mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE), can evaluate machine learning (ML) models. Out of the six machine learning models analyzed, the Extra Tree Regression model demonstrates the lowest error and achieves the highest level of accuracy in predicting gain.
Performance improvement of THz MIMO antenna with graphene and prediction bandwidth through machine learning analysis for 6G application
(2024-12-15) Haque, Md Ashraful; Ananta, Redwan A.; Nirob, Jamal Hossain; Ahammed, Md. Sharif; Sawaran Singh, Narinderjit Singh; Paul, Liton Chandra; Algarni, Abeer D.; ElAffendi, Mohammed; A Ateya, Abdelhamied
This article provides the findings of a study that integrated simulation, an RLC equivalent circuit, and machine learning (ML) techniques to improve wireless indoor communications clusters with future 6 G applications. The antenna being presented is constructed on a polyimide substrate. It exhibits an isolation of 27 dB and has a bandwidth of 4.331 THz, ranging from 0.631 THz to 4.962 THz. Along with its small size (95.52 × 227.24) µm2, it boasts an impressive maximum gain of 13.3 dB and an efficiency rating of 95 %. The ECC value drops below 0.0002 when the DG goes over 9.99. An advanced design system (ADS) creates a model like the proposed MIMO antenna to compare the return loss caused by CST (Computer Simulation Technology). Subsequently, following extensive data sampling with CST MWS (Microwave Studio) simulation, we employed supervised regression ML techniques. Gaussian process regression demonstrates exceptional accuracy, reaching almost 99 %, as evidenced by the high R-square and var scores. Additionally, it achieves the lowest error, less than one, while predicting bandwidth. The proposed antenna demonstrates strong potential as a formidable contender for 6 G THz band applications, as evidenced by the outcomes of the CST simulations and the prognostications derived from the machine learning techniques.
Performance Improvement of THZ Mimo Antenna with Graphene and Prediction Bandwidth Through Machine Learning Analysis for 6g Application
(Elsevier, 2024-10-23) Haque, Md Ashraful; Ananta, Redwan A.; Nirob, Jamal Hossain; Ahammed, Md. Sharif; Singh, Narinderjit Singh Sawaran; Paul, Liton Chandra; Algarni, Abeer D.; El Affendi, Mohammed; Ateya, Abdelhamied A.
This article provides the findings of a study that integrated simulation, an RLC equivalent circuit, and machine learning (ML) techniques to improve wireless indoor communications clusters with future 6 G applications. The antenna being presented is constructed on a polyimide substrate. It exhibits an isolation of 27 dB and has a bandwidth of 4.331 THz, ranging from 0.631 THz to 4.962 THz. Along with its small size (95.52 × 227.24) µm2, it boasts an impressive maximum gain of 13.3 dB and an efficiency rating of 95 %. The ECC value drops below 0.0002 when the DG goes over 9.99. An advanced design system (ADS) creates a model like the proposed MIMO antenna to compare the return loss caused by CST (Computer Simulation Technology). Subsequently, following extensive data sampling with CST MWS (Microwave Studio) simulation, we employed supervised regression ML techniques. Gaussian process regression demonstrates exceptional accuracy, reaching almost 99 %, as evidenced by the high R-square and var scores. Additionally, it achieves the lowest error, less than one, while predicting bandwidth. The proposed antenna demonstrates strong potential as a formidable contender for 6 G THz band applications, as evidenced by the outcomes of the CST simulations and the prognostications derived from the machine learning techniques.