Academic Journal

Machine learning based on patch antenna design and optimization for 5 G applications at 28GHz

التفاصيل البيبلوغرافية
العنوان: Machine learning based on patch antenna design and optimization for 5 G applications at 28GHz
المؤلفون: Md․Sohel Rana, Sheikh Md․ Rabiul Islam, Sanjukta Sarker
المصدر: Results in Engineering, Vol 24, Iss , Pp 103366- (2024)
بيانات النشر: Elsevier, 2024.
سنة النشر: 2024
المجموعة: LCC:Technology
مصطلحات موضوعية: Microstrip patch antenna, Return loss (S11), Voltage standing wave ratio (VSWR), Computer simulation technology (CST), Fifth Generation (5G), Rogers RT5880, Technology
الوصف: This study has meticulously designed, tested, and optimized a patch antenna with a rectangular shape at 28 GHz. The antenna utilizes two different substrate materials, each having a significantly different relative permittivity. The two substrate materials used are Rogers RT5880 for Design-I and FR-4 for Design-II. The data reveal a notable return loss, gain, directivity, efficiency, and bandwidth change due to the substrate materials' differing relative permittivity and thickness values. Design I achieved return loss, VSWR of -59.289 dB, 1.0023 and Design II are return loss, VSWR of -49.182 dB, 1.007. Design I achieved gain and directivity of 7.63 dBi and 8.51 dBi, and Design II is 3.98 dBi and 7.56 dBi, respectively. The efficiency of the design-I was 89.66 %, and design-II was, 52.65 %. In this paper, a predictive model was developed using a polynomial regression algorithm to create a predictive model for the designed antenna. The predictive model accelerates the design process and eliminates the need for rigorous physical prototyping and testing. This study is unique in that it creates a mathematical model using the Multivariate Polynomial algorithm for all three parameters: bandwidth, Return loss, and VSWR. A MPR was also deployed to build mathematical models on the antenna parameters. The evaluation of the predicted outcomes shows that the MPR model achieved an R2 score of 0.9999, 0.9968, 0.9985 and RMSE of 0.0, 0.0, and 0.25; respectively. The study utilized analysis of variance (ANOVA) to examine the impact of different independent factors on both responses and model validation.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2590-1230
Relation: http://www.sciencedirect.com/science/article/pii/S2590123024016190; https://doaj.org/toc/2590-1230
DOI: 10.1016/j.rineng.2024.103366
URL الوصول: https://doaj.org/article/bb549b17373c452088ad104ffa59f4af
رقم الانضمام: edsdoj.bb549b17373c452088ad104ffa59f4af
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:25901230
DOI:10.1016/j.rineng.2024.103366