AI-empowered mm-wave antenna design - Das, Bipul Chandra; Hasan Shovo, M.H.; Islam, Md. Rafiqul; Alam, A. H. M. Zahirul; Hasan Mahfuz, M M; Habaebi, Mohamed Hadi; Abdul Malek, Norun Farihah (IEEE, 2025)
The performance of wireless 5G communication networks can be significantly enhanced by integrating compact patch antenna systems with machine learning (ML) techniques. In this study, a compact antenna is proposed, constructed on a Rogers 5880 substrate, making it highly suitable for high-band 5G applications. The antenna demonstrates excellent performance, an impedance bandwidth ranging from 26.13 GHz to 26.18 GHz within the -10 dB reflection coefficient range. Despite its compact dimensions (5.33×6.3×0.13 mm3), the antenna achieves an efficiency of around 89%. CST software is used to compare the return loss characteristics with the HFSS generated model of the proposed microstrip patch antenna (MPA). Following this, extensive data sampling is carried out using HFSS, and regressor techniques are applied for performance prediction. Among the tested ML methods, Multi-Layer Perceptron (MLP) Regressor the most accurate results, demonstrating the lowest prediction error, particularly in bandwidth estimation. Overall, the proposed antenna proves to be a strong candidate for high frequency 5G communication systems. Designing compact patch antenna for 26 GHz mm-Wave 5G applications presents considerable challenges, primarily in achieving performance for mm-Wave applications.