Abstract
This paper presents the design and simulation of a multiband microstrip patch antenna optimized for modern wireless applications such as IoT devices, WLAN (2.4/5 GHz), WiMAX, and sub-6 GHz 5G systems. The novelty of this work lies in the integration of artificial neural networks (ANNs) with HFSS simulations, enabling rapid geometry prediction and reducing optimization iterations by nearly 50% compared to conventional design approaches. The proposed antenna, implemented on an FR-4 substrate with strategically placed L-shaped slots, achieves well-defined resonances at 2.4 GHz, 3.6 GHz, and 5.8 GHz. Simulation results show return loss (S11) better than –15 dB, VSWR < 2, and peak gains of 7–8 dBi, with stable broadside radiation patterns across all bands. A parametric study demonstrates the influence of slot dimensions and substrate parameters on multiband behavior, while a comparative analysis highlights performance advantages over existing designs. These results confirm that the ANN-assisted HFSS workflow provides an efficient methodology for realizing compact, high-performance antennas suitable for multistandard wireless communication platforms.