Synergistic Fusion of Deep Neural Networks for Antenna Dimension Inference and Design Enhancement at Futuristic Frequencies

ABSTRACT This paper focuses on the impact of Deep Neural Networks (DNN), especially those utilizing back propagation models, in the design aspects of a fractal geometry‐based microstrip patch antenna under high—frequency applications chosen at 25–35 GHz millimeter (mm) wave frequency band. Designing a fifth generation (5G) mm wave frequency microstrip patch is a challenging task due to size constraints and performance needs, making traditional methods complex. Antenna design for precise applications under high frequencies with better performance, DNNs are chosen. The ability to learn from the complex datasets of antenna designs enables DNNs to effectively predict and optimize the dimensions of the fractal geometry‐based microstrip patch antenna to meet the performance characteristics like bandwidth, gain, and voltage standing wave ratio. DNNs speed up the design process, minimize computing burden, and allow engineers to experiment with unique, compact designs that would be impossible to produce using traditional approaches. The proposed ANN integrated fractal geometry‐based 2 × 2 microstrip patch antenna array achieved a bandwidth of 5200 MHz and a gain of 9 dB, which are very much suitable for applications like vehicular communications under 5G models and biomedical communications.