Patch Antenna Design using Artificial Intelligence Methods for 4G/5G Applications

The advent of fifth generation wireless communications (5G), highlights the need for new priorities in research. Especially, the growing mobile networks require abilities for mas-sive connectivity, advanced security, increased network capacity and extremely low-latency. Antennas play a crucial part of this ecosystem, thus, their design is a challenging task. In this work, a Boundary - Seeking Generative Adversarial Network is trained to serve as a surrogate model for the design of a patch antenna with random slots and two operating frequencies at 2.6 GHz and 3.5 GHz, making it suitable for 4G/5G applications. The outcome of the trained model is used as an initial population for further op-timization using the biogeography-based optimization algorithm. The final design achieves
$S_{11}=-37$
dB and
$S_{11}=-33$
dB at 2.6 GHz and 3.5 GHz respectively.