Dataset for Closed-Loop Machine Learning Optimization of a 2.4 GHz Slotted Microstrip Patch Antenna

This dataset supports the manuscript titled:

"Machine Learning-Assisted Closed-Loop Optimization of a Slotted Inset-Fed Microstrip Patch Antenna for 2.4 GHz IoT Applications."

The dataset was generated using CST Studio Suite through parametric full-wave electromagnetic simulations of a rectangular microstrip patch antenna fabricated on an FR4 substrate (εr = 4.3, thickness = 1.5 mm).

A total of 56 valid simulations were conducted by varying:

Patch length (L): 26–33 mm

Patch width (W): 34–41 mm

For each simulation, the following parameters were extracted:

Resonance frequency (GHz)

Reflection coefficient S11 (dB)

This dataset was used to train a Random Forest regression model and to enable closed-loop optimization using differential evolution.

The dataset reflects the nonlinear relationship between antenna geometry and electromagnetic performance and supports reproducibility of the results presented in the manuscript.

File included:

final_production_data.csv (primary dataset used for training and validation)

This dataset is publicly available to support transparency, reproducibility, and future research in machine learning-assisted antenna design.