Abstract:
An inset fed-microstrip patch antenna (MPA) with a partial ground structure is constructed
and evaluated in this paper. This article covers how to evaluate the performance of the designed
antenna by using a combination of simulation, measurement, creation of the RLC equivalent circuit
model, and the implementation of machine learning approaches. The MPA’s measured frequency
range is 7.9–14.6 GHz, while its simulated frequency range is 8.35–14.25 GHz in CST microwave studio
(CST MWS) 2018. The measured and simulated bandwidths are 6.7 GHz and 5.9 GHz, respectively.
The antenna substrate is composed of FR-4 Epoxy, which has a dielectric constant of 4.4 and a loss
tangent of 0.02. The equivalent model of the proposed MPA is developed by using an advanced
design system (ADS) to compare the resonance frequencies obtained by using CST. In addition, the
measured return loss of the prototype is compared with the simulated return loss observed by using
CST and ADS. At the end, 86 data samples are gathered through the simulation by using CST MWS,
and seven machine learning (ML) approaches, such as convolutional neural network (CNN), linear
regression (LR), random forest regression (RFR), decision tree regression (DTR), lasso regression,
ridge regression, and extreme gradient boosting (XGB) regression, are applied to estimate the resonant
frequency of the patch antenna. The performance of the seven ML models is evaluated based on
mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and variance
score. Among the seven ML models, the prediction result of DTR (MSE = 0.71%, MAE = 5.63%,
RMSE = 8.42%, and var score = 99.68%) is superior to other ML models. In conclusion, the proposed
antenna is a strong contender for operating at the entire X-band and lower portion of the Ku-band
frequencies, as evidenced by the simulation results through CST and ADS, it measured and predicted
results using machine learning approaches.
