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.

In this research, linearly arranged circular antenna array (LCAA), linearly arranged circular planer antenna array (LCPAA), linearly arranged rectangular antenna array (LRAA), and linearly arranged concentric circular antenna array (LCCAA) are analyzed using robust techniques. These four antenna arrays are briefly investigated to evaluate their beam width performance, ability to detect a false signal, and SINR power pattern performances. Robust techniques—fixed diagonal loading (FDL), optimal diagonal loading (ODL), variable diagonal loading (VDL), and new variable loading (NVL) techniques—are applied, and the NVL technique is found to be superior and provides the best performance with the proposed LCCAA beamformer. With an SINR of 40.45 dB, 40.29 dB, 40.18 dB, and 39.99 dB for 0°, 1°, 2°, and 3° disparity angles, respectively, and output powers of −0.019 dB, −0.076 dB, and −0.171 dB for 1°, 2°, and 3° disparity angles, respectively, LCCAA beamformers using the NVL technique outperform other beamformers with different robust approaches.