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.

This study presents an innovative and compact monopole antenna with dual-band frequency reconfigurability for LoRa applications. It operates within the 915 MHz and 868 MHz frequencies, aligning with the designated bands for use in America, Asia and Europe. No existing compact reconfigurable antenna with these features for LoRa applications within ISM bands below 1 GHz is known. Employing an economical FR-4 substrate in its design, the antenna attains a compact size of $40 \times 42 \text {mm}^{2}$ ( $0.12\,\,\lambda _{0} \times 0.12\,\,\lambda _{0}$ ), where $\lambda _{0}$ denotes the wavelength in free space corresponding to 868 MHz. A single RF PIN diode enables seamless switching between 868 MHz and 915 MHz bands. Design, simulation, and optimization employed CST MWS® software. Supervised regression Machine Learning (ML) models predicted resonance frequencies, with Gaussian Process Regression emerging as optimal, achieving R-squared and variance scores of 92.87% and 93.77%, respectively. A maximum gain of 2 dBi at 915 MHz and 70% efficiency, boasting good radiation patterns and matching was demonstrated by the antenna. Experimental validation in a football field at Universiti Teknologi PETRONAS, Malaysia, assessed the proposed antenna’s performance on a LoRa transceiver system based on LoRa SX1276. The Received Signal Strength Indicator (RSSI) of the proposed antenna consistently exceeded the conventional commercially available monopole antenna by an average of $\mathbf {-12 \mathrm {dBm}}$ at every point up to 300 m, showcasing enhanced signal reception. The antenna proves promising for wireless sensor nodes in long-range applications.

A novel hexahedron fiber has been proposed for biomedical imaging applications and efficient guiding of terahertz radiation. A finite element method (FEM) has been applied to investigate the guiding properties rigorously. All numerically computational investigated results for optimum parameters have revealed the high numerical aperture (NA) of 0.52, high core power fraction of 64%, near zero flattened dispersion of 0.5 ± 0.6 ps/THz/cm over the 0.8–1.4 THz band and low losses with 80% of the bulk absorption material loss. In addition, the V–parameter is also inspected for checking the proposed fiber modality. The proposed single-mode hexahedron photonic crystal fiber (PCF) can be highly applicable for convenient broadband transmission and numerous applications in THz technology.

In this study, we have presented our findings on the deployment of a machine learning (ML) technique to enhance the performance of LTE applications employing quasi-Yagi-Uda antennas at 2100 MHz UMTS band. A number of techniques, including simulation, measurement, and a model of an RLC-equivalent circuit, are discussed in this article as ways to assess an antenna's suitability for the intended applications. The CST simulation gives the suggested antenna a reflection coefficient of -38.40 dB at 2.1 GHz and a bandwidth of 357 MHz (1.95 GHz-2.31 GHz) at a -10 dB level. With a dimension of 0.535${\lambda }_0\times$0.714${\lambda }_0$, it is not only compact but also features a maximum gain of 6.9 dB, a maximum directivity of 7.67, VSWR of 1.001 at center frequency and a maximum efficiency of 89.9%. The antenna is made of a low-cost substrate, FR4. The RLC circuit, sometimes referred to as the lumped element model, exhibits characteristics that are sufficiently similar to those of the proposed Yagi antenna. We use yet another supervised regression machine learning (ML) technique to create an exact forecast of the antenna's frequency and directivity. The performance of machine learning (ML) models can be evaluated using a variety of metrics, including the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE). Out of the seven ML models, the linear regression (LR) model has the lowest error and maximum accuracy when predicting directivity, whereas the ridge regression (RR) model performs the best when predicting frequency. The proposed antenna is a strong candidate for the intended UMTS LTE applications, as shown by the modeling results from CST and ADS, as well as the measured and forecasted outcomes from machine learning techniques.

Yagi antennas are useful for wireless communications because of the directional gain they provide, allowing the antenna to concentrate the signal in either the transmission or reception direction. It is built on a substrate made of FR-4, this antenna has a return loss of −46.85 dB at 3.6 GHz and a bandwidth of 3.3–4.2 GHz within a −10 dB range, making it ideal for use in the n77 bands. Not only is it small, with a size of 0.642λ ₀ × 0.583λ ₀, but it also has a maximum gain of 7.95 dB and a maximum directivity of 8.58 dB. This study investigates several approaches to estimating the performance of an antenna. These approaches include simulation with a variety of software tools, including as CST, HFSS, and Altair Feko; curve fitting technology; and the RLC equivalent circuit model. After that, simulation with CST MWS is used to collect a large amount of data samples, and then supervised regression machine learning (ML) methods are used to determine the resonance frequency and bandwidth of the antenna. When it comes to predicting bandwidth and frequency, Random Forest Regression demonstrates an exceptional level of performance, particularly when comparing with the results produced by curve-fitting tools, neural networks, and regression machine learning models. When all of these considerations are taken into account, it is clear that the antenna is an outstanding option for the n77 band of a 5G communication system.