Angle-insensitive co-polarized metamaterial absorber based on equivalent circuit analysis for dual band WiFi applications

Machine learning-based technique for resonance and directivity prediction of UMTS LTE band quasi Yagi antenna

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 0.535λ 0 × 0.714λ 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.

Machine learning-based technique for gain and resonance prediction of mid band 5G Yagi antenna

In this study, we present our findings from investigating the use of a machine learning (ML) technique to improve the performance of Quasi-Yagi-Uda antennas operating in the n78 band for 5G applications. This research study investigates several techniques, such as simulation, measurement, and an RLC equivalent circuit model, to evaluate the performance of an antenna. In this investigation, the CST modelling tools are used to develop a high-gain, low-return-loss Yagi-Uda antenna for the 5G communication system. When considering the antenna's operating frequency, its dimensions are [Formula: see text]. The antenna has an operating frequency of 3.5 GHz, a return loss of [Formula: see text] dB, a bandwidth of 520 MHz, a maximum gain of 6.57 dB, and an efficiency of almost 97%. The impedance analysis tools in CST Studio's simulation and circuit design tools in Agilent ADS software are used to derive the antenna's equivalent circuit (RLC). We use supervised regression ML method to create an accurate prediction of the frequency and gain of the antenna. Machine learning models can be evaluated using a variety of measures, including variance score, R square, mean square error, mean absolute error, root mean square error, and mean squared logarithmic error. Among the nine ML models, the prediction result of Linear Regression is superior to other ML models for resonant frequency prediction, and Gaussian Process Regression shows an extraordinary performance for gain prediction. R-square and var score represents the accuracy of the prediction, which is close to 99% for both frequency and gain prediction. Considering these factors, the antenna can be deemed an excellent choice for the n78 band of a 5G communication system.

Focusing on the Development and Current Status of Metamaterial Absorber by Bibliometric Analysis

In this paper, a total of 4770 effective documents about metamaterial absorbers were retrieved from the Web of Science Core Collection database. We scientifically analyzed the co-occurrence network of co-citation analysis by author, country/region, institutional, document, keywords co-occurrence, and the timeline of the clusters in the field of metamaterial absorber. Landy N. I.'s, with his cooperator et al., first experiment demonstrated a perfect metamaterial absorber microwave to absorb all incidents of radiation. From then on, a single-band absorber, dual-band absorber, triple-band absorber, multi-band absorber and broad-band absorber have been proposed and investigated widely. By integrating graphene and vanadium dioxide to the metamaterial absorber, the frequency-agile functionality can be realized. Tunable absorption will be very important in the future, especially metamaterial absorbers based on all-silicon. This paper provides a new research method to study and evaluate the performance of metamaterial absorbers. It can also help new researchers in the field of metamaterial absorbers to achieve the development of research content and to understand the recent progress.

Rotational symmetry engineered, polarization and incident angle-insensitive, perfect metamaterial absorber for X and Ku band wireless applications

In this paper, a square enclosed split-maze shaped metamaterial absorber is proposed for X and Ku band wireless applications. Two square metal enclosures were introduced around the split-maze structure to make it rotational symmetric and thus insensitive to cross-polarization. The proposed absorber has shown maximum absorptions at 9.33 GHz, 12.83 GHz, 13.86 GHz, and 15.61 GHz with single negative value of permittivity. The absorber is insensitive to the incident angle of applied EM waves for normal and oblique incidence up to 180 degrees. In addition, it was proved co- & cross-polarization insensitive due to the symmetric structure of the patch. A comprehensive equivalent circuit analysis was done to explain the fundamental EM behaviour of the metamaterial structure, and the circuit outputs coincided with the simulation results. Finally, the metamaterial was measured for both unit cell, and the array after fabrication and simulation results were validated. The proposed MMA is suitable for wireless applications in devices, especially for sensing, EM energy harvesting, EM coupling reduction, and antenna gain enhancement purposes.

Metamaterial based on an inverse double V loaded complementary square split ring resonator for radar and Wi-Fi applications

In this research paper, an inverse double V loaded complementary square split ring resonator based double negative (DNG) metamaterial has been developed and examined numerically and experimentally. The electromagnetic (EM) properties of the proposed inverse double V-structure were calculated using computer simulation technology (CST-2019) and the finite integration technique (FIT). The designed metamaterial provides three resonance frequencies are 2.86, 5, and 8.30 GHz, covering S-, C-, and X-bands. The total size of the recommended unit cell is 8 [Formula: see text] 8 [Formula: see text] 1.524 mm³, and a high effective medium ratio (EMR) value of 13.11 was found from it. The - 10 dB bandwidths of this structure are 2.80 to 2.91, 4.76 to 5.17, and 8.05 to 8.42 GHz. The proposed structure's novelty is its small size, simple resonator structure, which provides double negative characteristics, high EMR, maximum coverage band, and required resonance frequencies. Wi-Fi network speeds are generally faster when frequencies in the 5 GHz band are used. Since the proposed structure provides a 5 GHz frequency band, hence the suggested metamaterial can be used in Wi-Fi for high bandwidth and high-speed applications. The marine radars operate in X-band, and weather radar works in S-band. Since the designed cell provides two more resonance frequencies, i.e., 2.86 GHz (S-band) and 8.30 GHz (X-band), the proposed metamaterial could be used in weather radar and marine radar. The design process and various parametric studies have been analyzed in this article. The equivalent circuit is authenticated using the advanced design system (ADS) software compared with CST simulated result. The surface current, E-field, and H-field distributions have also been analyzed. Different types of array structure, i.e., 1 [Formula: see text] 2, 2 [Formula: see text] 2, 3 [Formula: see text] 3, 4 [Formula: see text] 4, and 20 [Formula: see text] 25 is examined and validated by the measured result. The simulated and measured outcome is an excellent agreement for the inverse double V loaded CSSRR unit cell and array. We showed the overall performance of the suggested structure is better than the other structures mentioned in the paper. Since the recommended metamaterial unit cell size is small, provides desired resonance frequency, gives a large frequency band and high EMR value; hence the suggested metamaterial can be highly applicable for Radar and Wi-Fi.