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 $${0.642}\lambda _0\times {0.583}\lambda _0$$0.642 λ 0 × 0.583 λ 0 . The antenna has an operating frequency of 3.5 GHz, a return loss of $$-43.45$$- 43.45 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.

The detection of partial discharge (PD) activities in high-voltage equipment can be conducted according to several mechanisms of signal detection, including electromagnetic wave signal detection, acoustic signal detection, chemical reactions, electrical signal detection, and optical emission detection. Recently, multiple methods of detection and localization of partial discharge activities, which occurred in power transformers and gas-insulated switchgear (GIS), have been proposed to monitor the health condition of high-voltage equipment, especially when the awareness regarding preventive maintenance has been emphasized at the industrial level and among electrical providers. In aligning the needs of the industrial sector and the improvement of PD-detection methods, this manuscript focuses on reviewing the current practice methods for the detection and localization of PD signals in high-voltage equipment, comparing their efficacy, and summarizing the future direction of research work-related methods of PD detection. The comparative reviews are discussed in terms of the mechanism of PD signal detection, indication parameters, calibration techniques, and the advantages and limitations of each method of PD measurement in detail.

Many countries have allocated new frequency bands for fifth generation (5G) communication systems. In this paper, a wideband continuous class-F (CCF) radio frequency power amplifier (RFPA) is presented for the new 5G frequency band, from 3.3 GHz to 4.3 GHz using a 10 W Cree CGH40100F device. A unique wideband RFPA design approach for the output matching network (OMN) is also presented by applying a harmonic tuning network (HTN) for the harmonics and a low pass matching technique (LPMT) for the fundamental frequency. The RFPA is fabricated, and promising measurement results show a drain efficiency of 55.9% to 65.3% is achieved at an output power of 40 dBm (± 0.3 dBm) over a frequency range of 3.3 GHz to 4.3 GHz. A two-tone signal with a 10 MHz spacing was also applied to investigate the linearity of the RFPA.

In this work, we investigate resource allocation and user pairing to improve the system’s Throughput for the downlink non-orthogonal multiple access (NOMA)-based 5G networks. The proposed resource allocation involves user pairing, subchannel power allocation, and proportional power allocation among the multiplexed users. The resource allocation is a non-deterministic polynomial (NP-hard) problem that is difficult to tackle throughput maximization. The user pairing and power allocation are coupled to address the substantial requirements of the NOMA system. The NOMA system requires an efficient deployment of resource allocation techniques to enhance the system’s throughput performance. In this work, we propose simulated annealing (SA) to optimize the power allocation and perform user pairing to maximize the throughput for the NOMA system. Also, we provide mathematical proof on the near-optimal solution for subchannel power and mathematical analysis on the optimal value of the power ratio for the multiplexed users in the NOMA system. The SA provides a significant throughput performance that increases by 7% compared to the existing numerical optimization methods. Results obtained show that SA performs with sufficient reliability and low time complexity in terms of Throughput improvement.

Beamforming networks for multiple beam antennas are believed to be in the vanguard of technological developments in mmWave for 5G wireless applications. The basic idea of beamforming technique is the application of multiple antenna elements radiating the same signal at an identical phase and wavelength, into one strong signal pointed to a specific direction. For low cost and power consumption, radio frequency beamforming networks (RF-BFNs) such as Blass matrices, Butler matrices, and Nolen matrices will play a critical role in achieving the ever-increasing demands in wireless technology. Therefore, this study aims to present a comprehensive survey and developments of RF-BFNs with (particular focus in Butler matrices). From the fundamental perspectives of the beamforming techniques and progress over time, component evolution includes branch-line coupler (BLC), Phase shifter (PS), and Crossovers to complete system Butler matrix (BM) integration. Different design techniques to improve bandwidth, size reduction, multi-band, and other performance characteristics are discussed extensively. Furthermore, the paper also discusses different geometry of Butler matrices in open microstrip transmission lines, substrate integrated waveguide (SIW) and gap waveguide (GWG) technologies highlighting key developments and research challenges from single band to dual-band operations. We expect this paper to provide more profound insights into the designs processes and suggest suitable ways to facilitate further developments of RF-beamforming networks at mmWave and sub-mmWave frequency ranges.

A 16-port massive Multiple-Input-Multiple-Output (mMIMO) antenna system featuring a high gain and efficiency is proposed for millimeter-wave applications. The antenna system consists of 64 elements with a total size of 17 λo × 2.5λo, concerning the lowest frequency. Each 2 × 2 (radiating patch) subarray is designed to operate within the 25.5–29 GHz frequency range. The antenna's performance in terms of isolation, gain, and efficiency has been significantly improved by utilizing the proposed unique double and epsilon negative (DNG/ENG) metamaterials. The array elements are positioned on top of a Rogers RT5880 substrate, with ENG metamaterial unit cells interposed in between to mitigate coupling effects. Additionally, the DNG metamaterial reflector is positioned at the rear of the antenna to boost the gain. As a result, the metamaterial-based mMIMO antenna offers lower measured isolation reaching 25 dB, a maximum gain of 20 dBi and an efficiency of up to 99%. To further analyze the performance of the MIMO antenna, the diversity gain and enveloped correlation coefficient are discussed in relation to the MIMO parameters.