Radio frequency energy harvesting (RF-EH) is a potential technology via the generation of electromagnetic waves. This advanced technology offers the supply of wireless power that is applicable for battery-free devices, which makes it a prospective alternative energy source for future applications. In addition to the dynamic energy recharging of wireless devices and a wide range of environmentally friendly energy source options, the emergence of the RF-EH technology is advantageous in facilitating various applications that require quality of service. This review highlights the abundant source of RF-EH from the surroundings sources, including nearby mobile phones, Wi-Fi, wireless local area network, broadcast television signal or DTS, and FM/AM radio signals. In contrast, the energy is captured by a receiving antenna and rectified into a working direct current voltage. This review also summarizes the power of RF-EH technology, which would provide a guideline for developing RF-EH units. The energy harvesting circuits depend on cutting-edge electrical technology to achieve significant efficiency, given that they are built to perform with considerably small current and voltage. Hence, the review includes a thorough analysis and discussion of various RF designs and their pros and cons. Finally, the latest applications of RF-EH are presented.

Massive multiple-input multiple-output (mMIMO) is a wireless access technique that has been studied and investigated in response to the worldwide bandwidth demand in the wireless communication sector (MIMO). Massive MIMO, which brings together antennas at the transmitter and receiver to deliver excellent spectral and energy efficiency with comparatively simple processing, is one of the main enabling technologies for the upcoming generation of networks. To actualize diverse applications of the intelligent sensing system, it is essential for the successful deployment of 5G—and beyond—networks to gain a better understanding of the massive MIMO system and address its underlying problems. The recent huge MIMO systems are highlighted in this paper’s thorough analysis of the essential enabling technologies needed for sub-6 GHz 5G networks. This article covers most of the critical issues with mMIMO antenna systems including pilot realized gain, isolation, ECC, efficiency, and bandwidth. In this study, two types of massive 5G MIMO antennas are presented. These types are used depending on the applications at sub-6 GHz bands. The first type of massive MIMO antennas is designed for base station applications, whereas the most recent structures of 5G base station antennas that support massive MIMO are introduced. The second type is constructed for smartphone applications, where several compact antennas designed in literature that can support massive MIMO technology are studied and summarized. As a result, mMIMO antennas are considered as good candidates for 5G systems.

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.

A single-layered multiple-input multiple-output (MIMO) antenna working at 28 GHz loaded with a compact planar-patterned metamaterial (MTM) structures is presented in this paper for millimeter-wave application. A combination of a split square and hexagonal shaped unit cell is designed and investigated with a wide range of effective near-zero index (NZI) of permeability and permittivity, along with a refractive index (NZRI) property. The metamaterial characteristics were examined through the material wave propagation in two main directions at y and x-axis. For wave propagation at the y-axis, it demonstrates mu-near-zero (MNZ) with more than 6 GHz bandwidth, near-zero refractive index (NZRI), and epsilon-near-zero (ENZ) properties. However, it indicates a wide negative range of single mu metamaterial (MNG) from 27.6 to 28.9 GHz frequency span at x-axis wave propagation. A single antenna with 3 × 3 metamaterial unit cells is proposed to operate at a frequency band (24 - 30) GHz. Furthermore, MIMO antenna with only 4 mm space between antenna elements provides high isolation of more than 24 dB. The measured results show that the MIMO antenna is satisfied with 6 GHz bandwidth, and maximum peak gain of 12.4 dBi. In addition to that, the proposed MIMO antenna loaded with MTM has also shown good performances with high diversity gain (DG > 9.99), envelope correlation coefficient (ECC) lower than 0.0013, channel capacity loss (CCL) <; 0.42, total active reflection coefficient (TARC) <; -7 dB, total efficiencies of higher than 98%, with an overall antenna size of 52 mm × 23 mm.

This study presents the design and analysis of a compact 28 GHz MIMO antenna for 5G wireless networks, incorporating simulations, measurements, and machine learning (ML) techniques to optimize its performance. With dimensions of 3.19 λ₀ × 3.19 λ₀, the antenna offers a bandwidth of 5.1 GHz, a peak gain of 9.43 dBi, high isolation of 31.37 dB, and an efficiency of 99.6%. Simulations conducted in CST Studio were validated through prototype measurements, showing strong agreement between the measured and simulated results. To further validate the design, an equivalent RLC circuit model was developed and analyzed using ADS, with the reflection coefficient results closely matching those from CST. Additionally, supervised ML techniques were employed to predict the antenna’s gain, evaluating nine models using metrics such as R-squared, variance score, mean absolute error, and root mean squared error. Among the models, Random Forest Regression achieved the highest accuracy, delivering approximately 99% reliability in gain prediction. This integration of machine learning with antenna design underscores its potential to optimize performance and enhance design efficiency. With its compact size, high isolation, and exceptional efficiency, the proposed antenna is a promising candidate for 28 GHz 5G applications, offering innovative solutions for next-generation wireless communication.

An integrated massive multiple-input multiple-output (mMIMO) antenna system loaded with metamaterial (MTM) is proposed in this article for fifth-generation (5G) applications. Besides, achievement of duple negative (DNG) characteristics using a proposed compact complementary split-ring resonator (SRR), a broad epsilon negative metamaterial (ENG) with more than 1 GHz bandwidth (BW), and near-zero refractive index (NZRI) features are presented. The proposed mMIMO antenna consists of eight subarrays with three layers that operate in the 5G mind band at 3.5 GHz (3.40–3.65 GHz) with high port isolation between adjacent antenna elements compared to an antenna that does not use MTM. Each subarray has two patches on the top layer, while the middle and bottom layers have two categories of full and partial ground plans, respectively. Simulated, produced, and tested are 32 elements with a total volume of 184 × 340 × 1.575 mm³. The measured findings reveal that the sub-6 antenna has a better than 10 dB reflection coefficient (S11), a lower than 35 dB isolation, and a peak gain of 10.6 dBi for each subarray. Furthermore, the recommended antenna loaded with MTM has demonstrated good MIMO performance with an ECC of less than 0.0001, total efficiencies of more than 90%, more than 300 MHz bandwidth, and an overall gain of 19.5 dBi.