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.
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 [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.