High gain multi-band circularly polarized wearable leaky wave zipper MIMO antenna.

A miniaturized, multi-band, four-port wearable Multiple Input Multiple Output (MIMO) antenna is proposed, which contains a leaky wave textile antenna (LWTA) on denim (εr = 1.6, tanδ = 0.006) as substrate and Shieldit Super Fabric as conductor textile. The concept in this work involves incorporating the metal and plastic zipper into the garment to function as an antenna worn on the body. Simulations and measurements have been conducted to explore this idea. The LWTA has dimensions of 40 × 30 × 1 mm³. Every two ports are separated by a zipper with two different kinds of materials: Acetal Polymer Plastic (APP) and 90 % brass to improve the isolation, gain, and Impedance bandwidth. The antenna operates in the frequency ranges covering the L, C, S, and X bands. Additionally, diversity performance is evaluated using the Envelope Correlation Coefficient (ECC) and diversity gain (DG). Simulation and measurement findings agree well, with a maximum gain of 12.15 dBi, low Specific Absorption Rate (SAR) based on the standards, DG greater than 9.65 dB, circular polarization (CP), and strong isolation (<-23 dB) between each port. Since the antenna's characteristics do not change significantly under bending and when the zipper is opened, the proposed antenna is a viable candidate for body-centric wireless communications on the battlefield. For example, it can facilitate communication covering wireless local area network (WLAN) and fifth-generation (5G) communications.

5G-enabled UAVs for energy-efficient opportunistic networking.

The article explores the potential of 5G-enabled Unmanned Aerial Vehicles (UAVs) in establishing opportunistic networks to improve network resource management, reduce energy use, and boost operational efficiency. The proposed framework utilizes 5G-enabled drones and edge command and control software to provide energy-efficient network topologies. As a result, UAVs operate edge computing for efficient data collecting and processing. This invention enhances network performance using modern Artificial Intelligence (AI) algorithms to improve UAV networking capabilities while conserving energy. An empirical investigation shows a significant improvement in network performance measures when using 5G technology compared to older 2.4 GHz systems. The communication failure rate decreased by 50 %, from 12 % to 6 %. The round-trip time was lowered by 58.3 %, from 120 Ms to 50 Ms. The payload efficiency improved by 13.3 %, dropping from 15 % to 13 %. The data transmission rate increased significantly from 1 Gbps to 5 Gbps, representing a 400 % boost. The numerical findings highlight the significant impact that 5G technology may have on UAV operations. Testing on a 5G-enabled UAV confirms the effectiveness of our technique in several domains, including precision agriculture, disaster response, and environmental monitoring. The solution seriously improves UAV network performance by reducing energy consumption and using peripheral network command-and-control software. Our results emphasize the versatile networking capacities of 5G-enabled drones, which provide new opportunities for UAV applications.

Dual-Slope Path Loss Model for Integrating Vehicular Sensing Applications in Urban and Suburban Environments.

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions.

Incremental RBF-based cross-tier interference mitigation for resource-constrained dense IoT networks in 5G communication system.

The deployment of resource-constrained and densely distributed Internet of Things (IoT) devices poses significant challenges for 5G communication systems due to the increased likelihood of inter-tier interference. This interference can degrade network performance and hinder the transmission of data in a reliable and efficient manner. Using an incremental Radial Basis Function (RBF) technique, this paper proposes a novel approach for cross-tier interference mitigation in 5G communication among resource-constrained dense IoT networks. Utilizing the incremental RBF method to model and optimize interference patterns in resource-constrained dense IoT networks is the primary innovation of our approach. In contrast to conventional interference mitigation techniques, which view interference as a static phenomenon, our method adapts to the dynamic nature of IoT networks by incrementally updating the RBF model. This enables precise modeling of the various interference scenarios and real-time modification of interference mitigation parameters. Utilizing the spatial distribution of IoT devices, this approach improves interference mitigation. The proposed method intelligently allocates resources and optimizes interference mitigation parameters based on the location and density of IoT devices. This adaptive resource allocation improves network capacity, reliability, and overall system performance by maximizing the utilization of available resources while minimizing interference. We demonstrate the effectiveness of the incremental RBF-based approach in mitigating cross-tier interference in resource-constrained dense IoT networks within the 5G ecosystem through extensive experiments and simulations. Our findings indicate substantial improvements in communication performance, including increased throughput, decreased packet loss, and decreased latency.

A comprehensive review of 5G NR RF-EMF exposure assessment technologies: fundamentals, advancements, challenges, niches, and implications.

This review offers a detailed examination of the current landscape of radio frequency (RF) electromagnetic field (EMF) assessment tools, ranging from spectrum analyzers and broadband field meters to area monitors and custom-built devices. The discussion encompasses both standardized and non-standardized measurement protocols, shedding light on the various methods employed in this domain. Furthermore, the review highlights the prevalent use of mobile apps for characterizing 5G-NR radio network data. A growing need for low-cost measurement devices is observed, commonly referred to as "sensors" or "sensor nodes," that are capable of enduring diverse environmental conditions. These sensors play a crucial role in both microenvironmental surveys and individual exposures, enabling stationary, mobile, and personal exposure assessments based on body-worn sensors, across wider geographical areas. This review revealed a notable need for cost-effective and long-lasting sensors, whether for individual exposure assessments, mobile (vehicle-integrated) measurements, or incorporation into distributed sensor networks. However, there is a lack of comprehensive information on existing custom-developed RF-EMF measurement tools, especially in terms of measuring uncertainty. Additionally, there is a need for real-time, fast-sampling solutions to understand the highly irregular temporal variations EMF distribution in next-generation networks. Given the diversity of tools and methods, a comprehensive comparison is crucial to determine the necessary statistical tools for aggregating the available measurement data.

Research on reliability mapping of 5G low orbit constellation network slice based on deep reinforcement learning.

Reliability mapping of 5G low orbit constellation network slice is an important means to ensure link network communication. The problem of state space explosion is a typical problem. The deep reinforcement learning method is introduced. Under the 5G low orbit constellation integrated network architecture based on software definition network (SDN) and network function virtualization (NFV), the resource requirements and resource constraints of the virtual network function (VNF) are comprehensively considered to build the 5G low orbit constellation network slice reliability mapping model, and the reliability mapping model parameters are trained and learned by using deep reinforcement learning, solve the problem of state space explosion in the reliability mapping process of 5G low orbit constellation network slices. In addition, node backup and link backup strategies based on importance are adopted to solve the problem that VNF/link reliability is difficult to meet in the reliability mapping process of 5G low orbit constellation network slice. The experimental results show that this method improves the network throughput, packet loss rate and intra slice traffic of 5G low orbit constellation, and can completely repair network faults within 0.3 s; For different number of 5G low orbit constellation network slicing requests, the reliability of this method remains above 98%; For SFC with different lengths, the average network delay of this method is less than 0.15 s.

Design and implementation of multi-band reflectarray metasurface for 5G millimeter wave coverage enhancement.

A compact low-profile multi-band millimeter-wave (mm-wave) reflectarray metasurface design is presented for coverage enhancement in 5G and beyond cellular communication. The proposed single-layer metasurface exhibits a stable reflection response under oblique incidence angles of up to 60 ∘ at 24 and 38 GHz, and transmission response at 30 GHz, effectively covering the desired 5G mm-wave frequency bands. The proposed reflectarray metasurface is polarization insensitive and performs equally well under TE and TM polarized incident waves due to the symmetric pattern. In addition, the low profile of the proposed metasurface makes it appropriate for conformal applications. In comparison to the state-of-the-art, the proposed reflectarray metasurface unit cell design is not only compact (3.3  ×  3.3 mm 2 ) but also offers two reflections and one transmission band based on a single-layer structure. It is easy to reconfigure the proposed metasurface unit cell for any other frequency band by adjusting a few design parameters. To validate the concept of coverage enhancement, a 32  ×  x32 unit-cell prototype of the proposed reflectarray metasurface is fabricated and measured under different scenarios. The experimental results demonstrate that a promising signal enhancement of 20-25 dB is obtained over the entire 5G mm-wave n258, n259, and n260 frequency bands. The proposed reflectarray metasurface has a high potential for application in mm-wave 5G networks to improve coverage in dead zones or to overcome obstacles that prevent direct communication linkages.

High Isolation MIMO Antenna System for 5G N77/N78/N79 Bands.

This paper presents a symmetric dual-band multiple-input multiple-output (MIMO) antenna system tailored for fifth-generation (5G) mobile terminals. Operating within the 5G frequency bands N77/N78 (3.4-3.6 GHz) and N79 (4.8-5.0 GHz), the proposed MIMO system achieves high isolation between adjacent antenna elements through slotting and self-decoupling technologies. Antenna elements are strategically positioned on two frames perpendicular to the smartphone's main board. Each antenna element integrates a rectangular microstrip radiator on the inner frame surface, accompanied by a grounded rectangular ring on the outer frame surface. The feed line, situated atop the main board, connects to an external SMA connector located at the main board's bottom. Measurement results reveal isolations exceeding 20 dB for the lower band and 24 dB for the higher band. The fabricated and tested MIMO antenna system demonstrates excellent agreement between simulation and measurement outcomes.

A Compact MIMO Antenna Based on Modal Analysis for 5G Wireless Applications.

This article presents a planar, non-angular, series-fed, dual-element dipole array MIMO antenna operating at 28 GHz with the metasurface-based isolation improvement technique. The initial design is a single-element antenna with a dual dipole array which is series-fed. These dipole elements are non-uniform in shape and distance. This dipole antenna results in end-fire radiation. The dipole antenna excites the J1 mode for its operation. Further, with the view to improve channel capacity, the dipole array expands the antenna to a three-element MIMO antenna. In the MIMO antenna structure, the sum of the J1, J2, and J3 modes is excited, causing resonance at 28 GHz. This article also proposes a metasurface structure with wide stopband characteristics at 28 GHz for isolation improvement. The metasurface is composed of rectangle-shaped structures. The defected ground and metasurface structure combination suppresses the surface wave coupling among the MIMO elements. The proposed antenna results in a bandwidth ranging from 26.7 to 29.6 GHz with isolation improvement greater than 21 dB and a gain of 6.3 dBi. The antenna is validated with the diversity parameters of envelope correlation coefficient, diversity gain, and channel capacity loss.

Wideband high gain metasurface-based 4T4R MIMO antenna with highly isolated ports for sub-6 GHz 5G applications.

This study presents the design of four 178 × 178  (mm) 2 wideband, high gain, highly efficient metasurface-based 4T4R MIMO (Multiple-Input Multiple-Output) antennas with highly isolated ports, covering the middle and a portion of the upper bands of the sub 6 GHz 5G frequency spectrum for 5G-based systems, such as IoT (Internet of Things) applications, vehicular communications (e.g., rooftop antennas of cars or trains), smart industries (e.g., farms and factories). The radiating elements of these antennas use the aperture-coupled feeding technique with a dumbbell-shaped slot, a truncated square patch with two U-shaped slots, and a metasurface layer. The proposed MIMO structures place four identical radiating elements like a 2 × 2 matrix with 90 ∘ successive rotations to produce orthogonal electromagnetic waves, improving the isolation between ports. Six-millimeter spaces are added between these elements, and two vertical and horizontal strip slots are carved on the ground as the decoupling structure to decrease the mutual coupling. Simulation results show that Antenna_1, Antenna_2, and Antenna_3 achieve gain values of 6.2 to 9.4 dBi, 8.2 to 11.6 dBi, 6.2 to 9.5 dBi, below - 35, - 25, and - 33 isolation and almost 10 dB diversity gain from 2.8 to 4.7 GHz, 2.8 to 4.5 GHz, and 2.7 to 4.9 GHz, respectively. As a prototype, Antenna_4 is manufactured, and measurements are performed. It achieves 6.28 to 10.45 dBi gain values, below - 23 dB isolation, and 0.001 envelope correlation coefficient over 2.7 to 4.3 GHz. The results confirm that the proposed MIMO antennas are compatible with the 5G essential requisites.

Compact metamaterial-based single/double-negative/near-zero index resonator for 5G sub-6 GHz wireless applications.

The concept, performance, and analyses of distinctive, miniaturized metamaterial (MTM) unit cell addressing the forthcoming Sub 6 GHz 5G applications are presented in this paper. Two circular split-ring resonators (CSRR) with two parallel rectangular copper elements in front of the design and a slotted square element in the background make up the suggested metamaterial. It has a line segment with tunable features that is positioned in the center of the little ring copper structure. The suggested design offers a significant operating frequency band of 220 MHz together with a resonance of transmission coefficient S21 at 3.5 GHz. Furthermore, in two (z & x) principal axes of wave propagation, wide-range achievement, single/double-negative (S/DNG) refractive index, negative permittivity, and near-zero permeability properties were demonstrated. Through varying central slotted-strip line length, resonance frequencies can be selectively altered. Moreover, the metamaterial has overall dimensions of 9 × 9 mm2 and is composed on a Rogers 5880 RT substrate. In order to create the suggested MTM's equivalent circuit, which shows similar coefficient of transmission (S21), a proposed design's numerical simulation is carried out in the CST micro-wave studio. This simulation is after that put to comparison with manufacturing of the design.

Summary of seven Swedish case reports on the microwave syndrome associated with 5G radiofrequency radiation.

The fifth generation, 5G, for wireless communication is currently deployed in Sweden since 2019/2020, as well as in many other countries. We have previously published seven case reports that include a total of 16 persons aged between 4 and 83 years that developed the microwave syndrome within short time after being exposed to 5G base stations close to their dwellings. In all cases high radiofrequency (RF) radiation from 4G/5G was measured with a broadband meter. RF radiation reached >2,500,000 to >3,180,000 µW/m2 in peak maximum value in three of the studies. In total 41 different health issues were assessed for each person graded 0 (no complaint) to 10 (worst symptoms). Most prevalent and severe were sleeping difficultly (insomnia, waking night time, early wake-up), headache, fatique, irritability, concentration problems, loss of immediate memory, emotional distress, depression tendency, anxiety/panic, dysesthesia (unusual touched based sensations), burning and lancinating skin, cardiovascular symptoms (transitory high or irregular pulse), dyspnea, and pain in muscles and joints. Balance disorder and tinnitus were less prevalent. All these symptoms are included in the microwave syndrome. In most cases the symptoms declined and disappeared within a short time period after the studied persons had moved to a place with no 5G. These case histories are classical examples of provocation studies. They reinforce the urgency to inhibit the deployment of 5G until more safety studies have been performed.

LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer.

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods.

Power Amplifier Predistortion Using Reduced Sampling Rates in the Forward and Feedback Paths.

The feasibility of implementing digital predistortion for next-generation wireless communication is faced with a dilemma due to the ever-increasing demand for faster data rates. This causes the utilized bandwidth to increase significantly, as seen in the 5G NR standard in which bandwidths as high as 400 MHz are utilized. Hence, the development of new predistortion techniques in which the forward and feedback paths operate at lower sampling rates is of utmost importance to realize efficient and practical predistortion solutions. In this work, a novel predistortion technique is presented by which the predistortion is divided between the digital and analog domains. The predistorter is composed of a memoryless AM/AM gain function that is implementable in the analog domain, and a nonlinear model with memory effects in the digital domain to relax the sampling rate requirements on both the forward and feedback paths. Experimental validation was carried out with a 20 MHz and a 40 MHz 5G signal, and the results indicate minimal linearization degradation with a sampling rate reduction of 50% and 30%, respectively. This sampling rate reduction is concurrently applied in the digital-to-analog converter of the forward path and the analog-to-digital converter of the feedback path.

Dynamic Coalition Formation among IoT Service Providers: A Systematic Exploration of IoT Dynamics Using an Agent-Based Model.

This paper introduces an Agent-Based Model (ABM) designed to investigate the dynamics of the Internet of Things (IoT) ecosystem, focusing on dynamic coalition formation among IoT Service Providers (SPs). Drawing on insights from our previous research in 5G network modeling, the ABM captures intricate interactions among devices, Mobile Network Operators (MNOs), SPs, and customers, offering a comprehensive framework for analyzing the IoT ecosystem's complexities. In particular, to address the emerging challenge of dynamic coalition formation among SPs, we propose a distributed Multi-Agent Dynamic Coalition Formation (MA-DCF) algorithm aimed at enhancing service provision and fostering collaboration. This algorithm optimizes SP coalitions, dynamically adjusting to changing demands over time. Through extensive experimentation, we evaluate the algorithm's performance, demonstrating its superiority in terms of both payoff and stability compared to three classical coalition formation algorithms: static coalition, non-overlapping coalition, and random coalition. This study significantly contributes to a deeper understanding of the IoT ecosystem's dynamics and highlights the potential benefits of dynamic coalition formation among SPs, providing valuable insights and opening future avenues for exploration.

Effectiveness and feasibility of 5G-based remote interactive ultrasound training in critical care.

BACKGROUND: Ultrasound has widely used in various medical fields related to critical care. While online and offline ultrasound trainings are faced by certain challenges, remote ultrasound based on the 5G cloud platform has been gradually adopted in many clinics. However, no study has used the 5G remote ultrasound cloud platform operating system for standardized critical care ultrasound training. This study aimed to evaluate the feasibility and effectiveness of 5G-based remote interactive ultrasound training for standardized diagnosis and treatment in critical care settings. METHODS: A 5G-based remote interactive ultrasound training system was constructed, and the course was piloted among critical care physicians. From July 2022 to July 2023, 90 critical care physicians from multiple off-site locations were enrolled and randomly divided into experimental and control groups. The 45 physicians in the experimental group were trained using the 5G-based remote interactive ultrasound training system, while the other 45 in the control group were taught using theoretical online videos. The theoretical and practical ultrasonic capabilities of both groups were evaluated before and after the training sessions, and their levels of satisfaction with the training were assessed as well. RESULTS: The total assessment scores for all of the physicians were markedly higher following the training (80.7 ± 11.9) compared to before (42.1 ± 13.4) by a statistically significant margin (P < 0.001). Before participating in the training, the experimental group scored 42.2 ± 12.5 in the critical care ultrasound competency, and the control group scored 41.9 ± 14.3-indicating no significant differences in their assessment scores (P = 0.907). After participating in the training, the experimental group's assessment scores were 88.4 ± 6.7, which were significantly higher than those of the control group (72.9 ± 10.8; P < 0.001). The satisfaction score of the experimental group was 42.6 ± 2.3, which was also significantly higher than that of the control group (34.7 ± 3.1, P < 0.001). CONCLUSION: The 5G-based remote interactive ultrasound training system was well-received and effective for critical care. These findings warrant its further promotion and application.

In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications.

In this presented study, we measured in situ the uplink duty cycles of a smartphone for 5G NR and 4G LTE for a total of six use cases covering voice, video, and data applications. The duty cycles were assessed at ten positions near a 4G and 5G base-station site in Belgium. For Twitch, VoLTE, and WhatsApp, the duty cycles ranged between 4% and 22% in time, both for 4G and 5G. For 5G NR, these duty cycles resulted in a higher UL-allotted time due to time division duplexing at the 3.7 GHz frequency band. Ping showed median duty cycles of 2% for 5G NR and 50% for 4G LTE. FTP upload and iPerf resulted in duty cycles close to 100%.

A Comprehensive Overview of Network Slicing for Improving the Energy Efficiency of Fifth-Generation Networks.

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future.

Design of antipodal vivaldi antenna with patch and corrugations for 5G applications.

A compact 1 × 4 antipodal Vivaldi antenna (AVA) array designed for 5 G applications is introduced in this study. An elliptical-shaped parasitic patch and corrugation are strategically employed to enhance gain and bandwidth, making it well-suited for 5 G applications. The resulting AVA array with corrugation and parasitic patch (AVA-PC) is designed and simulated on ANSYS HFSS, demonstrating a stable gain ranging from 10 dBi to 11.7 dBi over the frequency range of 23.45 GHz to 28.74 GHz. The antenna, with 25.8 mm x 22.4 mm x 0.5 mm dimensions, is implemented on Roger's RT/Duroid substrate 5880. •Design uses an antipodal Vivaldi antenna to build a 1 × 4 AVA.•The array employs corrugations and an elliptical patch as a performance enhancement technique.•Simulated results confirm the designed antenna's practical utility for 5 G applications in a band of 23.45 GHz to 28.74 GHz.

Enhancing gain and isolation of a quad-element MIMO antenna array design for 5G sub-6 GHz applications assisted with characteristic mode analysis.

This paper presents a novel quad-element array with multiple inputs and multiple outputs (MIMO) designed for 5th generation sub-6 GHz applications. The MIMO system achieves a wide impedance bandwidth, high gain, and high isolation among its components, representing significant advancements in sub-6 GHz antenna applications. The single element, an elliptical resonator with a circular slot, is fed by a 50 Ω microstrip feedline, achieves a broad characteristic bandwidth from 3.7 to 5.7 GHz with a resonant frequency of 4.33 GHz and a gain of 1.81 dBi. Characteristic Mode Analysis (CMA) was employed to elucidate the evolution phases of this design. The quad-element MIMO antenna array maintains a compact size and broadband characteristics by arranging mirrored elements on the same ground plane. Implemented on a cost-effective FR-4 substrate measuring 44 × 44 × 1.6 mm3, the recommended MIMO antenna array, enhanced with a partial ground plane and due to the introduction of a vertical strip, a high isolation of - 38.53 dB is achieved between MIMO components along with a realized gain of 3.01 dBi and a radiation efficiency of 71% in the 5G sub-6 GHz band. Noteworthy properties include high isolation, diversity gain (DG), and envelope correlation coefficient (ECC), verifying the appropriateness of the suggested MIMO scheme for 5G transmission and reception in sub-6 GHz applications.