Integration Of 5G Research Articles

포스트모던 박물관 시대에 인터랙티브 미디어를 활용한 무형 문화유산 살아있는 전시 방안 연구

This study addresses challenges in the living exhibitions of intangible cultural heritage through interactive media, providing curators with clear design strategies. It outlines the characteristics of these exhibitions and the technical features of interactive media, summarizes the difficulties encountered, and proposes effective solutions. The results show that cross-platform data integration and cross-industry collaboration help curators accurately distinguish between the appearance and essence of living exhibitions. The development of mobile communication technology is transforming how interactive media is presented, and the integration of 5G provides stable support for dynamic displays across time and space. Participatory audience interaction platforms expand engagement channels and fundamentally shift the unequal relationship between museums and visitors.

Deposition of highly crystalline AlScN thin films using synchronized high-power impulse magnetron sputtering: From combinatorial screening to piezoelectric devices

With the integration of 5G in day-to-day devices and the foreseeable 6G revolution, demand for advanced radio frequency (RF) microelectromechanical systems (MEMS) is growing. Aluminum scandium nitride (AlScN) has emerged as the material of choice for many of those applications due to its superior piezoelectric and electromechanical properties compared to aluminum nitride (AlN). However, synthesizing high-quality, textured AlScN thin films is challenging. Alloying of Sc in AlN induces structural frustration leading to strain, defects, disoriented grains, and disrupted crystal symmetry during growth. Higher deposition temperatures, while improving crystalline quality, risk undesirable phase precipitation and limit industrial sustainability. In addition, future MEMS technologies also demand conformal and textured coatings over diverse topographies. Addressing these challenges collectively requires new and innovative synthesis approaches. In this study, we investigate the feasibility of ionized physical vapor deposition to deposit highly oriented AlScN films with minimal defects at lower temperatures. To this end, we employ combinations of different deposition approaches, such as metal-ion synchronized (MIS) high-power impulse magnetron sputtering (HiPIMS). Leveraging the high ionization rates of HiPIMS and optimally timed substrate bias potentials, we selectively bombard the growing film with Al and/or Sc ions to enhance the adatom mobility at low temperatures while simultaneously providing the ability to tune stress and coat complex structures conformally. The nonequilibrium solubility of Sc in wurtzite AlN under different conditions is investigated using a combinatorial deposition approach. Promising candidates with ∼20% Sc composition are isolated and characterized for crystallinity and residual stress. Disoriented grains, a significant issue in growing AlScN films, are observed through atomic force microscopy and found to be completely removed by substrate rotation and application of substrate biasing. The measured piezoelectric response of the films with approximately 20% Sc concentration ranges from 6.3 to 8.8 pm/V, in line with density functional theory predictions and experimentally reported values for films deposited in a production tool with coplanar geometry. At the same time, MIS-HiPIMS-deposited films offer unique properties and flexibility to tune their stress state and structural properties, thus presenting exciting opportunities for the fabrication of advanced RF filters and next-generation MEMS devices. Published by the American Physical Society 2024

Integration of 5G, 6G and IoT with Low Earth Orbit (LEO) networks: Opportunity, challenges and future trends

The rapid growth of the massive smart Internet of Things (IoT) with mobile connections, the enhanced Mobile Broadband (eMBB) and the high demand for building a connected and intelligent world increase the probability of mobile satellite systems to be a major network in providing internet communication services in the future. Currently, the mobile satellite systems are envisioned as a significant solution for providing mobile services in different settings and for various vital objectives. These satellite systems have special qualities in each of these situations, including extensive coverage area, robustness, and ability to broadcast/multicast. The Low Earth Orbit (LEO) systems are the best promising technology that will offer internet services among the different types of satellite systems. However, the LEO systems are still experiencing certain restrictions with respect to connectivity, stability, and mobility support; because of which communication becomes unreliable. Therefore, the aim of this paper is to broadly explain the LEO systems and services in a comprehensive manner using a variety of perspectives. The paper focus is on key aspects of mobile internet based on satellite systems. This paper illustrates the integration of LEO systems with fifth and sixth generations of mobile cellular networks as well as with the IoT networks. It discusses the problems being faced as a result of the integration between cellular with IoT and satellite systems by comprehending which future research plans are outlined.

Innovative Modes of Distance Education in the Context of 5G Digital Technologies Implementation

This article delves into the complexities of adapting distance education to the advancements brought forth by the implementation of 5G digital technologies. The primary objective of the study is to elucidate the influence of 5G digital technologies on the adaptation of distance education models. With a hypothesis positing that the integration of 5G will catalyze the evolution of distance education towards more innovative and comprehensive educational models, the study employs a mixed-methods approach. The findings shed light on the significant impact of 5G digital technologies on distance education, facilitating the introduction of novel educational approaches that leverage enhanced connectivity and multimedia-rich learning experiences.

Weighted Robust Tensor Principal Component Analysis for the Recovery of Complex Corrupted Data in a 5G-Enabled Internet of Things

Technological developments coupled with socioeconomic changes are driving a rapid transformation of the fifth-generation (5G) cellular network landscape. This evolution has led to versatile applications with fast data-transfer capabilities. The integration of 5G with wireless sensor networks (WSNs) has rendered the Internet of Things (IoTs) crucial for measurement and sensing. Although 5G-enabled IoTs are vital, they face challenges in data integrity, such as mixed noise, outliers, and missing values, owing to various transmission issues. Traditional methods such as the tensor robust principal component analysis (TRPCA) have limitations in preserving essential data. This study introduces an enhanced approach, the weighted robust tensor principal component analysis (WRTPCA), combined with weighted tensor completion (WTC). The new method enhances data recovery using tensor singular value decomposition (t-SVD) to separate regular and abnormal data, preserve significant components, and robustly address complex data corruption issues, such as mixed noise, outliers, and missing data, with the globally optimal solution determined through the alternating direction method of multipliers (ADMM). Our study is the first to address complex corruption in multivariate data using the WTRPCA. The proposed approach outperforms current techniques. In all corrupted scenarios, the normalized mean absolute error (NMAE) of the proposed method is typically less than 0.2, demonstrating strong performance even in the most challenging conditions in which other models struggle. This highlights the effectiveness of the proposed approach in real-world 5G-enabled IoTs.

Empirical Insights into 5G Deployments in Highway Operational Environments and Comparative Performance with 4G

Despite the promising benefits, the integration of 5G, particularly through Non-Standalone (NSA) architectures that rely on existing 4G infrastructures, introduces challenges in maintaining optimal radio network performance and service quality. This study evaluates 4G and 5G radio network performance through empirical field trials across highway operational scenarios, a domain less scrutinized compared with urban environments. By conducting simultaneous measurements and comparing all available networks, this research focuses on dissecting the performance of critical quality indicators to gauge the intricacies of radio network behavior in high-speed travel conditions. Our findings illuminate distinct behavioral patterns, highlighting the unique challenges and optimization opportunities in these scenarios. Variability in signal strength across the highway routes underscores the influence of geography and infrastructure on coverage, while the relative stability in signal quality suggests the networks’ capability to maintain signal quality amid fluctuating strength. Interference results indicate effective management of signal interference, crucial for high-quality links, whereas latency and throughput metrics highlight a lag behind anticipated goals for reduced latency but promising data rates. This study not only showcases the variances in network quality and performance, thus pinpointing areas for operator-specific enhancements, but also emphasizes the comparison between the robustness of 4G infrastructures and the challenges in optimizing 5G networks.

Machine Learning for Wireless Network Throughput Prediction

This paper analyzes a dataset containing radio frequency (RF) measurements and Key Performance Indicators (KPIs) captured at 1876.6MHz with a bandwidth of 10MHz from an operational 4G LTE network in Nigeria. The dataset includes metrics such as RSRP (Reference Signal Received Power), which measures the power level of reference signals; RSRQ (Reference Signal Received Quality), an indicator of signal quality that provides insight into the number of users sharing the same resources; RSSI (Received Signal Strength Indicator), which gauges the total received power in a bandwidth; SINR (Signal to Interference plus Noise Ratio), a measure of signal quality considering both interference and noise; and other KPIs, all derived from three evolved node base stations (eNodeBs). After meticulous data cleaning, a subset of measurements from one serving eNB, spanning a 20-minute duration, was selected for deeper analysis. The PDCP DL Throughput, as a vital KPI metric, plays a paramount role in evaluating network quality and resource allocation strategies. Leveraging the high granularity of the data, the primary aim was to predict throughput. For this purpose, I compared the predictive capabilities of two machine learning models: Linear Regression and Random Forest. Metrics such as Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) were used to examine the models as they offer a comprehensive insight into the models’ accuracies. The comparative analysis highlighted the superior performance of the Random Forest model in predicting the PDCP DL Throughput. The insights derived from this research can potentially guide network engineers and data scientists in optimizing network performance, ensuring a seamless user experience. Furthermore, as the telecommunication industry advances towards the integration of 5G and beyond, the methodologies explored in this paper will be invaluable in addressing the increasingly complex challenges of future wireless networks.

ARTIFICIAL INTELLIGENCE AS A LEARNING MEDIA IN ENGLISH EDUCATION: BIBLIOMETRIC USING BIBLIOSHINY ANALYSIS (2009-2023)

This research examines the publication trends and emerging keywords within the domain of artificial intelligence applied to English education. Analyzing 74 documents sourced from Scopus using RStudio and Vosviewer revealed distinct patterns. Notably, the year 2021 marked a significant milestone with 26 articles, constituting 35.14% of total publications, while the preceding decade (2009-2020) contributed only 13 articles, representing less than 18%. However, the period from 2021 to 2023 experienced a noteworthy surge, introducing over 80% of the total publications, incorporating 60 new articles. Esteemed institutions like Hebei University of Engineering in China and three other affiliates each contributed 4 publications. The prominence of the ACM International Conference Proceedings Series underscores the influential role of technology conferences in shaping discussions around artificial intelligence in English education. An influential article, cited 20 times, focused on the integration of 5G and AI technologies to reform English education at universities, considering the necessity of cutting-edge technological infrastructure. Visual analysis highlights specific keywords such as speech recognition, quality control, teaching systems, and learning algorithms within yellow circles, suggesting potential areas for further research in the intersection of artificial intelligence and English education.

Impact of 5G Wireless Technologies on Cloud Computing and Internet of Things (IOT)

With its promise of unmatched speed, low latency, and widespread device connection, the introduction of 5G wireless technology represents a critical turning point in the development of telecommunications. This study examines how 5G would significantly affect cloud computing and the Internet of Things (IoT), two important technical areas.5G brings about a paradigm change in the field of cloud computing by facilitating quicker and more dependable connectivity between end-user devices and cloud servers. High data transmission rates and low latency enable real-time service delivery and processing, creating new opportunities for resource-intensive applications like edge computing, virtual reality, and augmented reality. The integration of 5G with cloud computing has the potential to transform cloud-based services' architecture and functionalities, enhancing their responsiveness and dynamism. Moreover, a new age of automation and communication is heralded by the merging of 5G with IoT. The large flood of IoT devices is accommodated by 5G's expanded network capacity, which facilitates smooth communication and coordination amongst them. By enabling IoT applications with never-before-seen speed and dependability, this synergy opens up new opportunities in industries including industrial automation, smart cities, healthcare, and agriculture. The convergence of 5G and IoT not only expedites the implementation of IoT solutions but also amplifies their efficiency and scalability. But there are obstacles in the way of 5G's revolutionary promise. As more and more devices are connected and data is exchanged, security and privacy issues become critical. In addition, significant financial outlays and careful planning are needed to meet the infrastructure needs for the rollout of 5G. The symbiotic link between 5G, cloud computing, and the Internet of Things is thoroughly examined in this article. It seeks to contribute to the knowledge of the technical environment and direct future research and development activities in order to fully realize the promise of 5G-enabled innovations by analysing the possibilities and difficulties posed by this convergence.

The Impact of 5G on IoT Ecosystems

The convergence of 5G and IoT is revolutionizing industries by enhancing connectivity, scalability, and security. This synergy enables efficient edge computing, reducing latency and optimizing data processing. Security measures like authentication and encryption are crucial for protecting sensitive data. The integration of 5G and IoT opens innovative business avenues, transforming manufacturing, logistics, healthcare, and more. Businesses can optimize operations, automate processes, and enhance customer experiences, making it essential to embrace this transformative potential for staying competitive and capitalizing on advancements.

Integration of 5G and GNSS Technologies for Enhanced Positioning: an Experimental Study

Integration of 5G and GNSS Technologies for Enhanced Positioning: an Experimental Study

Routing and Timeslot Scheduling for SPN Fine-Granularity Slices

The integration of 5G and vertical industries promotes the development of the energy Ethernet while putting forward fine granularity, flexibility, high reliability, and deterministic low-latency service requirements for the smart grid and the ubiquitous power Internet of Things (UPIoT). As the bearer architecture supporting the next-generation optical transmission network, the Slicing Packet Network (SPN) slice granularity decreases from 5 Gbps to 10 Mbps fine granularity and the frame period of 5 Gbps large-granularity slices is short, so the non-deterministic delay caused by timeslot conflicts has a negligible impact on the end-to-end delay, and the timeslot scheduling is unnecessary. However, due to the reduction in timeslot granularity and the change in frame structure in 10 Mbps slices, the scheduling of conflicting timeslots and the complex device computing management problems need to be solved urgently. In this paper, we establish a model of routing embedded timeslot scheduling for the routing of fine-granularity slices and timeslot scheduling problems in SPN-based FlexE interfaces, for which we propose a deterministic timeslot allocation mechanism supporting end-to-end low-latency transmission. According to the timeslot symmetry, the mechanism can reduce the space of feasible solutions through ant colony optimization and unidirectional neighborhood search (ACO-UNS), so as to efficiently solve the scheduling of conflicting timeslots and provide end-to-end delay guarantee for delay-sensitive services. Finally, we make a comparison between the ACO-UNS algorithm and the timeslot random dispatching algorithm (ACO-RD); the results show that, relative to the ACO-RD, the reduction in the proposed ACO-UNS is 98.721% for the end-to-end delay of fine-granularity slices.

A proposed customized architecture for 5G-IoT based smart healthcare system: 5G-IoT-SHS

The emergence of new technology is making enormous changes in healthcare which is the largest industry in the world. Therefore, a smart healthcare system is required that can communicate across different available network devices and applications. This will enable patients and doctors to trace, observe and record sensitive patient data with medical readings. The Internet of things (IoT) has a great contribution to put the healthcare sector in its leading state to provide the safe and healthy environment for patients and to enable doctors to give outstanding care remotely. The integration of 5G and IoT can be utilized to address these issues due to its capability to handle massive amounts of data with low latency and offers larger bandwidth, fast data rates, and greater reliability. It has heightened the patient involvement and satisfaction due to its capability to make the real time uncomplicated and competent communication among patients and doctors. Thus, a comprehensive smart healthcare architecture including 5G is required. This research is presented a literature review on various healthcare architectures and proposed a customized architecture for a 5G-IoT based smart healthcare system that incorporate different sensors, technology standards and communication protocols. The proposed architecture is validated using a software called Cisco Packet tracer, demonstrating the effectiveness and dependability of the suggested system. This work was carried out in Arya Bhatt Lab, GEU, Dehradun, India.

Thinking on the integration of 5G and industrial applications

Thinking on the integration of 5G and industrial applications

A novel deep deterministic policy gradient model applied to intelligent transportation system security problems in 5G and 6G network scenarios

Traffic congestion has been an actual problem in large cities, causing personal inconvenience and environmental pollution. To solve this problem, new applications for Intelligent Transportation System (ITS) have been created, to monitor actual traffic conditions. Therefore, fast, reliable and safe systems are desirable for creating a real intelligent transportation environment. Deep learning algorithms have been proposed for a better understanding of traffic behavior from a security-related perspective. Thus, we aim to maximize the safety problems using a deep learning algorithm, where a novel policy gradient model is presented for detecting vehicular misuse. The proposed model uses a triple network replay algorithm, maximizing the network convergence speed. Three networks are selected to optimize the policy network variables. Finally, the replay algorithm is partitioned with the aim of obtaining a faster model. Simulations on a real urban map are performed in a scenario with the integration of 5G or 6G networks. An architectural model for the integration of a Vehicular Ad-hoc Network (VANET) and cellular networks is determined in software-defined networking (SDN). The results show that the accuracy prediction of the proposed system presents better performance compared to related studies, where the proposed model increases its convergence speed and cumulative reward. Thus, the ITS improvement by the proposed deep learning algorithm increases the prediction accuracy, and reduces the transmission delay, treating the traffic path according to the congestion.

Resilient Deployment of Smart Nodes for Improving Confident Information Coverage in 5G IoT

The development of 5G has brought new opportunities for the application of Internet of Things (IoT). The integration of 5G and IoT technologies promote high availability, resilience, and reliability of the network infrastructures. IoT deployment optimization is the core issue of 5G IoT. Traditionally, IoT node deployment methods mostly used disk coverage model or probabilistic detection coverage model, which only utilizes the sensing capability of a single IoT node, which results in higher deployment costs. In this article, we study the network resilience of coverage estimation error and solve the coverage problem of resilient deployment of smart nodes in 5G IoT. The coverage formulation in the deployment optimization method is defined based on the confident information coverage (CIC). In order to obtain the optimal deployment with a given coverage quality and with a given budget, the mixed-integer linear programming models (CICILP-COST) and (CICILP-ERROR) are proposed based on the CIC model. After analyzing the model complexity, the proposed models are solved by the variable relaxation algorithm (CICVR-COST) and dichotomous search algorithm (CICDS-ERROR), respectively. Simulations on air pollution datasets in Lyon, France, show that the proposed model yields a lower cost optimal deployment than existing peer schemes.

Network Operator-Provided 5G Streaming Media and Future Broadcast Services

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Beyond the spectral efficiency and throughput improvement for enhanced mobile broadband (eMBB), 5G brings many new features targeting vertical applications. To leverage the new 5G features and capabilities for media distribution, 5G offers the 5G Media Streaming Architecture (5GMSA), which supports a full set of collaboration scenarios between third-party content providers and mobile network operators (MNOs) with various degrees of integration adapted to the over-the-top (OTT) ecosystem. While the first version of 5GMSA focuses on media delivery over unicast, multicast/broadcast in 5G is one of the key new features currently specified by 3GPP and expected for Release 17. Integration of 5G multicast/broadcast capabilities within 5GMSA is essential to scale up the network capacity for linear contents. With the publication of Digital Video Broadcast-I (DVB-I) in 2020, DVB allows for the delivery of linear television services to internet-connected devices over broadband and broadcast networks. As an access-independent service layer, DVB-I becomes a strong candidate for providing a converging service layer for 5G. This article explores the growing capabilities offered by the 5GMSA, how the multicast/broadcast capabilities will be integrated, and how it will enable the delivery of DVB-I services</i> .

MB-MaaS: Mobile Blockchain-based Mining-as-a-Service for IIoT environments

In this paper, we propose a mobile blockchain (MB) based mining-as-a-service (MaaS) scheme, MB-MaaS for resource-constrained industrial internet-of-things (IIoT) environments. The scheme addresses the research gaps of fixed static allocation for miners to perform computationally intensive mining tasks through a multi-hop computational offloading (CO) scheme and addresses an auction mechanism for a fair bidding process among the miner nodes. The scheme operates in three phases. In the first phase, a multi-hop CO scheme with a fair incentive policy is formulated for miners. The CO schemes offer guaranteed offloading services to mobile devices from far-edge systems through a chain of neighbor nodes. Then, in the second phase, MaaS is proposed to leverage expensive mining tasks through 5G-enabled pico/femtocells. Integration of 5G allows massive end-to-end device and service connectivity. As IIoT ecosystems have limited memory and compute requirements, MaaS assures that the proposed consensus has a responsive validation and mining time. To make the data exchange in the consensus process lightweight, and allow a large number of sensors to share the data in a lightweight manner, an effective consensus mechanism Lightweight Proof-of-Proximity (LPoP), is proposed that forms group validations instead of single block validation. The data is exchanged through javascript object notation (JSON) format, maintaining a steady transaction rate. MB-MaaS is compared against the existing scheme for parameters bid thresholds and request servicing times, and mining and consensus formation. For example, the request serving time at 12 requests is improved by 56.78%, and a significant improvement of 26.47% is observed for processed blocks; parsing time on average is improved by 7.89%. The comparative analysis suggests that the scheme is more efficient than other competing approaches.

A sustainable deep learning framework for fault detection in 6G Industry 4.0 heterogeneous data environments

The integration of 5G and Beyond 5G (B5G)/6G in Machine-to-Machine (M2M) communications, is making Industry 4.0 smarter. However, the goal of having a sustainable self-monitored industry has not been reached yet. State-of-the-art deep learning-based Fault Detection algorithms cannot handle heterogeneous data, meaning that more than one fault detection computational device has to be used for each data format, in addition to the inability to take advantage of the combination of all the information available in different formats to derive more accurate conclusions. Moreover, these algorithms rely on inefficient hyper-parameters tuning strategies. In this paper, we propose an Advanced Deep Learning framework for Fault Diagnosis in Industry 4.0 (ADL-FDI4), which combines Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN) and graph CNN (GNN), to handle heterogeneous data. Furthermore, our novel framework uses a Branch-and-Bound procedure to guide the learning process. Our experimental results show that ADL-FDI4 outperforms the state-of-the-art solutions in terms of detection rate and running time, and for that, it consumes less energy. In addition to handling heterogeneous data, which implies that one computational device is sufficient to handle all data formats.

Influence of Knowledge Sharing on Students’ Learning Ability under the Background of “5G+AI”

The integration of 5g and AI realizes the intelligent communication and deep interaction between educators and learners, and promotes the reconstruction of various education and teaching resources in space and time dimensions. With the wide application of 5g and AI technology, knowledge sharing has become ubiquitous. The willingness and behavior of knowledge sharing provide a huge space for students to improve their learning ability. Taking 5g + AI new technology as the intermediary variable, this study analyzes the impact of knowledge sharing on students' learning ability. The results show that the overall reliability coefficient is 0.787. The reliability of the questionnaire is high, and the Kaiser Meyer Olkin value is 0.756, ranging from 0.7 to 0.8. The validity of the research data is good. The willingness to share knowledge has a positive role in promoting the improvement of students' learning ability. Knowledge sharing behavior plays a positive role in promoting the improvement of students' learning ability. "5g + AI" new educational technology plays an intermediary role in actively promoting knowledge sharing behavior and improving learning ability. The research results have important reference value for fully understanding the impact of knowledge sharing on the improvement of students' learning ability under the background of 5g + AI.