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The deployment of fifth-generation mobile network (5G), beyond 5G and sixth-generation mobile network platforms encounters challenges of blockage, interference, and path loss in radio mobile environments. Metasurfaces provide a promising solution to address these limitations. In this paper, we present a methodology for developing ultrathin flexible metasurface-based frequency selective surfaces (FSSs). Our approach combines thermal evaporation for metallic thin films with a macroscopic metasurface mask (something analogous to screen-printing but using thermal evaporation instead of inks). As a proof of concept, we fabricate a sub-6 GHz metasurface-based FSS using gold deposition on a flexible polyethylene terephthalate substrate. Experimental results are validated through numerical full-wave simulations using COMSOL Multiphysics and equivalent-circuit model simulations. The metasurface operates within the primary frequency band utilized in 5G networks (3–5 GHz), indicating its potential applicability across a wide range of flexible, conformal, and wearable devices. The fabricated FSS can be installed on surfaces of any shape, such as flat or curved windows, as well as on walls or other external surfaces. This methodology offers practical solutions for wireless communications and enhancing signal transmission in diverse environments. [ABSTRACT FROM AUTHOR]

An experimental demonstration of directivities exceeding the fundamental Kildal limit, a phenomenon called superdirectivity, is provided for spherical high-index dielectric antennas with an electric dipole excitation. A directivity factor of about 10 with a total efficiency of more than 80% for an antenna having a size of a third of the wavelength was measured. High directivities are shown to be associated with constructive interference of particular electric and magnetic modes of an open spherical resonator. Both analytic solutions for a point dipole and a full-wave rigorous simulation for a realistic dipole antenna were employed for optimization and analysis, yielding an excellent agreement between experimentally measured and numerically predicted directivities. The use of high-index low-loss ceramics can significantly reduce the physical size of such antennas while maintaining their overall high radiation efficiency. Such antennas can be attractive for various high-frequency applications, such as antennas for the Internet of Things, smart city systems, 5G network systems, and others. The demonstrated concept can be scaled in frequency. [ABSTRACT FROM AUTHOR]

This study presents a planar dual-band multiple-input multiple-output (MIMO) antenna design for the prospective fifth-generation (5G) frequency bands of 28 and 38 GHz. The antenna element is designed by utilizing a rectangular patch with an offset microstrip feeding technique. A dual-band response is achieved by placing semi-circular slots on each side of the patch element. To tune the frequency response and improve impedance matching, vertical rectangular slits are etched in the rectangular patch and the ground plane, respectively. The results show that the single antenna element offers an impedance bandwidth of 2.52 GHz (26.32–28.84 GHz) and 7.5 GHz (34–41.5 GHz). In addition, a MIMO configuration based on pattern diversity using four antenna elements is designed and fabricated. The designed MIMO configuration achieves an impedance bandwidth of 3 GHz (27–30 GHz) and 5.46 GHz (35.54–41 GHz) at operating bands of 28 and 38 GHz. The peak realized gain for the single element at 28 and 38 GHz is noted to be 7.4 dBi and 7.5 dBi, respectively. Furthermore, the polarization diversity configuration illustrates an isolation of > 15 dB and > 25 dB for the 28 and 38 GHz frequency bands, respectively. Moreover, the MIMO configuration attains appropriate values for the envelope correlation coefficient (ECC) and diversity gain (DG), Total Active Reflection Co-efficient (TARC), Channel Capacity Loss (CCL) and Mean Effective Gain (MEG) for the operating frequency bands. The proposed MIMO system based on results seems to be potential choice for mmwave Ka Band Applications. [ABSTRACT FROM AUTHOR]

In this article, the designing and analysis of a compact novel dual-port multiple-input multiple-output (MIMO) printed antenna are investigated for 30 GHz mm-wave applications. The single antenna unit is having modified C-shaped radiator and an attached rectangular stub with an overall dimension of 10 × 7 × 0.8 mm3. The dual-element MIMO antenna is achieved by creating a back mirror of the single antenna element around the x-axis. This back mirror composition of the MIMO antenna elements is introducing the high level of inter-element isolation (> 20 dB). The proposed antenna prototype is built on a Roger RT/duriod substrate with a loss tangent (tanδ) of 0.0009 and a relative constant (εrsub) of 2.2. As a way to determine the capabilities of the proposed MIMO antenna, many diversity parameters are computed, including the envelope correlation coefficient (ECC < 0.05), diversity gain (DG > 9.99 dB), channel capacity loss (CCL < 0.2 bits/s/Hz), mean effective gain (MEG < − 3 dB), and total active reflection coefficient (TARC). The suggested MIMO antenna is appropriate for 5G new radio frequency bands under mm-wave communication as it has 8.36% impedance bandwidth across the frequency range of simulated (29.04–31.57 GHz)/measured (28.82–31.30 GHz). The antenna under consideration is constructed, and the simulated outcomes are verified by the measurement results. [ABSTRACT FROM AUTHOR]

Summary: This research project takes on a crucial role in the quickly changing field of integrated 5G networks inside smart environments by concentrating on the creation of an extremely effective sleep scheduling system designed especially for Narrowband Internet of Things (NB‐IoT) devices. This work introduces a unique method for precisely controlling the nodes' sleep schedules through the use of convolutional neural network (CNN) architecture, which optimizes both energy usage and operating patterns. The principal goal still stands to guarantee the extended lifetime of operation and dependability of NB‐IoT devices in the larger framework of intelligent ecosystems driven by synchronized 5G networks. To achieve its objectives, the research explores a number of complex domains and employs cutting‐edge technologies and techniques, such as CNN‐based pattern recognition. This method's real‐time component is essential since it allows for prompt modifications to sleep schedules in order to optimize energy savings. Further boosting the devices' effectiveness and flexibility is continuous contact with a central server, which guarantees that the devices are updated with the most recent data and instructions. Essentially, the main objective of this study is to greatly increase the energy economy and operational lifetime of NB‐IoT devices, which will allow for stable and long‐lasting IoT deployments in the context of 5G networks in intelligent settings. This advancement is not only a boon for businesses and industries leveraging IoT technology but also a substantial step toward building smarter, more energy‐efficient, and resilient smart ecosystems that benefit society as a whole. [ABSTRACT FROM AUTHOR]

Summary: 5G and beyond (5GB) systems show a flexible characteristic to meet different user communications requirements in parallel. Multi‐numerology waveform design is an important part of this human centric characteristic, however, it is necessary to make multi‐numerology planning. In this paper, the integrated sensing and communications (ISAC) concept is used with machine learning (ML) techniques to develop an intelligent numerology control mechanism for 5GB. Thanks to the ISAC capabilities, an environment‐aware system is designed, and several useful sensing information (USI) that affect the wireless channel characteristic is utilized. It is assumed that the USI is obtained through 5GB smart city networks. At that point, synthetic data generation is performed to form a new dataset with USI‐based features. Additionally, a novel feature control algorithm is proposed in the paper. Lastly, simulation results are presented for different type of ML models including several ensemble methods. [ABSTRACT FROM AUTHOR]

Millimeter wave MIMO wireless communication systems are deployed in 5G and next‐generation networks. The effectiveness of deep learning models for improving the performance of these systems has been proven in the literature. However, several deep learning models are vulnerable to security threats, such as adversarial attacks. Therefore, for the deployment of these systems, it is essential to make them resilient to such kinds of attacks for good quality secure communication. Adversarial training is a solution by which deep learning models are trained for adversarial attacks beforehand. Adversarial training for three types of adversarial attacks, that is, Fast Gradient Sign Method, Iterative Fast Gradient Sign Method, and Momentum Iterative Fast Gradient Sign Method is implemented in this paper. The simulation results depict a decrease in the error at the receiving end after adversarial training, even after an adversarial attack has been applied. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Reliability is one big concern for future communication networks such as 5G and beyond to support massive machine-type communications. These networks face reliability issues due to the frequent movement of users or/and a deep fading environment, particularly in urban areas. To overcome this bottleneck, a reliable communication protocol is proposed in this paper by incorporating the hybrid automatic repeat request (HARQ) along with non-orthogonal multiple access (NOMA) schemes. The NOMA scheme helps in improving spectral efficiency and maintaining fairness among the users, while HARQ initiates a retransmission of the user signal in case of unsuccessful decoding of the received signal. Unlike existing work, the proposed scheme allows the base station (BS) to utilise the available resources most efficiently by initiating the retransmission of the signal of the user who failed to decode the received signals. A two-user scenario is considered in this framework and all the possible combinations of positive and negative acknowledgement feedback from users are analysed. Depending upon the feedback received from the receiver, BS exploits transmission mode diversity during signal retransmission. The extensive Monte Carlo simulations are executed to analyse the outage performance of the proposed communication system. To validate the efficacy of the proposed scheme, numerically simulated results are compared with the existing research works. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) mobile networks are being deployed all over the world and researchers are concerned about the network's security against hacking. Researchers have focused heavily on this topic in recent years as new technologies attempt to integrate into numerous sectors of corporate and social organizations. Malicious software referred to as malware, is an unwanted application that attackers frequently use to execute online attacks. Advanced packing and obfuscation methods are being used by malware variants to continue their evolution. Due to several application features in 5G networks such as APIs, calls and SMS, it is difficult to detect and classify malware attacks. In a variety of research areas, deep learning (DL) techniques are effectively utilized to address malware-related solutions. Especially, convolutional neural networks (CNN) played a crucial role in the classification of malware detection in 5G networks and the Internet of Things (IoT). In this work, a lightweight CNN architecture with a sequential Long Short-Term Memory (LSTM) layer is developed for malware detection and classification trained on the Malimg dataset. The results prove that the proposed approach achieves 99.8% accuracy with an F1-score of 0.9925 for malware detection and outperforms state-of-the-art approaches in the literature. The classification performance is improved up to 12.8% and 14% in terms of accuracy and F1-score, respectively when compared with existing malware classification models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Real‐time telemedicine detection can solve the problem of the shortage of public medical resources caused by the coming aging society. However, the development of such an integrated monitoring system is hampered by the need for high‐performance sensors and the strict‐requirement of long‐distance signal transmission and reproduction. Here, a bionic crack‐spring fiber sensor (CSFS) inspired by spider leg and cirrus whiskers for stretchable and weavable electronics is reported. Trans‐scale conductive percolation networks of multilayer graphene around the surface of outer spring‐like Polyethylene terephthalate (PET) fibers and printing Ag enable a high sensitivity of 28475.6 and broad sensing range over 250%. The electromechanical changes in different stretching stages are simulated by Comsol to explain the response mechanism. The CSFS is incorporated into the fabric and realized the human‐machine interactions (HMIs) for robot control. Furthermore, the 5G Narrowband Internet of Things (NB‐IoT) system is developed for human healthcare data collection, transmission, and reproduction together with the integration of the CSFS, illustrating the huge potential of the approach in human–machine communication interfaces and intelligent telemedicine rehabilitation and diagnosis monitoring. [ABSTRACT FROM AUTHOR]

In this paper, a new microstrip triplexer is designed to work at 2.5 GHz, 4.4 GHz and 6 GHz for mid-band 5G applications. All channels are flat with three low group delays (GDs) of 0.84 ns, 0.75 ns and 0.49 ns, respectively. Compared to the previously reported works, the proposed triplexer has the minimum group delay. The designed triplexer has 18.2%, 13.7%, 23.6% fractional bandwidths (FBW%) at 2.5 GHz, 4.4 GHz and 6 GHz, respectively. The obtained insertion losses (ILs) are low at all channels. These features are obtained without a noticeable increase in the overall size. A novel and simple resonator is used to design the proposed triplexer, which includes two pairs of coupled lines combined with a shunt stub. A perfect mathematical analysis is performed to find the resonator behavior and the layout optimization. The type of shunt stub is determined mathematically. Also, the smallness or largeness of some important physical dimensions is determined using the proposed mathematical analysis. Finally, the designed triplexer is fabricated and measured, where the measurement results verify the simulations. [ABSTRACT FROM AUTHOR]

Summary: A novel antenna system that utilizes multiple‐input and multiple‐output (MIMO) technology to suit the requirements of 5G applications is proposed in this article. The antenna has a wide operating bandwidth and high isolation in n257/n258/n261 5G bands. The suggested arc‐shaped MIMO antenna is composed of four similar patch units with modified ground planes arranged in a four‐port configuration and is made on a Rogers RT/duroid 5880 substrate, which measures 31.891 × 37.5885 × 0.508 mm3. The prototype of the suggested MIMO antenna is realized to confirm the obtained simulated results and validate the proposed design. The four‐port MIMO antenna features a broad frequency range of 7.3 GHz from 21.8 to 29.1 GHz, a peak gain of 5.76 dBi at 29 GHz, and a good isolation less than −18 dB. The proposed antenna's MIMO performance is evaluated by analyzing several diversity parameters. The findings of the evaluation demonstrate an envelope correlation coefficient (ECC) value of less than 0.001, a diversity gain (DG) value exceeding 9.995 dB, and an average mean effective gain (MEG) of −3 dB within the operational frequency band. The suggested MIMO antenna's compact size and impressive performance make it a suitable choice for intended n257/n258/n261 5G NR networks. [ABSTRACT FROM AUTHOR]

Summary: The millimeter‐wave (mmW) band has been chosen to obtain multi‐gigabit per second in the fifth generation (5G) communication and beyond. The knowledge of this frequency band is still the focus of research today. After UTI announced its choice of mmW for 5G communication, massive studies were carried out on this band. Among these studies, many optimization methods were proposed to reduce the impact of degradation sources. In this paper, we propose a new method to optimize the system error vector magnitude (EVM) of complex systems with many different calibration sources. A difference of 0.1% is observed between our optimization method and old methods. This small improvement of the EVM system can push the 3GPP system EVM (EVM at the receiver) limits by a few centimeters, which is not negligible in a high‐frequency transmission. Moreover, we have also proposed a new method to compute the throughput that takes into account the TX and RX directional antenna heights. The measurement campaigns and simulation results have shown an improvement from 0% to 6% of throughput. The results depend on indoor environment study and different obstacles in NLOS (non‐line of sight) conditions. [ABSTRACT FROM AUTHOR]

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-μRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%. [ABSTRACT FROM AUTHOR]

In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of O (n 2) to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution. [ABSTRACT FROM AUTHOR]

Despite significant progress has been made in image and video deblurring, much less attention has been paid to process ultra high-definition (UHD) videos (e.g., 4K resolution). In this work, we propose a novel deep model for fast and accurate UHD video deblurring (UHDVD). The proposed UHDVD is achieved by a depth-wise separable-patch architecture, which operates with a multi-scale integration scheme to achieve a large receptive field without adding the number of generic convolutional layers and kernels. Additionally, we adopt the temporal feature attention module to effectively exploit the temporal correlation between video frames to obtain clearer recovered images. We design an asymmetrical encoder–decoder architecture with residual channel-spatial attention blocks to improve accuracy and reduce the depth of the network appropriately. Consequently, the proposed UHDVD achieves real-time performance on 4K videos at 30 fps. To train the proposed model, we build a new dataset comprised of 4K blurry videos and corresponding sharp frames using three different smartphones. Extensive experimental results show that our network performs favorably against the state-of-the-art methods on the proposed 4K dataset and existing 720p and 2K benchmarks in terms of accuracy, speed, and model size. [ABSTRACT FROM AUTHOR]

Continuous deployment has been practiced for many years by companies developing web‐ and cloud‐based applications. To succeed with continuous deployment, these companies have a strong collaboration culture between the operations and development teams. In addition, these companies use AI, analytics, and big data to assist with time‐consuming postdeployment activities such as continuous monitoring and fault identification. Thus, the term AIOps has evolved to highlight the importance and difficulty of maintaining highly available applications in a complex and dynamic environment. In contrast, software‐intensive embedded systems often provide customer product‐related services, such as maintenance, optimization, and support. These services are critical for these companies as they provide significant revenue and increase customer satisfaction. Therefore, the objective of our study is to gain an in‐depth understanding of the impact of continuous deployment on product‐related services provided by software‐intensive embedded systems companies. In addition, we aim to understand how AIOps can support continuous deployment in the context of software‐intensive embedded systems. To address this objective, we conducted a case study at a large and multinational telecommunications systems provider focusing on the radio access network (RAN) systems for 4G and 5G networks. The company provides RAN products and three complementing services: rollout, optimization, and customer support. The results from the case study show that the boundaries between product‐related services become blurry with continuous deployment. In addition, product‐related services, which were conducted in sequence by independent projects, converge with continuous deployment and become part of the same project. Further, AIOps platforms play an important role in reducing costs and increasing postdeployment activities' efficiency and speed. These results show that continuous deployment has a profound impact on the software‐intensive system's provider service organization. The service organization becomes the connection between the R&D organization and the customer. In order to cope with the increased speed of releases, deployment and postdeployment activities need to be largely automated. AIOps platforms are seen as a critical enabler in managing the increasing complexity without increasing human involvement. [ABSTRACT FROM AUTHOR]

A four-port low-profile broadband patch antenna is designed for fifth-generation (5G) multi-input multi-output (MIMO) mobile applications. The designed antenna loads shorting pins S1 and S2 on a square patch with etched fan-leaf slits, making the parasitic patch work more visible. In addition, loading shorting pin S3 excites a low-frequency resonant mode, which enables the antenna to completely cover frequency ranges of 3.3–5 GHz (5G N77 and N79 bands). The effective height of the designed antenna is only 3.7 mm (0.05λ at 4.15 GHz). The antenna achieves a good balance between impedance bandwidth and profile, realizing wide bandwidth coverage. To confirm the precision of the results, the designed antenna is fabricated and measured. The measured results show that the −6 dB impedance bandwidth covers 3.3–5 GHz. All the isolation between ports are superior to 12 dB. The envelope correlation coefficients (ECCs) is below 0.15 and the overall efficiency is between 50% and 90% in the 3.3–5 GHz. [ABSTRACT FROM AUTHOR]

This paper presents a four-port multiple-input multiple-output (MIMO) magneto-electric dipole antenna array with high gain and high isolation for 5G millimeter-wave communications. Each antenna contains an eight-way power divider, an eight-element magneto-electric dipole antenna array, a metal rectangular ring, and a dielectric slab. The proposed magneto-electric dipole antenna array has high radiation gain. The metal rectangular ring is applied to not only support the dielectric slab but suppress the surface wave propagation and thus the mutual coupling can be reduced. A further increase in the isolation performance can be realized based on the polarization diversity. The simulated and measured impedance bandwidths at 10 dB return losses are 24–31.86 GHz (28.1%) and 24.12–32 GHz (28.1%), respectively. The simulated and measured isolation levels are larger than 25 and 20 dB, respectively. The simulated and measured realized peak gains at 30 GHz are 20.5 and 19.2 dBi, respectively. [ABSTRACT FROM AUTHOR]

In the article, the extreme problem of finding the optimal placement plan of 5G base stations at certain points within a linear area of finite length is set. A fundamental feature of the author's formulation of the extreme problem is that it takes into account not only the points of potential placement of base stations but also the possibility of selecting instances of stations to be placed at a specific point from a defined excess set, as well as the aspect of inseparable interaction of placed 5G base stations within the framework of SON. The formulation of this extreme problem is brought to the form of a specific combinatorial model. The article proposes an adapted branch-and-bounds method, which allows the process of synthesis of the architecture of a linearly oriented segment of a 5G network to select the best options for the placement of base stations for further evaluation of the received placement plans in the metric of defined performance indicators. As the final stage of the synthesis of the optimal plan of a linearly oriented wireless network segment based on the sequence of the best placements, it is proposed to expand the parametric space of the design task due to the specific technical parameters characteristic of the 5G platform. The article presents a numerical example of solving an instance of the corresponding extremal problem. It is shown that the presented mathematical apparatus allows for the formation of a set of optimal placements taking into account the size of the non-coverage of the target area. To calculate this characteristic parameter, both exact and two approximate approaches are formalized. The results of the experiment showed that for high-dimensional problems, the approximate approach allows for reducing the computational complexity of implementing the adapted branch-and-bounds method by more than six times, with a slight loss of accuracy of the optimal solution. The structure of the article includes Section 1 (introduction and state-of-the-art), Section 2 (statement of the research, proposed models and methods devoted to the research topic), Section 3 (numerical experiment and analysis of results), and Section 4 (conclusions and further research). [ABSTRACT FROM AUTHOR]

5G communication technology has been skyrocketing, and has changed everything in our daily lives, and its applications in various fields are also promising. However, the thermal conductivity/dissipation problems of highly integrated electronic devices and electrical equipment are becoming more and more prominent, and thermodynamics offers a solution, and fractal meta-surfaces provides an extremely efficient approach to transfer the generated waste heat, which can be used for thermal energy harvesting, and a fractal thermodynamic model is developed for thermal management. [ABSTRACT FROM AUTHOR]

In the evolving landscape of Industry 4.0, the integration of advanced wireless technologies into manufacturing processes holds the promise of unprecedented connectivity and efficiency. In particular, the data transmission in a heavy industry environment needs stable connectivity with mobile operators. This paper deals with the performance study of 4G and 5G mobile signal coverage within a complex factory environment. For this purpose, a cost-effective and portable measurement setup was realized and used to provide long-term measurement campaigns monitoring and recording several key parameter indicators (KPIs) in 4G/5G downlink and upload. To support the reproducibility of the provided study and other research activities, the measured dataset is publicly available for download. Among others findings, the obtained results show how the performance of 4G/5G is influenced by a heavy industry environment and of the time of day on the network load. [ABSTRACT FROM AUTHOR]

Many emerging applications, such as factory automation, electric power distribution, and intelligent transportation systems, require multicast Ultra-Reliable Low-Latency Communications (mURLLC). Since 3GPP Release 17, 5G systems natively support multicast functionality, including multicast Hybrid Automatic Repeat Request and various feedback schemes. Although these features can be promising for mURLLC, the specifications and existing studies fall short in offering guidance on their efficient usage. This paper presents the first comprehensive system-level evaluation of mURLLC, leveraging insights from 3GPP specifications. It points out (i) how mURLLC differs from traditional multicast broadband wireless communications, and (ii) which approaches to provide mURLLC require changing the paradigm compared with the existing solutions. Finally, the paper provides recommendations on how to satisfy strict mURLLC requirements efficiently, i.e., with low channel resource consumption, which increases the capacity of 5G systems for mURLLC. Simulation results show that proper configuration of multicast mechanisms and the corresponding algorithms for mURLLC traffic can reduce resource consumption up to three times compared to the baseline solutions proposed for broadband multicast traffic, which significantly increases the system capacity. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is what we have as a great breakthrough in the 5G network. Although the 5G network can support several Internet of Everything (IoE) services, 6G is the network to fully support that. This paper is a survey research presenting the 5G and IoT technology and the challenges coming, with the 6G network being the new alternative network coming to solve these issues and limitations we are facing with 5G. A reference to the Control Plane and User Plane Separation (CUPS) is made with IPv4 and IPv6, addressing which is the foundation of the network slicing for the 5G core network. In comparison to other related papers, we provide in-depth information on how the IoT is going to affect our lives and how this technology is handled as the IoE in the 6G network. Finally, a full reference is made to the 6G network, with its challenges compared to the 5G network. [ABSTRACT FROM AUTHOR]

To ensure the safe and stable operation of 5G base stations, it is essential to accurately predict their power load. However, current short‐term prediction methods are rarely applied rationally in pertinent circumstances to the features of base station power load over time. For high accuracy and generalization capabilities, this work proposes a hybrid approach that combines gated recurrent unit (GRU) with particle swarm optimization (PSO) and completes ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The complex load is decomposed into multiple components to improve the fit of the neural network. To deal with the nonlinearity that constrains the modal aliasing and data noise, CEEMDAN is used to reconstruct the trend and noise sequences. The GRU network is utilized to improve the neural network fitness and obtain long‐term features. In addition, PSO is used to optimize the number of neurons and the learning rate of the GRU. Compared with existing neural network GRUs, the RMSE is reduced by 16.6, the mean absolute error is reduced by 11.97, and the coefficient of R2 is improved by 0.13, indicating that the model has a better fitting effect. The comparisons prove that the proposed model has better accuracy than the existing methods. [ABSTRACT FROM AUTHOR]

The rapid deployment of 5G networks necessitates innovative solutions for efficient and dynamic resource allocation. Current strategies, although effective to some extent, lack real-time adaptability and scalability in complex, dynamically-changing environments. This paper introduces the Dynamic Resource Allocator using RL-CNN (DRARLCNN), a novel machine learning model addressing these shortcomings. By merging Convolutional Neural Networks (CNN) for feature extraction and Reinforcement Learning (RL) for decision-making, DRARLCNN optimizes resource allocation, minimizing latency and maximizing Quality of Service (QoS). Utilizing a state-of-the-art “5G Resource Allocation Dataset”, the research employs Python, TensorFlow, and OpenAI Gym to implement and test the model in a simulated 5 G environment. Results demonstrate the effectiveness of DRARLCNN, showcasing an impressive R2 score of 0.517, MSE of 0.035, and RMSE of 0.188, surpassing existing methods in allocation efficiency and latency. The DRARLCNN model not only outperforms existing methods in allocation efficiency and latency but also sets a new benchmark for future research in dynamic 5G resource allocation. Through its innovative approach and promising results, DRARLCNN opens avenues for further advancements in optimizing resource allocation within dynamic 5G networks. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, the digital transformation of industrial systems necessitates advanced wireless communication solutions that are both agile and secure. Private 5G networks, characterized by their unparalleled bandwidth, reduced latency, and fortified security, have emerged as a beacon of innovation in this paradigm shift. This study explores the feasibility of deploying private 5G networks across diverse enterprises, underpinned by a robust methodological framework. Drawing from contemporary research, it is evident that while 5G promises transformative benefits for sectors like logistics, manufacturing, and autonomous vehicles, its adoption is riddled with challenges such as network oversaturation and frequency coordination. Analyzing 2006 companies, our findings reveal that only 103 enterprises align with the optimal criteria for private 5G network implementation, highlighting the intricate balance of spatial, economic, and regulatory considerations. This research not only offers a strategic roadmap for businesses and stakeholders but also positions private 5G networks as a pivotal tool for achieving operational excellence, enhanced data privacy, and seamless connectivity in the modern industrial landscape. [ABSTRACT FROM AUTHOR]

In this paper, we adopt a proposal to reduce the complexity and increase the throughput in time domain using 5G. This is achieved by speeding up the conventional architecture in advance. Using this method, the number of coefficients of feedback filter is reduced, thus decreasing the hardware complexity. The convergence strategy and steady state error performance are also increased without any additional hardware requirements. This is based on storing the past values of decision maker in a separate look up table and updating it time to time using parallel error multiplexer. Using this method, the results are very efficient by decreasing the power, area and logic utilization of the new architecture. If we include this concept as a hands-on experience, students can learn and also optimize the architecture easily. [ABSTRACT FROM AUTHOR]

The object of the research is the energy-efficient multimedia networks based on 5G wireless access technologies. The research is aimed at developing energy-efficient multimedia networks based on 5G wireless access technologies, considering the increasing importance of information and communication technologies (ICT) in modern society. With the proliferation of wireless access networks and the advancement of fifth-generation mobile networks (5G), there is a need to assess and reduce the environmental impact of ICT. The article specifically focuses on the challenges related to energy consumption and CO2 emissions in radio access networks, highlighting the responsibility to balance technological advancements with environmental concerns. The paper examines various components and technologies necessary for enhancing energy efficiency in multimedia networks. It discusses the concept of multimedia, including digital storage, data processing, and interactive elements. Statistical data is provided to underscore the significant energy consumption and carbon footprint of the ICT industry, with an emphasis on radio access networks. Heterogeneous networks, non-orthogonal multiple access (NOMA) technologies, and multiple-input multiple-output (MIMO) technologies are identified as key components for achieving energy efficiency. The importance of reducing the distance between transmitters and receivers in heterogeneous networks is emphasized, as well as the use of energy-saving strategies such as putting small base stations into sleep mode during low network loads. Special attention is given to the role of green data centers in reducing CO2 emissions and optimizing the use of green energy in high-performance networks. Proposed methods include leveraging renewable energy sources, improving hardware energy efficiency, and implementing energy-efficient routing. The findings offer valuable insights for the development and implementation of energy-efficient multimedia networks, particularly in the context of 5G networks. The interdisciplinary approach advocated in the conclusion emphasizes the collective efforts needed to address environmental challenges in the field of information and communication technologies. The combination of these technologies ensures efficient resource utilization and reduced environmental impact compared to similar known approaches. [ABSTRACT FROM AUTHOR]

Objective: Public exposure to radiofrequency (RF) fields from fifth generation (5G) and other sources is known to be below human exposure limits. The interaction of RF fields with the human body has been widely documented, with tissue heating being the primary consequence for RF fields above 100 kHz. This study aimed to reveal possible harm to orthodontic patients associated with 5G electromagnetic waves. The possibility of individuals with orthodontic appliances (metallic braces, porcelain braces, thermoplastic appliances) being more affected by 5G was investigated. Methods: Sixty extracted human teeth were divided into 5 groups. Different types of brackets, arch, and ligature wires were applied to each group. Each group was exposed to 5G electromagnetic waves (3.6 GHz of frequency) for 60 minutes. Temperature measurements were made inside the canine root canals and in saline solution. Results: Exposure to a 5G electromagnetic field increased the temperatures of the root canal of the tooth and sodium chloride (NaCl) solution surrounding samples. Temperature increase in the canals were as follows; metal self-ligating braces > mini metal braces > porcelain braces > clear aligner > control. The temperature change in the NaCl solution at the 60th minute was close to each other in the self-ligating braces and mini metal braces groups. The temperature rise of the NaCl solution in the control group was also minimal. Conclusion: The hypothesis that orthodontic materials alter electromagnetic waves is supported by temperature increases. The ferromagnetic density of the orthodontic materials used was shown to be closely connected to temperature increase. [ABSTRACT FROM AUTHOR]

Our society is being transformed by the technology emergence and the industrial revolution. The advances in the internet and artificial intelligence are reshaping the means of education, profoundly changing the ways of teaching and learning. This paper studies the pattern of how the new 5th generation blended campus network is applied to aid the new generation of intelligence teaching. This pattern is the implementation of national major policies and the measure of cultivating people. This paper introduces a new model for the intelligence teaching system. Based on the new model, distance interaction teaching system, VR practicing teaching system, intelligence testing system, and higher education intelligence decision system are developed. This model can be the basis of the informatization of future education. [ABSTRACT FROM AUTHOR]

Smart farming is revolutionizing agriculture by integrating advanced technologies to enhance productivity, efficiency, and sustainability. This paper proposes a novel, 5G-enabled Pest and Disease Detection and Response System (PDDRS) that synergizes environmental sensor data with image analytics for comprehensive Plant Disease Detection (PDD). By leveraging the high bandwidth and ultra-low latency capabilities of 5G, our integrated system surpasses traditional communication technologies, facilitating real-time data analytics and immediate intervention strategies. We introduce two Machine Learning (ML) models: an image-based Mask R-CNN with FPN, which achieves a precision of 91.1% and an accuracy of 95.1%, and an environmental-based FFNN + LSTM model, evaluated for ACC, AUC, and F1-Score, showing promising results in disease forecasting. Our experiments demonstrate that the PDDRS significantly enhances throughput and latency performance under various connected devices, showcasing a scalable, cost-effective solution suitable for next-generation smart farming. These advancements collectively empower the PDDRS to deliver actionable insights, enabling targeted applications such as precise pesticide deployment, and stand as a testament to the potential of 5G in agricultural innovation. [ABSTRACT FROM AUTHOR]

Railway communication is undergoing a transitional period worldwide, and 5G for railway (5G-R), which has been adopted by the Railway International Union (UIC) in China and in many other countries, which is a common concern all over the world. The 2100 MHz frequency band, which ranks among the top three mainstream frequency bands for public 5G network deployment globally, has recently been issued by MIIT for a 5G-R trial in China, and the frequency is fairly close to 5G-R frequency band of 1900 MHz in Europe. Propagation study for railway communication, especially for mainline, which is the most common and widely used scenario, is of great importance, because it is closely related to passengers' lives and properties. In this article, based on the 5G-R dedicated network testing environment constructed along the test line of loop railway of National Railway Track Test Center of China, the 2100 MHz 5G-R propagation measurement campaign is conducted, with an in-service SS-RSRP signal-based passive 2100 MHz large-scale channel testing system, as well as a SDR-based 2100 MHz 5G-R channel sounding system, based on which the large-scale as well as small-scale channel characteristics are derived and extensively analyzed. With data derived from the passive testing system, the classical FI, CI as well as TR 38.901 large-scale channel models are properly fitted and compared by calculating RMSE as well as MAE values. Moreover, based on the SDR-based channel sounding system, conclusions on the relationship between the parameters of multi-path component numbers, RMS time spread, received power and distance between the transmitter and the receiver are drawn. The 5G-R system studied in this letter is symmetric FDD communication system, apart from the 2 aspects of channel modeling characteristics, which are large-scale and small-scale complementing each other, the research methodology adopted of the letter also has two aspects, passive measurement, as well as active measurement, and both methods have their own advantages and different focuses, which can also complements each other. Relevant research results of this letter will be helpful for facilitating the R&D, deployment, as well as network optimization of a future mobile communication system under railway mainline scenario. [ABSTRACT FROM AUTHOR]

The internet of medical things (IoMT) has transformed the healthcare sector by facilitating real-time monitoring, remote patient care, and tailored healthcare solutions. However, the challenge of upholding a high standard quality of service (QoS) in IoMT implementations remains a pressing issue. This article delves into the possibilities of utilizing 5G networks and smart techniques to optimize QoS within IoMT systems. By capitalizing on the capabilities of 5G networks, including substantial bandwidth, minimal latency, and extensive connectivity, in conjunction with intelligent methods such as machine learning and predictive analytics, this paper introduces novel strategies to enhance QoS in IoMT environments. The summary underscores the advantages of these methods in elevating network dependability, diminishing latency, enhancing data transmission efficiency, and enabling resource allocation efficiency in IoMT deployments. Additionally, it explores the potential ramifications of these developments on healthcare outcomes, patient contentment, and the overall effectiveness of the healthcare system. The conclusions propose that by maximizing QoS through 5G networks and intelligent techniques, IoMT holds the potential to significantly enhance the delivery of healthcare services, fostering a more interconnected and efficient healthcare ecosystem in the process innovation. [ABSTRACT FROM AUTHOR]

In this paper, we design and simulate a 28/38 GHz dual-band multiple-input multiple-output (MIMO) patch antenna array that operates in the FR2 frequency band (28 GHz and 38 GHz). This antenna array comprises four similar patch antennas with a rectangular "L minus two slots" shape. In addition, it applies to 5G electronic components such as a smartphone. We used high-frequency structure simulator (HFSS) software to perform the design and simulation for this antenna. In addition, this proposed antenna array provides better performance, such as; bandwidth around 28 GHz is equal to 0.69 GHz and 38 GHz is equal to 0.86 GHz, equal gains 5. 9 dB at 28 GHz and 9 dB at 38 GHz, directivities are 6.3 dB at 28 GHz and 9.4 dB at 38 GHz, the efficiency of 95.38% at 28 GHz, and efficiency of 96.53% at 38 GHz. [ABSTRACT FROM AUTHOR]

This study proposes a deep learning method for pavement defect detection, focusing on identifying potholes and cracks. A dataset comprising 10,828 images is collected, with 8662 allocated for training, 1083 for validation, and 1083 for testing. Vehicle attitude data are categorized based on three-axis acceleration and attitude change, with 6656 (64%) for training, 1664 (16%) for validation, and 2080 (20%) for testing. The Nvidia Jetson Nano serves as the vehicle-embedded system, transmitting IMU-acquired vehicle data and GoPro-captured images over a 5G network to the server. The server recognizes two damage categories, low-risk and high-risk, storing results in MongoDB. Severe damage triggers immediate alerts to maintenance personnel, while less severe issues are recorded for scheduled maintenance. The method selects YOLOv7 among various object detection models for pavement defect detection, achieving a mAP of 93.3%, a recall rate of 87.8%, a precision of 93.2%, and a processing speed of 30–40 FPS. Bi-LSTM is then chosen for vehicle vibration data processing, yielding 77% mAP, 94.9% recall rate, and 89.8% precision. Integration of the visual and vibration results, along with vehicle speed and travel distance, results in a final recall rate of 90.2% and precision of 83.7% after field testing. [ABSTRACT FROM AUTHOR]

The sharing of mobile network infrastructure has become a key topic with the introduction of 5G due to the high costs of deploying such infrastructures, with neutral host models coupled with features such as network function virtualization (NFV) and network slicing emerging as viable solutions for the challenges in this area. With this in mind, this work presents the design, implementation, and test of a flexible infrastructure-sharing 5G network architecture capable of providing services to any type of client, whether an operator or not. The proposed architecture leverages 5G's network slicing for traffic isolation and compliance with the policies of different clients, with roaming employed for the authentication of users of operator clients. The proposed architecture was implemented and tested in a simulation environment using the UERANSIM and Open5GS open-source tools. Qualitative tests successfully validated the authentication and the traffic isolation features provided by the slices for the two types of clients. Results also demonstrate that the proposed architecture has a positive impact on the performance of the neutral host network infrastructure, achieving 61.8%-higher throughput and 96.8%-lower packet loss ratio (PLR) in a scenario sharing the infrastructure among four clients and eight users when compared to a single client with all the network resources. [ABSTRACT FROM AUTHOR]

A novel aperture‐shared dual‐wideband 5G antenna with dual polarization in the sub‐6 GHz band and multi‐input multi‐output (MIMO) function in the 28‐GHz band is presented in this work. A dual‐polarized megnetoelectric dipole (ME‐dipole) with wide bandwidth is introduced in the sub‐6 GHz band. Based on the partial structure reuse method, four 2 × 2 substrate integrated waveguide (SIW) cavity‐backed patch arrays are integrated into the electric dipole structure of the ME‐dipole to construct a 28‐GHz 2 × 2 MIMO system. Two high‐order SIW cavity modes that cooperate with patch modes are used to enhance bandwidth in the 28‐GHz band. For demonstration, a prototype of the proposed antenna was fabricated and measured. The simulated results are in good agreement with the measured results, showing wide bandwidths of 39.6% and 10.85% in the sub‐6 GHz and the 28‐GHz bands, respectively. In addition, excellent measured in‐band and cross‐band isolations of over 44.4 dB and 45.2 dB are obtained, respectively. [ABSTRACT FROM AUTHOR]

In the realm of the fifth-generation (5G) wireless cellular networks, renowned for their dense connectivity, there lies a substantial facilitation of a myriad of Internet of Things (IoT) applications, which can be supported by the massive machine-type communication (MTC) technique, a fundamental communication framework. In some scenarios, a large number of machine-type communication devices (MTCD) may simultaneously enter the communication coverage of a target base station. However, the current handover mechanism specified by the 3rd Generation Partnership Project (3GPP) Release 16 incurs high signaling overhead within the access and core networks, which may have negative impacts on network efficiency. Additionally, other existing solutions are vulnerable to malicious attacks such as Denial of Service (DoS), Distributed Denial of Service (DDoS) attacks, and the failure of Key Forward Secrecy (KFS). To address this challenge, this paper proposes an efficient and secure handover authentication protocol for a group of MTCDs supported by blockchain technology. This protocol leverages the decentralized nature of blockchain technology and combines it with certificateless aggregate signatures to mutually authenticate the identity of a base station and a group of MTCDs. This approach can reduce signaling overhead and avoid key escrow while significantly lowering the risk associated with single points of failure. Additionally, the protocol protects device anonymity by encrypting device identities with temporary anonymous identity markers with the Elliptic Curve Diffie–Hellman (ECDH) to abandon serial numbers to prevent linkage attacks. The resilience of the proposed protocol against predominant malicious attacks has been rigorously validated through the application of the BAN logic and Scyther tool, underscoring its robust security attributes. Furthermore, compared to the existing solutions, the proposed protocol significantly reduces the authentication cost for a group of MTCDs during handover, while ensuring security, demonstrating commendable efficiency. [ABSTRACT FROM AUTHOR]

In this manuscript, we present a novel deployment protection method aimed at safeguarding aeronautical radio altimeters (RAs) from interference caused by fifth-generation (5G) telecommunication base stations (BSs). Our methodology involves an integrated interference model for defining prohibited zones and utilizes power control and angle shutoff methods to mitigate interference. First, to ensure reliable protection, we define both horizontal and vertical prohibited zones and investigate their variations to immunize RA against 5G interference. Second, we validate the effectiveness of the model in various operational scenarios, analyzing the influence of factors such as base station types, antenna parameters, flight altitude, and aircraft attitudes to cover a wide range of real-world scenarios. Third, to mitigate interference, we propose and analyze the power control and angle shutoff methods through simulation for the RMa prohibited zone. Our results demonstrate the efficacy of the deployment protection method in safeguarding RAs from 5G interference, providing guidance for interference protection during civil aviation operations and base station deployment near airports. [ABSTRACT FROM AUTHOR]

The error performance of the 5G low‐density parity‐check codes is significantly impacted by the update order of layers. At present, the row degree (RD) and the number of punctured bits (PBs) are widely used to decide the update order of layers. Here, the authors propose a metric to assess the soft message recovery speed of coded bits and then suggest updating layers with the lowest RD first rather than those with the least PBs first. Besides, a new criterion is introduced to further determine the update order of layers with the same number of PBs and the same RD. The resulting reordered layered schedule yields better error performance and faster convergence speed than the prior arts. [ABSTRACT FROM AUTHOR]

In this letter, an optimal calibration factor for mutual information effective signal‐to‐interference‐plus‐noise ratio (SINR) mapping (MIESM) scheme in fifth‐generation new radio (5G NR) is provided. By applying the optimal calibration factor to the MIESM scheme, the estimation accuracy of the effective SINR can be improved. The optimal calibration factor minimizes the mean squared error (MSE) of the difference between the estimated effective SINR and the required SNR under additive white Gaussian noise (AWGN) at a block error rate (BLER) of 10%. Simulation results show that the optimized MIESM scheme outperforms the non‐optimized MIESM scheme. [ABSTRACT FROM AUTHOR]

A 90° hybrid coupler operating at a central frequency of 5.40 GHz is introduced, offering high-performance characteristics with compact dimensions of 616.65 mm² (33.3 mm × 18.5 mm). These features are achieved while maintaining a design that makes it easy to produce. The unique aspect of this microstrip FR4 substrate-based design is the consistent S-parameters, which remain steady despite changes in structural dimensions, thereby making it adaptable to fabrication variations. The reported S-parameters measured in 5.4 GHz are as follows:|S11|= –29.46 dB,|S21|= –4.11 dB,|S31|= –3.96 dB, and|S41|= –21.23 dB. This hybrid coupler operates over a bandwidth of 0.8 GHz (16%), demonstrating remarkable performance within this spectrum. In addition to its design, the coupler exhibits robust resilience against changes in substrate parameters and structural dimensions, ensuring reliability during the fabrication process. Following thorough simulation studies, a physical prototype of the coupler was constructed and subjected to laboratory measurements. The experimental results align closely with the simulation data, validating the accuracy and predictability of the design. In comparison with other studies and designs documented in the literature, this compact, high-performance 90° hybrid coupler exhibits clear advantages in terms of size, isolation, phase deviation, and structural complexity. Therefore, the benefits and applicability of this presented structure in the context of microwave circuits and systems are underscored. It's noteworthy to mention that the central frequency of 5.4 GHz falls within the sub-6 GHz 5G band range. [ABSTRACT FROM AUTHOR]

Network traffic prediction is an important network monitoring method, which is widely used in network resource optimization and anomaly detection. However, with the increasing scale of networks and the rapid development of 5-th generation mobile networks (5G), traditional traffic forecasting methods are no longer applicable. To solve this problem, this paper applies Long Short-Term Memory (LSTM) network, data augmentation, clustering algorithm, model compression, and other technologies, and proposes a Cluster-based Lightweight PREdiction Model (CLPREM), a method for real-time traffic prediction of 5G mobile networks. We have designed unique data processing and classification methods to make CLPREM more robust than traditional neural network models. To demonstrate the effectiveness of the method, we designed and conducted experiments in a variety of settings. Experimental results confirm that CLPREM can obtain higher accuracy than traditional prediction schemes with less time cost. To address the occasional anomaly prediction issue in CLPREM, we propose a preprocessing method that minimally impacts time overhead. This approach not only enhances the accuracy of CLPREM but also effectively resolves the real-time traffic prediction challenge in 5G mobile networks. [ABSTRACT FROM AUTHOR]