5G User Research Articles

A Near-Field-Based Gain and Pattern Measurement Technique for Probe-Fed Millimeter-Wave Antennas [Measurements Corner

Driven by the perpetual demand for higher data rates, extensive research efforts have been devoted to millimeter-wave (mm-wave) 5G technologies in recent years. Highly integrated mm-wave antennas for 5G user equipment are considered one of the key components in the realization of mm-wave-based mobile networks. An accurate characterization of antennas is as important as antenna design. Despite great advances in measurement capabilities over the years, we are still facing many difficulties in probe-based mm-wave antenna measurements. This article presents a fast, simple, and accurate near-field (NF)-to-far-field (FF) technique for gain and radiation pattern measurements of probe-fed mm-wave antennas. A setup based on an electro-optical (EO) NF measurement system (NeoScan) is utilized to measure the NF of the antenna under test (AUT). The system uses a high-spatial-resolution nonintrusive probe to scan the NF in very close proximity to the antenna’s surface, which significantly alleviates multipath effects and reduces the required scan area. The radiation pattern of the AUT is computed using the NF-to-FF transformation. To obtain the gain of the AUT, a ground-backed dipole antenna is created and used as a gain standard. The absolute gain of the ground-backed dipole is characterized using the image method. Measurement accuracy is checked against the simulation. With the help of the standard gain antenna, the NF-to-FF method can be used to measure both the gain and radiation pattern of any type of probe-fed mm-wave antenna. For demonstration purposes, two test antennas, a horizontally polarized tapered slot antenna, and a dual-band vertically polarized hexagonal bridge antenna operating at 28 GHz are fabricated and characterized. Excellent agreement against full-wave simulation is achieved.

The DAG blockchain: A secure edge assisted honeypot for attack detection and multi-controller based load balancing in SDN 5G

Software-defined networking (SDN) has increased the need for security due to the participation of illegitimate packets resulting from poor processing times and inadequate resource utilizations. In recent days, wireless 5G users, e.g., Internet of Things (IoT) users, have accessed networks from great distances due to their mobility, which requires multiple handovers between communication technologies. However, the process generates illegitimate packets due to connectivity changes. This paper addresses the security issue by using a modified blockchain and handover authentication. The access points (APs) in the infrastructure plane authenticate the 5G users with a hash generation using their identities and pseudo IDs with a lightweight QUARK algorithm. If an excessive number of users are connected with the same AP, then the users’ handover is performed by edge servers based on probabilities. In the data plane, OpenFlow switches perform a flow rule validation and a honeypot implementation for performing the packet validation. Classification of packets into normal, malicious and suspicious packets is also performed at the edge server using a capsule neural network (CapsNet). Deployment of NFV-enabled virtual switches (vSwitch) reduces switch overloading based on the load threshold. For faster validation, a directed acyclic graph (DAG) is implemented at the control plane to store the hashed credentials of the users for authentication and the hashed flow rules for flow rule validation. Suspicious packet validation is performed at the control plane by the controller using the Soft Actor–Critic (SAC) algorithm, and the Honey Badger Optimization algorithm (HBO) is used to select an optimal underloaded controller for efficient load balancing. This model is developed in NS-3, and the results show that our model outperforms the existing approaches in terms of QoS metrics such as bandwidth, response time, delay and packet loss, and security metrics such as detection accuracy and authentication time.

Security Threats to Voice Services in 5G Standalone Networks

With the rapid development of 5G SA (standalone) networks, increasing subscribers are motivated to make calls through 5G. To support voice services critical to mobile users, 5G SA networks adopt two solutions: VoNR (Voice Over New Radio) and EPS (Evolved Packet System) fallback. At this stage, 5G SA networks provide voice services through EPS fallback, which leverages 4G networks to support voice calls for 5G users. This switch between cellular network systems may expose vulnerabilities to adversaries. However, there is a lack of security research on voice services in the 5G SA network. In this paper, we analyze the security of EPS fallback and its closely related IMS from the perspective of the protocol and the practices of the carriers. We uncover two protocol design vulnerabilities and two implementation flaws. In addition, we exploit them to design three attacks: voice DoS, voice monitoring, and SMS spoofing and interception. We validated these vulnerabilities and attacks using SDR (software-defined radio) tools and a set of open-source software in three mobile carriers. Our analysis reveals that the problems stem from both specifications and carrier networks. We finally propose several potential countermeasures to defend these attacks.

Self-adaptive Differential Evolution based Optimized MIMO Beamforming 5G Networks

The industrial factory is one of the challenging environments for future wireless communication systems, where the goal is to produce products with low cost in short time. This high level of network performance is achieved by distributing massive MIMO that provides indoor networks with joint beamforming that enhances 5G network capacity and user experience as well. Judging from the importance of this topic, this study introduces a new optimization problem concerning the investigation of multi-beam antenna (MBA) coverage possibilities in 5G network for indoor environments, named Base-station Beams Distribution Problem (BBDP). This problem has an extensive number of parameters and constrains including user’s location, required data rate and number of antenna elements. Thus, BBDP can be considered as NP-hard problem, where complexity increases exponentially as its dimension increases. Therefore, it requires a special computing method that can handle it in a reasonable amount of time. In this study, several differential evolution (DE) variants have been suggested to solve the BBDP problem. The results show that among all DE variants the self-adaptive DE (jDE) can find feasible solutions and outperform the classical ones in all BBDP scenarios with coverage rate of 85% and beam diameter of 500 m.

Mobile Collaborative Secrecy Performance Prediction for Artificial IoT Networks

The integration of artificial intelligence and Internet of Things (IoT) has promoted the rapid development of artificial IoT (AIoT) networks. A wide range of AIoT applications have generated a great deal of data. The fifth-generation (5G) mobile communication has powerful data processing capabilities, and it is a key technology to enable AIoT big data processing. The explosive growth of the 5G users has made information security in AIoT networks a significant issue. Real-time security evaluation in AIoT networks is difficult due to user mobility and dynamic wireless environments. Thus, the evaluation and prediction of secrecy performance is a very critical research. In this article, new expressions for the nonzero secrecy capacity probability (NSCP) are derived to evaluate the mobile collaborative secrecy performance. An improved convolutional neural network (CNN) model, named as SI-CNN in this article, is proposed to predict the NSCP performance. The SI-CNN model combines the SqueezeNet and InceptionNet, and it has four convolution layers, which all adopt the same convolution model. For the first two layers, they employ a 2 × 1 convolution and a three-branch convolution, which not only increase the number of channels but also extract more features. For the last two layers, they employ the same structure, but different convolution kernels. The proposed SI-CNN prediction algorithm is shown to provide better NSCP performance prediction than other state-of-the-art methods. In particular, compared with wavelet neural network, the prediction precision of SI-CNN is improved by 26.8%.

Understanding the Mediating Effect of Anchoring Price in Extant Mature 4G and Market-Creating 5G Technology Products

This study explores the influence of anchoring price (ANE) on consumer behavior using a quantitative research method. An online survey was piloted, yielding 246 useable responses from 4G and 5G users in China. SmartPLS 3.00 was used to analyze the data, and the assumptions were assessed using the partial least squares-based structural equation modeling (PLS-SEM) technique. The empirical findings reveal that the anchoring price (ANE) component partially mediates the connections between purchase intention (CPI) and behavior (CPB) of 5G technology items. This is the first study to investigate the impact of anchoring price (ANE) on intention–behavior relationships. Manufacturers and marketers of 5G technology goods that want to influence positive changes in consumer behavior should look into the study’s findings and implications.

Deep Neural Network Model Construction for Digital Human Resource Management with Human-Job Matching.

This article uses deep neural network technology and combines digital HRM knowledge to research human-job matching systematically. Through intelligent digital means such as 5G communication, cloud computing, big data, neural network, and user portrait, this article proposes the design of the corresponding digital transformation strategy of HRM. This article further puts forward the guaranteed measures in enhancing HRM thinking and establishing HRM culture to ensure the smooth implementation of the digital transformation strategy of the HRM. This system uses charts for data visualization and flask framework for background construction, and the data is stored through CSV files, My SQL, and configuration files. The system is based on a deep learning algorithm for job applicant matching, intelligent recommendation of jobs for job seekers, and more real help for job applicants to apply for jobs. The job intelligent recommendation algorithm partly adopts bidirectional long and short-term memory neural network (Bi-LSTM) and the word-level human post-matching neural network APJFNN built by the attention mechanism. By embedding the text representation of job demand information into the representation vector of public space, a joint embedded convolutional neural network (JE-CNN) for post matching analysis is designed and implemented. The quantitative analysis method analyzes the degree of matching with the job.

LTCC-Based Antenna-in-Package Array for 5G User Equipment With Dual-Polarized Endfire Radiations at Millimeter-Wave Frequencies

An antenna-in-package (AiP) of endfire and dual-polarization at 28 GHz is presented. It is formed by four periodic antennas of broad bandwidth and high-isolation, fabricated by the low-temperature cofired ceramics technology. The dual-polarizations are realized by integrating a horizontal metal stripline-coupled dipole with a vertical magneto-electric monopole, incorporated into a multilayer architecture for compactness. The AiP skillfully implements vertical vias and horizontal strip lines in the cavities between antennas to improve isolation and polarization purity. This AiP has numerically been examined for endfire high gain and beam steering in user equipment (UE) applications. The prototype was measured to show good isolation lower than −25 dB from 26.5 to 29.5 GHz and agree with full-wave simulations. The antenna array is finally integrated with 1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 4 boresight radiation microstrip antennas to form a complete UE AiP.

Run-Time Adaptive In-Kernel BPF/XDP Solution for 5G UPF

Flexibility is considered a key feature of 5G softwarization to deliver a timely response to changes in network requirements that may be caused by traffic variation, user mobility, dynamic network function chains, slice lifecycle management operations, among others. In this article, we evolve the upf-bpf1 open-source project by proposing a new design to improve its flexibility by reducing the run-time adaptation time. The project proposes an in-kernel solution based on BPF and eXpress Data Path (XDP) for 5G User Plane Function (UPF) implementations. The Just-In-Time (JIT) compilation may have a huge impact on the adaptation time due to the in-kernel verification of the BPF programs at run-time. Our results show latency improvements of around 95% to inject the BPF program into the Linux kernel. Furthermore, the solution keeps the same functionalities and delivers a packet processing performance of around 10–11 Mpps using 6 cores with almost 70% of the CPU utilization in downlink/uplink directions.

Reconfigurable Intelligent Surfaces: A signal processing perspective with wireless applications

Antenna array technology enables the directional transmission and reception of wireless signals for communication, localization, and sensing purposes. The signal processing algorithms that underpin it began to be developed several decades ago <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , but it was with the deployment of 5G wireless mobile networks that the technology became mainstream <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . The number of antenna elements in the arrays of 5G base stations (BSs) and user devices can be measured on the order of hundreds and tens, respectively. As networks shift toward using higher-frequency bands, more antennas fit into a given aperture. For communication purposes, the arrays are harnessed to form beams in desired directions to improve the signal-to-noise ratio (SNR) and multiplex data signals in the spatial domain (to one or multiple devices) and to suppress interference by spatial filtering <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . For localization purposes, these arrays are employed to maintain the SNR when operating across wider bandwidths, for angle-of-arrival estimation, and to separate multiple sources and scatterers <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> . The practical use of these features requires that each antenna array is equipped with well-designed signal processing algorithms.

A comprehensive review on the users’ identity privacy for 5G networks

Fifth Generation (5G) is the final generation in mobile communications, with minimum latency, high data throughput, and extra coverage. The 5G network must guarantee very good security and privacy levels for all users for these features. Therefore, researchers have deliberated the privacy and security solution of 5G users. The 5G wireless network offers a futuristic concept that helps to solve challenges affecting previous communications generations. The key concern to many scholars in the field of mobile networking is user privacy, which is long-term subscription identifier as International Mobiles Subscribers Identifiers (IMSIs) and short-term subscription identifier as Temporary Mobiles Subscribers Identifiers and Cell-Radio Networks Temporary Identifiers (TMSIs and C-RNTIs), which are used for permanent identifying, paging, and location update. This article investigates the existing literature survey about user privacy for 5G networks, which continues the identity and location privacy. Also, it discusses most of the studies that handle user identifications in authentication, paging, and location update. This article discusses the various privacy issues in the 5G network that use IMSI in clear text or temporary identities such as TMSI & C-RNTI with IMSI to disclose user identity privacy. This article also investigates the existing literature on user identity and location privacy and highlights the key parameters, issues, challenges, and future recommendations with potential solutions.

Performance Analysis of Massive MIMO-OFDM System Incorporated with Various Transforms for Image Communication in 5G Systems

Modern-day applications of fifth-generation (5G) and sixth-generation (6G) systems require fast, efficient, and robust transmission of multimedia information over wireless communication medium for both mobile and fixed users. The hybrid amalgamation of massive multiple input multiple output (mMIMO) and orthogonal frequency division multiplexing (OFDM) proves to be an impressive methodology for fulfilling the needs of 5G and 6G users. In this paper, the performance of the hybrid combination of massive MIMO and OFDM schemes augmented with fast Fourier transform (FFT), fractional Fourier transform (FrFT) or discrete wavelet transform (DWT) is evaluated to study their potential for reliable image communication. The analysis is carried over the Rayleigh fading channels and M-ary phase-shift keying (M-PSK) modulation schemes. The parameters used in our analysis to assess the outcome of proposed versions of OFDM-mMIMO include signal-to-noise ratio (SNR) vs. peak signal-to-noise ratio (PSNR) and SNR vs. structural similarity index measure (SSIM) at the receiver. Our results indicate that massive MIMO systems incorporating FrFT and DWT can lead to higher PSNR and SSIM values for a given SNR and number of users, when compared with in contrast to FFT-based massive MIMO-OFDM systems under the same conditions.

Principle of 6G Wireless Networks: Vision, Challenges and Applications

The 5G mobile communication has now become commercially available. Furthermore, research across the globe has begun to improve the system beyond 5G and it is anticipated that 6G will deliver higher quality services and energy efficiency than 5G. The mobile network architecture needs to be redesigned to meet the requirements of the future. In the wake of the commercial rollout of the 5G model, both users and developers have realized the limitations of the system when compared to the system's original premise of being able to support the vast applications of connected devices. The article discusses the related technologies that can contribute to a robust and seamless network service. An upheaval in the use of vast mobile applications, especially those powered and managed by AI, has opened the doors to discussion on how mobile communication will evolve in the future. 6G is expected to go beyond being merely a mobile internet service provider to support the omnipresent AI services that will form the rock bed of end-to-end connected network-based devices. Moreover, the technologies that support 6G services and comprehensive research that enables this level of technical prowess have also been identified here. This paper presents a collective wide-angle vision that will facilitate a better understanding of the features of the 6G system.

Testing 5G User Equipment: Review, Challenges, and Gaps from the Medical Device Perspective.

5G technology can enable novel healthcare applications and augment existing medical device use of wireless technology to improve healthcare delivery. Having the dual role of medical device and cellular user equipment (UE), 5G-enabled devices navigate a plethora of rules and tests before they can operate on a 5G network to deliver their intended medical function. In this paper, we review the state-of-the-art of 5G UE certification programs and testing specifications. We then identify test challenges and discuss the relevance of existing test practices to support the 5G-enabled medical device safety and effectiveness. This information is useful to medical device developers planning to incorporate 5G technology in their products and wireless engineers working to expand mobile services into the healthcare vertical.

Simulation and Field Trial Results of Reconfigurable Intelligent Surfaces in 5G Networks

Recently, reconfigurable intelligent surfaces (RISs) are considered as a strong candidate for next generation wireless technologies, thanks to its advantage of being able to configure the wireless propagation environment in a cost-effective and energy-efficient way. Many literature study the theoretical aspects of RIS-assisted communication while prototyping and field trials are relatively scarce. A key step towards the standardization and commercialization of RISs is to complete comprehensive field trials in cellular networks, such as the 5th generation (5G) network. There are several typical deployment scenarios in 5G networks such as indoors, outdoors and mixed indoors and outdoors, where RISs can provide coverage to weak reception areas, enhance transmission robustness, fix coverage holes and increase the maximum available data rate. In this paper, a variety types of RIS prototypes are fabricated and tested with off-the-shelf 5G user equipments (UEs) and 5G base stations (BSs) to validate the performance gain introduced by RISs to typical deployment scenarios of 5G at different working frequencies. Some system-level simulations are also conducted for several typical scenarios to be used as a baseline to verify that the test results are theoretically correct. All test parameters and signals are selected according to 5G standards to reflect the performance of RISs in real-world 5G networks. The experimental results confirm the feasibility and effectiveness of RISs to solve coverage issues in 5G networks across different frequency ranges.

Practical Papers, Articles and Application Notes

5G technology has led to many novel healthcare delivery applications. The 5G-enabled medical devices also double up as cellular user equipment (UE), and therefore must comply with EMC regulations before they can operate on a 5G network. The first paper, &#x201C;Testing 5G User Equipment: Review, Challenges, and Gaps from the Medical Device Perspective&#x201D;, reviews the state-of-the-art of 5G UE certification programs and testing specifications. It also identifies test challenges and discusses the relevance of existing test practices to support the 5G-enabled medical devices. This paper is informative and useful to medical device developers planning to incorporate 5G technology in their products.

Profit-Aware Edge Server Placement

In a 5G network, mobile-edge computing (MEC) plays a key role in providing low access delay services. The placement of edge servers not only determines the quality of services on the user side but also affects the profit of running a MEC system. In this article, we study how to properly place edge servers so as to guarantee the access delay and maximize the profit of edge providers. We first propose a profit model which involves both access delay and energy consumption. In this model, we take the 5G user plane function (UPF) into consideration to calculate access delay for the first time. Then, we devise a particle swarm optimization-based algorithm to optimize the profit. In the algorithm, we introduce a weight value <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> to guarantee the access delay and assign base stations properly. Moreover, a service-level agreement is adopted to balance the tradeoff between access delay and energy consumption. We take advantage of our 5G network emulator called mini5Gedge and data set from Shanghai Telecom to conduct massive experiments. The results show that our algorithm stands out in terms of achieving the highest profit.

Latency Reduction in Vehicular Sensing Applications by Dynamic 5G User Plane Function Allocation with Session Continuity.

Vehicle automation is driving the integration of advanced sensors and new applications that demand high-quality information, such as collaborative sensing for enhanced situational awareness. In this work, we considered a vehicular sensing scenario supported by 5G communications, in which vehicle sensor data need to be sent to edge computing resources with stringent latency constraints. To ensure low latency with the resources available, we propose an optimization framework that deploys User Plane Functions (UPFs) dynamically at the edge to minimize the number of network hops between the vehicles and them. The proposed framework relies on a practical Software-Defined-Networking (SDN)-based mechanism that allows seamless re-assignment of vehicles to UPFs while maintaining session and service continuity. We propose and evaluate different UPF allocation algorithms that reduce communications latency compared to static, random, and centralized deployment baselines. Our results demonstrated that the dynamic allocation of UPFs can support latency-critical applications that would be unfeasible otherwise.

Efficient solutions to the placement and chaining problem of User Plane Functions in 5G networks

This study attempts to solve the placement and chaining problem of 5G User Plane Functions (UPFs) in a Multi-access Edge Computing (MEC) ecosystem. The problem is formalized as a multi-objective Integer Linear Programming (ILP) model targeted at optimizing provisioning costs and quality of service. Our model takes into account several aspects of the system such as UPF-specific considerations, the Service Function Chain (SFC) requests topology (single and multiple branches), Virtual Network Function (VNF) order constraints, service demands, and physical network capacities. Since the formulated problem is NP-hard, two heuristic solutions are devised to enhance solution efficiency. Specifically, an algorithm called Priority and Cautious-UPF Placement and Chaining (PC-UPC) and a simulated annealing (SA) meta-heuristic are proposed. Through extensive simulation experiments, we evaluated the performance of the proposed solutions. The results revealed that our solutions outperformed the baselines (i.e., two greedy-based heuristics and a variant of the classical SA) and that we had obtained nearly optimal solutions with significant reductions in running time. Moreover, the PC-UPC algorithm can effectively avoid SFC rejections and improve provisioning costs by considering session requirements, current network conditions, and the effects of VNF mapping decisions. Additionally, the proposed SA approach incorporates several mechanisms (e.g., variable Markov chain length and restart–stop) that allow the improvement of not only the quality of the solutions but also their computation time.

A 28 GHz 2 × 2 Antenna Array with 10 Beams Using Passive SPDT Switch Beamforming Network.

In this paper, a dual-polarized four-port 2 × 2 series fed antenna array operating at 28 GHz with beam-switching capability is proposed. The antenna array uses a simple passive beamforming network to switch the main beam. The presented antenna design is suitable for 5G user equipment and high data rates applications by which it has a compact size with low cost and complexity. The size of the antenna is 37.2 × 37.2 mm including the ground plane, and it produces 10 different switched beams by using only two simple 3 dB/90 couplers which create the required amplitudes and phase excitations for the antenna elements. A one-port simple feeding mechanism including Peregrine PE42525 SPDT switch modules and a power divider is used to generate and measure the 10 switched beams. The antenna design is implemented on a two-layer 0.203 mm thick low-loss ( = 0.0027) Rogers 4003C substrate, and it has a measured 10 dB impedance bandwidth of 4 GHz (14.3%, from 26 GHz to 30 GHz) for all ports. Measured peak isolation between any dual-polarized ports of the antenna is better than 30 dB. The antenna has an average measured realized gain of 8.9 dBi and around 10 dB side lobe level (SLL) for all beams. The antenna has 3-dB coverage of 80 to 90 in 2D space and it has a maximum of ±26 beam-steering angle. The antenna is designed and simulated using Ansys HFSS and fabricated using regular PCB processing.