5G Network Services Research Articles

Model and Methods of Traffic Routing in a Communication Network Using UAVs

Relevance. The development of 5G networks and subsequent generations is accompanied by the development of new services, in particular, virtual, augmented reality services, as well as telepresence, as well as radio access networks. In particular, there is an increase in operating frequencies, which poses additional challenges for organizing a network that can meet the requirements for the quality of traffic service from new services and ensure the availability of communication to users. These problems can be solved by various methods of placing access points, including using UAVs. This approach ensures the efficiency of construction and flexibility of the access network structure, but also requires the use of methods for placing access points in relation to users and other elements of the communication network. Problem statement: development of methods for placing routers in a UAV swarm and selecting traffic routes when organizing an access network, in order to improve the efficiency of the communication network. Purpose of the work: improving the efficiency of building an access network using UAVs through the development of clustering methods and distributing routers in a UAV swarm. Methods. The studies were carried out using the provisions of information theory, mathematical optimization methods, graph theory methods and clustering methods. The numerical results were obtained using the numerical simulation method in Python. Result. The developed model and methods allow for the distribution of network routers (access points) located on UAVs taking into account the quality of service and ensuring the construction of a connected mesh network and its connection with the mobile network, which can be used in both modern and future communication networks. Novelty: a modeling and methodological apparatus has been developed that allows for increasing the efficiency of building wireless access networks using UAVs, in particular, allowing for selecting the placement positions of routers in a UAV swarm and the logical structure of the network. The developed modeling and methodological apparatus solves the problem of traffic routing taking into account the quality of its service. Practical significance: the proposed model and methods can be used to organize service in 5G networks and subsequent generations. In particular, they allow for ensuring the availability of communication and the efficiency of network organization in cases of insufficient coverage, as well as in cases of failure of individual network elements. The ability to unload traffic to a local network allows for improving the quality of traffic service in the operator's network.

Improving 5G network operations with Software-Defined Network Control

The fifth generation (5G) mobile network is being created to keep up with the Internet of Things (IoT) and the rapid expansion of communication technologies, which generate large amounts of data. This mobile network provides expanded communication features for cellular phones. However, there are limitations to this strategy. Programming Characterized Organization (SDN) technology is being developed as a basic solution to the problem of dealing with many devices running multiple administrations. In 5G portable organizations, SDN architecture improves adaptation, versatility, cost-effectiveness, and energy efficiency. Many alternative designs are often utilized to build the SDN control plane. For 5G networks, we analyze several setups and recommend using the. Controller management architecture for Logically Centralized-Physically Distributed (LC-PD) systems. This solution outperforms traditional control plane systems, delivering superior throughput and reduced latency. This study demonstrates how the LC-PD control plane architecture improves internet services' QoS and communication efficiency in 5G mobile networks. We conduct simulation experiments utilizing the Mini net-WIFI emulator. Our simulations show that the LC-PD control plane architecture increases the quality of service (QoS) compared to other SDN implementations of Internet services in 5G networks.

POSSIBILITIES OF IMPROVING THE VOICE SERVICES QUALITY IN 5G NETWORKS

Background. The introduction of fifth-generation (5G) networks creates new opportunities for fast and continuous data exchange, but there are still some problems with the quality of voice services in such networks. With the rapid development of technology and the further spread of 5G, there is a need to understand the impact of key aspects of 5G on voice quality. This requires research that can systematically analyse the features of 5G networks that affect the quality of voice services.
 Objective. Identification of ways to improve the quality of voice services in 5G networks. Assessment of key indicators of voice service quality in 5G networks. Determination of the best option for the gradual transition to the Standalone mode and the use of VoNR technology in the fifth generation networks.
 Methods. Analysis of factors affecting the quality of voice services in fifth-generation networks. Analysis of well-known publications on the implementation of 5G networks. Comparison of the implementation of Non-Standalone and Standalone modes in the 5G network. Testing of the modern EVS codec, which provides an opportunity to improve the customer experience.
 Results. Confirmation that 5G networks can significantly improve the quality of voice services compared to previous mobile communication technologies such as 4G and 3G. Certain factors that may affect the quality of voice services and require additional attention when planning and deploying 5G networks are identified. The optimal steps for the transition to the Standalone mode and the use of VoNR technology in fifth-generation networks are determined. The main differences in the QoS architecture between LTE and 5G are identified, and the purpose of DRB flows for separating traffic types and services is established.
 Conclusions. It has been confirmed that 5G networks can significantly improve the quality of voice services compared to previous technologies such as 4G and 3G. This is possible due to the broadband capabilities of 5G networks, improved data transmission, low latency, the use of an advanced EVS codec and reduced response time. However, certain factors, such as network coverage, optimisation level and traffic characteristics, can affect the quality of voice services and require additional attention when planning and deploying 5G networks. The QoS management architecture consists of QoS flows, which allow separating packet assignment to flows (managed by the CN) from the assignment of DRB flows (managed by the RAN). As 5G networks are being rolled out gradually, it is important to properly integrate the 5G domain into the existing telecoms provider's network. The transition from Non-Standalone to Dual connectivity is a necessary step for the implementation of VoNR technology in Standalone mode. Using the modern EVS codec allows not only improving the customer experience, but also introducing new voice services.

Deep Learning-Based Multi-Domain Framework for End-to-End Services in 5G Networks

Deep Learning-Based Multi-Domain Framework for End-to-End Services in 5G Networks

An Enhanced Deep Reinforcement Learning-based Slice Acceptance Control System (EDRL-SACS) for Cloud–Radio Access Network

In 5G networks, Network Slicing (NS) allows service providers (SPs) to create customized standalone networks on shared platforms provided by infrastructure providers (InPs). However, the challenge arises when prioritizing slice requests sent by SPs to InPs due to limited and shared resources. The Cloud Radio Access Network (C-RAN) architecture is a novel approach that supports various services in 5G networks, but efficient resource utilization and increased revenue across the three layers of C-RAN are necessary. To address this issue, we propose an Enhanced Deep Reinforcement Learning-based Slice Acceptance Control System (EDRL-SACS) technique to maximize network utility by accepting suitable requests. EDRL-SACS considers time-varying resource situations, various service requirements, and the frequency of requests from SPs to endorse model-free solutions for Slice Acceptance Control (SAC). By adding effective parameters to the deep reinforcement learning algorithm, the proposed methodology improves its efficiency and performance resulting in better resource utilization, coverage, revenue, and rejection of users’ request terms. The system’s evaluation shows that EDRL-SACS performs well and effectively maximizes the utility of the network by providing slices to SPs in response to their slice requests.

Leveraging artificial intelligence and mutual authentication to optimize content caching in edge data centers

Available online Edge data centers are designed to meet the stringent QoE requirements of delay-sensitive and computationally intensive services in Content Delivery Network (CDN) and 5G networks. The primary purpose of this paper was to formulate and solve the problem of optimizing many control variables jointly: (i) what contents to store by taking into consideration edge capacity, and (ii) what contents to recommend to each Internet of Everything (IoE) item, based on identity and access management (IAM). In reactive caching policy, we proposed a new Two-Factor Authentication (2FA) scheme founded upon the Elliptic Curve Cryptography (ECC) and one-way hash function for access control. More interestingly, we use Non-negative Matrix Factorization (NMF), Fuzzy C-Means (FCM), Random Forest (RF) and Pearson Correlation (PC) to improve the accuracy and latency of traditional data filtering models. The intelligent recommendation engine we propose is designed to be implemented by cloud for caching and prefetching contents at the edge. The experimental results validate the theoretical guarantees of the proposed solution and its ability to achieve significant performance gains compared to common baseline models.

Enhancing 5G QoS Management for XR Traffic Through XR Loopback Mechanism

5G networks are designed to support a variety of services with highly demanding Quality-of-Service (QoS) requirements. This opened the door for novel extended reality (XR) media applications to emerge with 5G. However, recent 5G field tests and system-level simulation studies show that further XR enhancements are required to support a massive adoption of XR services in 5G networks. Such enhancements are expected to come into play with 5G-Advanced. In this line, we propose and study an XR loopback mechanism that adapts the XR traffic to the instantaneous 5G network conditions by exploiting an XR application feedback. We propose various XR loopback algorithms, strategies, and parameters’ configurations and study their impact on the 5G end-to-end performance. We conduct extensive simulation campaigns by building realistic end-to-end 5G network scenarios with 3GPP mixed XR traffic setups. Results show that the proposed XR loopback mechanism can boost XR performance in 5G networks by adapting to 5G network conditions, while keeping the XR QoS requirements under control. We provide various insights and practical directions on XR loopback design that allow us to take full advantage of the 5G network capabilities and progress toward 5G-Advanced network design.

Analysis of the impact of radio interference on the quality of service in 5G networks and the possibility of their minimization

This article is devoted to the analysis of the impact of radio interference on fifth-generation mobile communication networks and options for minimizing their impact. 5G networks are considered high-speed and high-load networks, which is why stability and performance are critical for effective service delivery in next-generation mobile networks, which in turn is achieved by combating or minimizing the effects of radio interference on the network. The article presents an overview of modern research and technologies aimed at detecting and eliminating radio interference in fifth-generation mobile communication networks. The article also considers the prospects and future directions of research in this field and is a valuable source of information for professionals in the field of wireless communications.

Superposition-Based URLLC Traffic Scheduling in 5G and Beyond Wireless Networks

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other services, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system in the presence of eMBB traffic. Based on the proposed superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the rate loss of the eMBB service and URLLC packet segmentation loss while satisfying the eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed-integer non-linear program (MINLP) which is generally very hard to solve in polynomial time. Hence, we reformulate the problem as a one-to-one pairing problem and we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to support the many-to-many pairing. Simulation results show that the proposed algorithm achieves higher URLLC packet admission rate and lower rate loss for eMBB. For instance, the URLLC packet admission rate, unlike baseline methods, is shown to be preserved under the proposed method even at higher URLLC load. It is shown that at least 30% more URLLC users can be served without degrading their QoS, while keeping the impact on eMBB rate minimal. Detailed numerical evaluation is presented to quantify the benefits of the proposed method.

Priority-based load-adaptive preamble separation random access for QoS-differentiated services in 5G networks

Fifth generation (5G) networks provide connectivity for several services including enhanced mobile broadband (eMBB), ultra-reliable low latency communication (URLLC) and massive Internet of things (mIoT). With the accelerated increase in the number of devices connected to the networks, and the diversity in quality-of-service (QoS) requirements, an appropriate modification in protocol design is required. Initial access in 5G networks is carried out based on the random access (RA) procedure through the random access channel (RACH). However, the limited resources, i.e. preambles, of the RACH causes intensive collisions among the massive number of contending devices, which result in low success probability and high access delay, causing failure to meet QoS requirements for different services. This paper introduces a priority-based load-adaptive preamble separation (PLPS) RA scheme in which RACH resources are separated between devices according to the service class priority and load estimation. The number of preambles assigned for each class of device is updated before each random access opportunity (RAO), based on the arrival load estimation, aimed at increasing the RACH throughput and therefore reducing the access delay for eMBB and URLLC devices. The proposed scheme is evaluated through a mathematical analysis model and extensive simulations. The results show that the proposed PLPS RA scheme succeeds in achieving the different QoS requirements of the three considered services, while increasing the RACH throughput by approximately 140%, compared to the benchmark which indicates the efficiency of the proposed scheme.

Cognitive radio based spectrum sharing models for multicasting in 5G cellular networks: A survey

The dramatic growth of smart devices catering content-centric data traffic has fueled the need for group communication services in cellular networks, among which multicasting is of particular interest. The unique potential of multicasting to fulfill the vision of a hyper-connected society in 5G networks is underlined by its ability to manage resources efficiently, while supporting concurrent transmission of common data to a large number of users. The bandwidth intensive communication requirements of multimedia multicast applications demand optimal usage of the spectral resources. Moreover, massive capacity, connectivity and ultra low latency requirements of the content centric applications have triggered the urge to get the content nearer to the users and employ short range communication, thus leading to densification of cellular networks. However, the limited availability of radio resources coupled with elevated levels of interference pose a major hindrance towards enabling spectrum reuse in dense networks. Cognitive radio (CR) is a viable technology to enhance the spectral efficiency, by allowing low priority unlicensed users to share the spectrum of high priority licensed users. The ability of CR to adapt its transmission parameters according to the application demands makes it a promising candidate for efficient spectrum management and interference mitigation in 5G cellular networks. This paper presents a survey on the state-of-the art techniques proposed for catering multicast services in CR networks. The study provides a detailed analysis on the utility of various spectrum sharing models of CR networks in fulfilling the end-to-end communication goals of multicast services. We particularly emphasize on the nature of multicast traffic, the key enabling techniques and methodologies employed for enhancing the efficacy of such services and approaches for mitigating interference experienced by the licensed users. By carefully analyzing the existing works, we provide possible design guidelines for multicast services in future 5G networks. Finally, we identify few open issues and discuss the future research directions.

Dimensioning V2N Services in 5G Networks Through Forecast-Based Scaling

With the increasing adoption of intelligent transportation systems and the upcoming era of autonomous vehicles, vehicular services (such as remote driving, cooperative awareness, and hazard warning) will have to operate in an ever-changing and dynamic environment. Anticipating the dynamics of traffic flows on the roads is critical for these services and, therefore, it is of paramount importance to forecast how they will evolve over time. By predicting future events (such as traffic jams) and demands, vehicular services can take proactive actions to minimize Service Level Agreement (SLA) violations and reduce the risk of accidents. In this paper, we compare several techniques, including both traditional time-series and recent Machine Learning (ML)-based approaches, to forecast the traffic flow at different road segments in the city of Torino (Italy). Using the most accurate forecasting technique, we propose n-max algorithm as a forecast-based scaling algorithm for vertical scaling of edge resources, comparing its benefits against state-of-the-art solutions for three distinct Vehicle-to-Network (V2N) services. Results show that the proposed scaling algorithm outperforms the state-of-the-art, reducing Service Level Objective (SLO) violations for remote driving and hazard warning services.

Token-Based Authentication Framework for 5G MEC Mobile Networks

MEC technology provides a distributed computing environment in 5G mobile networks for application and service hosting. It allows customers with different requirements and professional competencies to use the services offered by external suppliers. We consider a service access control framework on 5G MEC networks that is efficient, flexible, and user-friendly. Its central element is the MEC Enabler, which handles AAA requests for stakeholders accessing services hosted on the edge servers. The JSON Web Token (JWT) open standard is a suitable tool for the MEC Enabler to manage access control credentials and transfer them securely between parties. In this paper, in the context of access control, we propose the token reference pattern called JSON MEC Access Token (JMAT) and analyze the effectiveness of its available protection methods in compliance with the standard requirements of MEC-hosted services in 5G networks.

A Graph Neural Network-Based Digital Twin for Network Slicing Management

Network slicing has emerged as a promising networking paradigm to provide resources tailored for Industry 4.0 and diverse services in 5G networks. However, the increased network complexity poses a huge challenge in network management due to virtualized infrastructure and stringent quality-of-service requirements. Digital twin (DT) technology paves a way for achieving cost-efficient and performance-optimal management, through creating a virtual representation of slicing-enabled networks digitally to simulate its behaviors and predict the time-varying performance. In this article, a scalable DT of network slicing is developed, aiming to capture the intertwined relationships among slices and monitor the end-to-end (E2E) metrics of slices under diverse network environments. The proposed DT exploits the novel graph neural network model that can learn insights directly from slicing-enabled networks represented by non-Euclidean graph structures. Experimental results show that the DT can accurately mirror the network behaviour and predict E2E latency under various topologies and unseen environments.

Supporting Unmanned Aerial Vehicle Services in 5G Networks: New High-Level Architecture Integrating 5G With U-Space

To provide efficient, safe, and secure access to the airspace, the European Union has launched a set of new services called U-space that allow for the support of unmanned aerial vehicle (UAV) management and conflict prevention for flights in the airspace. These services are based on communication technology, which is expected to be the key enabler to unlock the underlying potential of UAV operations. In this regard, the 5G mobile network is envisioned to be the communication standard to support diverse UAV operations and applications. In this article, we propose a novel architecture that integrates 5G systems (5GSs) with U-space. The main aim consists of providing a reference design that demonstrates how 5G can support U-space services and shows the interactions among different stakeholders. Further, we introduce the 5G!Drones project, which relies on the proposed architecture to test UAV use-case scenarios on top of the 5G infrastructure.

A Software-Defined Networking Solution for Transparent Session and Service Continuity in Dynamic Multi-Access Edge Computing

Multi-Access Edge Computing (MEC) will allow implementing low-latency services that have been unfeasible so far. The European Telecommunications Standards Institute (ETSI) and the 3rd Generation Partnership Project (3GPP) are working towards the standardization of MEC in 5G networks and the corresponding solutions for routing user traffic to applications in local area networks. Nevertheless, there are neither practical implementations for dynamically relocating applications from the core to a MEC host nor from one MEC host to another ensuring service continuity. In this paper we propose a solution based on Software-Defined Networking (SDN) to create a new instance of the IP anchor point to dynamically redirect User Equipment (UE) traffic to a new physical location (e.g. an edge infrastructure). We also present a novel approach that leverages SDN to replicate the previous context of the connection in the new instance of the IP anchor point, thus guaranteeing Session and Service Continuity (SSC), and compare it with alternative state replication strategies. This approach can be used to implement edge services in 4G or 5G networks.

Next generation of microservices for the 5G Service‐Based Architecture

SummaryThe architecture for 5G core includes a Service‐Based Architecture for the diverse network functions (NFs), which relies on HTTP/2 for the SBI and TCP as the underlying transport protocol. The specifications of the HTTP family is moving towards more efficient and secure protocols, which are based on UDP to assure enhanced transport but using TLS to secure the communication channel. The next generation of microservices needs to be more secure, performant and easily manageable, where HTTP/3 and containers orchestration platforms (like Kubernetes) can provide significative contributions towards such goals. Different deployment approaches can be followed for services implemented in compliance to the 5G SBA. This paper contributes with the assessment of deployment models for services in 5G networks, where NFs are implemented following traditional architectures (all the functions in a VM) or as serverless architectures (with functions distributed in containers). The performance of microservices in Kubernetes is also evaluated. The evaluation conducted also considers the employment of different versions of HTTPs to empower the service‐based interfaces of 5G services. Results demonstrate performance benefits of employing HTTP/3, based on QUIC protocol, in scenarios with networks characterised by losses or delay conditions. Despite such gain, deployment in 5G networks needs to carefully consider aspects related with connection tracking mechanisms to support high volumes of requests.

Service Development Kit for Media-Type Virtualized Network Services in 5G Networks

Network functions virtualization (NFV) attributes to the substitute of network functions on dedicated appliances such as load balancers and routers with the use of virtualized instances running as software. Any enterprise can easily implement a wide array of network functions using NFV while maximizing efficiencies and introducing new revenue-generating services that are significantly faster and easier than ever before. NFV is a key enabler of the coming 5G infrastructure, supporting the virtualization of various appliances in the network. The scope of this document is to provide a comprehensive overview of the development of NFV-based services, media services in particular, and their specific requirements that are addressed in existing service development kits (SDKs). By supplying an overview of available tools, the development workflow of an example use case on immersive media is demonstrated with an SDK that provides a set of open source tools facilitating the development of media applications.

Performance Model for Video Service in 5G Networks

Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.

Crowd V-IoE: Visual Internet of Everything Architecture in AI-Driven Fog Computing

Fog computing has emerged as a unifying platform to provide computing, communication, and storage for a variety of mobile applications. That helps achieve high bandwidth, high intelligence, low latency, and low energy consumption in handling massive networking devices and emerging rich multimedia services in 5G networks. Current prominence and future promises are changing from the Internet of Things (IoT) to the Internet of Everything (IoE), which is a union of people, process, data, and things. However, the development of fog radio access networks (F-RANs) is challenged by the diversity of IoE, ultra-high-definition videos on demand from users, and low-latency requirement of heterogeneous IoT devices. In this article, we present an architecture of visual IoE (V-IoE) in F-RANs. We systemically analyze the key challenges of V-IoE from the perspective of F-RANs, and propose a crowd V-IoE architecture. Through experimental results, we demonstrate that our proposed architecture exhibits better performance with lower bandwidth requirement, lower energy consumption, and lower latency in F-RANs. Finally, we conclude with a discussion of potential directions.