Core Network Architecture Research Articles

6G Self‐Evolution Network for IoT Using Rainbow Deep Q‐Network Based on Decision‐Making

ABSTRACTIn recent trends, the development of 6G technology has provided a critical focus on its core network architecture which facilitates the robust foundation for entire network. A solution of self‐evolving networks for 6G is under consideration, in which networks develop their autonomy as a key perspective in the decision‐making process. Till now, various decision‐making mechanisms are identified, such as their key features, individual theoretical analyzes, and customized for 6G networks. To reduce the network load and improve system utilization, a decision‐making based on Rainbow Deep Q‐Network (RDQN) is proposed in this research. This methodology considers distributed decision‐making scenarios in which IoT devices enhance the quality of experience (QoE) with low training. Furthermore, it aims at a community of self‐evolving networks to show the improvements in 6G technologies and accelerates the learning rate. From the result analysis, it evidently displays that proposed RDQN has better training rate than other algorithms and requires fewer rounds of approximately 20 for a high number of episodes (500). Similarly, while evaluating QoE utility value, the proposed RDQN demonstrates a higher value of 100 when the number of target customers is large which outperforms the other existing models. The above results prove that the proposed RDQN is more efficient in IoT traffic monitoring and more suitable for the 6G environment.

Open set identification of malicious encrypted traffic based on multi-feature fusion

In the current network environment, an increasing amount of malicious traffic is transmitted through encrypted channels, carrying control commands and data. With the continuous development of communication protocols and applications, new types of malicious encrypted traffic are emerging, posing significant challenges for network management (e.g., traffic engineering). Therefore, accurately identifying malicious traffic in complex open network spaces has become a hot research topic in network security. In this study, we draw inspiration from channel theory in image science and innovatively convert traffic data into Red-Green-Blue (RGB) image format to achieve the fusion of multiple features. Inspired by image recognition technologies, we have designed a multi-granularity network model that integrates both global and local features, serving as our core network architecture. At the top of the model, we have equipped each known category with a unique autoencoder, using its generated manifold to replace traditional prototypes for model construction. Classification is accomplished through a scoring mechanism that evaluates category membership and by setting thresholds to achieve open set recognition of unknown categories. Relying on our self-created dataset,Malicious and Encrypted Traffic 2024 (MNET2024), we conduct a series of extensive experiments. The results demonstrate that our proposed method exhibits outstanding performance in both closed-set and open-set recognition tasks.

Toward next-generation networks: A blockchain-based approach for core network architecture and roaming identity verification

With the evolution of next-generation communication networks, ensuring robust Core Network (CN) architecture and data security has become paramount. This paper addresses critical vulnerabilities in the architecture of CN and data security by proposing a novel framework based on blockchain technology that is specifically designed for communication networks. Traditional centralized network architectures are vulnerable to Distributed Denial of Service (DDoS) attacks, particularly in roaming scenarios where there is also a risk of private data leakage, which imposes significant operational demands. To address these issues, we introduce the Blockchain-Enhanced Core Network Architecture (BECNA) and the Secure Decentralized Identity Authentication Scheme (SDIDAS). The BECNA utilizes blockchain technology to decentralize data storage, enhancing network security, stability, and reliability by mitigating Single Points of Failure (SPoF). The SDIDAS utilizes Decentralized Identity (DID) technology to secure user identity data and streamline authentication in roaming scenarios, significantly reducing the risk of data breaches during cross-network transmissions. Our framework employs Ethereum, free5GC, Wireshark, and UERANSIM tools to create a robust, tamper-evident system model. A comprehensive security analysis confirms substantial improvements in user privacy and network security. Simulation results indicate that our approach enhances communication CNs security and reliability, while also ensuring data security.

Interaction between the 4G - EPC core and the 5G RAN

The Radio Access Network - 5G RAN is designed to work seamlessly with the existing 4G EPC network infrastructure. This enables a smooth transition from fourth 4G to fifth generation 5G mobile systems, both for operators and end users. The radio access network - 5G RAN uses a new core network architecture - 5GC, which is designed to be more flexible and scalable than the previous EPC architecture. The purpose of writing the article is to analyze the methods of interaction between the EPC core of 4G networks and the 5G system core. To explore interfaces and interconnection protocols with an emphasis on roaming and non-roaming architectural models. To discuss the interaction between the 5GC core network and the E-UTRAN radio network associated with the EPC in terms of achieving this goal through non-3GPP access. Analysis of the methods of interaction between the EPC core of 4G networks and the 5GС, the study of interfaces and interaction protocols in different operating environments can be noted as contributions to the development of the article.

A Vulnerability Assessment of Open-Source Implementations of Fifth-Generation Core Network Functions

The paper presents an experimental security assessment within two widely used open-source 5G projects, namely Open5GS and OAI (Open-Air Interface). The examination concentrates on two network functions (NFs) that are externally exposed within the core network architecture, i.e., the Access and Mobility Management Function (AMF) and the Network Repository Function/Network Exposure Function (NRF/NEF) of the Service-Based Architecture (SBA). Focusing on the Service-Based Interface (SBI) of these exposed NFs, the analysis not only identifies potential security gaps but also underscores the crucial role of Mobile Network Operators (MNOs) in implementing robust security measures. Furthermore, given the shift towards Network Function Virtualization (NFV), this paper emphasizes the importance of secure development practices to enhance the integrity of 5G network functions. In essence, this paper underscores the significance of scrutinizing security vulnerabilities in open-source 5G projects, particularly within the core network’s SBI and externally exposed NFs. The research outcomes provide valuable insights for MNOs, enabling them to establish effective security measures and promote secure development practices to safeguard the integrity of 5G network functions. Additionally, the empirical investigation aids in identifying potential vulnerabilities in open-source 5G projects, paving the way for future enhancements and standard releases.

Exploring the core network of the structural covariance network in childhood absence epilepsy

Childhood absence epilepsy (CAE) is a generalized pediatric epilepsy, which is generally considered to be a benign condition since most children become seizure-free before reaching adulthood. However, cognitive deficits and changes of brain morphological have been previously reported in CAE. These morphological changes, even if they might be very subtle, are not independent due to the underlying network structure and can be captured by the structural covariance network (SCN).In this study, SCNs were used to quantify the structural brain network for children with CAE as well as controls. Seventeen children with CAE (6-12y) and fifteen controls (6-12y) were included. To estimate the SCN, T1-weighted images were acquired and parcellated into 68 cortical regions. Graph measures characterizing the core network architecture, i.e. the assortativity and rich-club coefficient, were calculated for all individuals. Multivariable linear regression models, including age and sex as covariates, were used to assess differences between children with CAE and controls. Additionally, potential relations between the core network and cognitive performance was investigated.A lower assortativity (i.e. less efficiently organized core network organization) was found for children with CAE compared to controls. Moreover, better cognitive performance was found to relate to stronger assortative mixing pattern (i.e. more efficient core network structure). Rich-club coefficients did not differ between groups, nor relate to cognitions.The core network organization of the SCN in children with CAE tend to be less efficient organized compared to controls, and relates to cognitive performance, and therefore this study provides novel insights into the SCN organization in relation to CAE and cognition.

Architecture for Self-Evolution of 6G Core Network Based on Intelligent Decision Making

The rapid progress of 6G mobile communication technologies has sparked a great deal research interests. The 6G core network architecture faces formidable challenges due to the escalating complexity of network service demands and diverse application scenarios. In response, our research endeavors to tackle these challenges by proposing a self-evolving architecture based on intelligent decision making. Inspired by the principles of biological morphological evolution, our architecture empowers the core network to dynamically adapt and reshape itself in order to effectively address the evolving communication environments. To facilitate this self-evolutionary process, we introduce a comprehensive framework encompassing mechanisms, architecture, agents, and algorithms that enable the network to autonomously generate and optimize its own structure, thereby ensuring adaptability to a wide range of application scenarios. By conducting concept proof simulation experiments, we have demonstrated the effectiveness of our self-evolution algorithm, which enables the 6G core network to make rational evolving decisions and exhibit remarkable adaptability to various application scenarios.

Simulation and Analysis of Civil Aircraft Cabin Core Network Architecture based on Daisy Chain

With the rapid development of electronic technology, the application of civil aircraft cabin electronics is becoming more and more advanced. The traditional cabin core network architecture can no longer meet current cabin application requirements. Meanwhile, there are multiple of devices with duplicated functions in cabin of civil aircraft, and the number of each device with duplicated functions is very large. In order to solve the problem of repetitive deployment of a large number of devices with duplicated functions in the cabin system, the civil aircraft cabin core network architecture is proposed to adopt a two-level daisy chain network topology which has many advantages such as high robustness, high scalability and less cables. In the design phase of civil aircraft cabin core network architecture, modeling and simulation of the network architecture through simulation tools is an important means to study, predict, justify and judge the network performance. In this paper, a two-level daisy chain network architecture model with about 67 nodes for civil aircraft cabin core network is developed and the network performance of the model mentioned above is simulated through OPNET simulation tool in order to perform a trade-off analysis and evaluate the cabin core network architecture.

THE IMPLEMENTATION OF CLOUD COMPUTING AS STRATEGIC TECHNOLOGY FOR SUSTAINABLE DEVELOPMENT USING REGRESSION ANALYSIS

As information technology has advanced, there has been a shift toward relying more and more on online cloud storage and computing services. There is no getting around the fact that recent times have seen a meteoric rise in interest in cloud computing. This technology is used by many different organisations as the central component of their information technology infrastructure. The use of cloud computing results in increased data processing efficiency across a variety of computer and storage systems that are available over the internet. The approaches have advanced as a direct result of the cutting-edge and forward-thinking computer procedures that are the foundation of the internet's core database and network architecture. In the 1990s, a new sort of cutting-edge computing known as grid computing came into being. 2005 saw the birth of two new computing paradigms: cloud computing and utility computing. Consolidating several virtual computing components into a single physical platform is one of the most distinguishing features of cloud computing services and infrastructure. These components include the central processing unit (CPU), the network, storage, and memory. A piece of software known as a hypervisor is responsible for isolating each virtual machine (VM) (used by Virtual box and VMware, for example). Using this strategy, one virtual disc or machine may be prevented from directly accessing the memory and programmes of another inside the same environment. This can be accomplished by using a firewall. Through the use of hardware abstraction, it is feasible to conceal the complexity of operating physical computer systems, while at the same time efficiently boosting the systems' processing capacity. Utilizing virtualization technology in the cloud comes with a number of benefits, including scalability and the capacity to support many tenants (one software programme serving many users at once). These properties are essential to cloud computing because they make sharing and pooling resources possible. Sharing and pooling resources provides a number of benefits, some of which include increased business value, more flexibility, and cost savings.

Improved flat mobile core network architecture for 5G mobile communication systems

The current mobile network core is built based on a centralized architecture, including the S-GW and P-GW entities to serve as mobility anchors. Nevertheless, this architecture causes non-optimal routing and latency for control messages. In contrast, the fifth generation (5G) network will redesign the network service architecture to improve changeover management and deliver clients a better Quality-of-Experience (QoE). To enhance the design of the existing network, a distributed 5G core architecture is introduced in this study. The control and data planes are distinct, and the core network also combines IP functionality anchored in a multi-session gateway design. We also suggest a control node that will fully implement the control plane and result in a flat network design. Its architecture, therefore, improves data delivery, mobility, and attachment speed. The performance of the proposed architecture is validated by improved NS3 simulation to run several simulations, including attachment and inter- and intra-handover. According to experimental data, the suggested network is superior in terms of initial attachment, network delay, and changeover management.

An Overview of Interslice and Intraslice Resource Allocation in B5G Telecommunication Networks

We are now in the first phase of the Fifth Generation (5G) network, and its full potential is still a long way to reach. Network operators and manufacturers are preparing to release new 5G end-users services and products. Each service must be performant to meet the need of users. Network Slicing (NS) is now one of the most popular 5G technologies in the research community. For network softwarization, Network Slicing affords flexibility and scalability with embedded Quality of Experience (QoE) and Quality of Services (QoS) features. In this paper, we explore the latest developments in related research fields. We summarize the 3GPP network slicing evolution, the Radio Access Network, and Core Network architectures. We describe background concepts correlated with inter-slice and intra-slice, as well as their management and orchestration architectures. We extend our discussion to the taxonomy of NGWN resource allocation via a deep comparison of the optimization algorithms. Finally, we enrich our study by including open issues and some recommendations for the future.

Machine Learning Assisted Security Analysis of 5G-Network-Connected Systems

The core network architecture of telecommunication systems has undergone a paradigm shift in the fifth-generation (5G) networks. 5G networks have transitioned to software-defined infrastructures, thereby reducing their dependence on hardware-based network functions. New technologies, like network function virtualization and software-defined networking, have been incorporated in the 5G core network (5GCN) architecture to enable this transition. This transition has significantly improved network efficiency, performance, and robustness. However, this has also made the core network more vulnerable, as software systems are generally easier to compromise than hardware systems. This article presents a comprehensive security analysis framework for the 5GCN. The novelty of this approach lies in the creation and analysis of attack graphs of the software-defined and virtualized 5GCN through machine learning. This analysis points to 119 novel possible exploits in the 5GCN. We demonstrate that these potential exploits of 5GCN vulnerabilities generate five novel attacks on the 5G Authentication and Key Agreement protocol. We combine the attacks at the network, protocol, and application layers to generate complex attack vectors. In a case study, we use these attack vectors to find four novel security loopholes in WhatsApp running on a 5G network.

Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources.

Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them via a late fusion based approach. To tackle this challenge, we develop and investigate the usefulness of a novel deep learning method called tower networks. This method is able to learn from multiple input data sources at once. We apply the tower network to the problem of short-term temperature forecasting. First, we compare our method to a number of meteorological baselines and simple statistical approaches. Further, we compare the tower network with two core network architectures that are often used, namely the convolutional neural network (CNN) and convolutional long short-term memory (convLSTM). The methods are compared for the task of weather forecasting performance, and the deep learning methods are also compared in terms of memory usage and training time. The tower network performs well in comparison both with the meteorological baselines, and with the other core architectures. Compared with the state-of-the-art operational Norwegian weather forecasting service, yr.no, the tower network has an overall 11% smaller root mean squared forecasting error. For the core architectures, the tower network documents competitive performance and proofs to be more robust compared to CNN and convLSTM models.

Signaling Load Reduction in 5G Network and Beyond

A huge traffic flow in the next generation network is anticipated due to the rising in number of users and the new services that need low end-to-end latency causing a large signaling load on the Core Network (CN). In order to mitigate this issue, many revolutionary architectures have been proposed to reduce this burden such as Cloud Radio Access Network (C-RAN). In this paper, a new C-RAN distributed core network architecture has been proposed by splitting some CN functions and grouping them into one location with Base Band Units (BBUs). As a measure of testing the proposed architectures and by using the MATLAB, the number of signaling messages processed by the control entities was analyzed. The evaluation results indicate a significant improvement if it have been compared to Long Term Evolution (LTE) architecture in terms of signaling load reduction, As the average signaling load was reduced by 46.04 percent in one of the proposed architectures when the number of user equipments increased.

Highly Available Service Access Through Proactive Events Execution in LTE NFV

The explosion of mobile applications and phenomenal adoption of mobile connectivity by end users make all-IP based 4G LTE as an ideal choice for providing Internet access on the go. LTE core network which handles device control-plane and data-plane traffic becomes susceptible to network resource constraints. To ease these constraints, Network Function Virtualization (NFV) provides high scalability and flexibility by enabling dynamic allocation of LTE core network resources. NFV achieves this by decomposing LTE Network Functions (NF) into multiple instances. However, LTE core network architecture which is designed considering fewer NF boxes does not fit well where decomposed NF instances add delays in network event execution. Certain control-plane events being time critical hurt data-plane traffic requirements defined by LTE standard. This paper proposes Fat-proxy which acts as a stand-alone execution engine of these critical network events. Through space uncoupling, we execute several signalling messages in parallel while skipping unnecessary messages to reduce event execution time and signalling overhead while ensuring highly available service access. We build our system prototype of open source LTE core network over virtualized platform. Our results show that we can reduce event execution time and signalling overhead upto 50% and 40%, respectively.

Security policies definition and enforcement utilizing policy control function framework in 5G

This research analyses new approaches to security enforcement in fifth generation (5G) architecture from end to end perspective. With the aim of finding a suitable and effective unified schema across the different network domains, it shows that policy control framework may become the cornerstone for the definition and enforcement of security policies in new 5G networks. The 5G core network architecture reference model is defined as a Service Based Architecture (SBA). The Policy Control Function (PCF) is a Network Function (NF) that constitutes, within the SBA architecture, a unique framework for defining any type of policies in the network and delivering those to other control plane NFs. In previous generations the policy control approach has been restricted to Quality of Service (QoS) and charging aspects. In contrast, the 5G system is now based on a unified policy control scheme that allows to build consistent policies covering the entire network. By utilizing the unified 5G policy framework we have found an effective security enforcement schema flexible to create new security policies, and agile to react to the constantly changing environment, across the end to end architecture. Within this schema we have defined mechanisms to apply the QoS principles to security use cases. We have also set up the user plane security enforcement within the session management and established security policies. Finally we have made proposals to extend the network analytics to security analytics. Our overall vision is to consider security as a quality element of the network.

Mobility Prediction for 5G Core Networks

Mobility is an essential factor in the 5G core network (CN). If the network control plane can predict mobility, the operation will be more efficient and agile with intelligent and proactive decisions. So far, mobility patterns and their prediction models have been extensively studied in the research community with cellular network datasets. Recently, 3GPP initiated the specification of a CN architecture for data analysis and machine learning. In this article, in accordance with this trend, we provide a taxonomy of mobility prediction frameworks in 5G CNs ranging from data collection to model serving, with consideration of the 3GPP architecture and interfaces; and we introduce two key use cases in 5G CNs, where the gains from mobility predictions are evaluated on datasets from live networks. In particular, one of the proposed methods, machine-learning-assisted adaptive paging, reduces signaling overhead by up to 75 percent.

Modeling and Evaluation of Software Defined Networking Based 5G Core Network Architecture

Nowadays, the increase in the number of mobile users and cellular traffic leads to new challenges in the fifth-generation (5G) of cellular networks. The increase in the demand for high data rates brings challenges like scalability and flexibility in the 5G network. Software-defined networking (SDN) is a network paradigm that separates the control plane and data plane in the network and ease the management of the network. In this work, an SDN based 5G core architecture is proposed, in order to introduce flexibility and ease of management in the network. Another benefit of using SDN is to make the network vendor-independent. Furthermore, the explanation of initial attachment and handover procedures in the proposed architecture is provided. A network simulator is built to evaluate the performance of proposed architecture, in terms of end-to-end delay, throughput and resource utilization of controller, under different network factors. A performance comparison, in terms of end-to-end delay, between proposed SDN based 5G architecture and traditional 5G architecture is provided. Results show that the proposed architecture provides 18% to 62% less end-to-end delay, under different factors for different procedures, compared to the traditional 5G architecture. A comparison with previous works is also provided, which indicates similar trends in delay between our work and previous studies.

ATSC 3.0 Broadcast 5G Unicast Heterogeneous Network Converged Services Starting Release 16

In this paper, the ATSC 3.0 broadcast Radio Access Technology (RAT) is aligned with 3GPP 5G NR RAT in the context of 5G convergence starting in Release 16. The 5G system architecture release 16 includes a new 5G physical layer known as 5G NR “New Radio” and a “Cloud Native” 5G Core (5GC) using cloud computing. The 5GC is agnostic to the type of radio access technology used and is enabler of many types of convergence. A novel shared multi-tenant broadcast core network architecture designed to interwork with 5GC is discussed. With the 3GPP 5G NR unicast and Non-3GPP ATSC 3.0 broadcast synergistically aligned using methods of Release 16. This includes using 3GPP Access Traffic Steering, Switching, Splitting (ATSSS) and a multi-radio dual simultaneous connected User Equipment (UE). This aligns ATSC 3.0, the first forward looking (non-backward-compatible) native IP OFDM broadcast standard, with 3GPP LTE/5G unicast as a converged 5G vertical. The proposed method and architecture are orthogonal to LTE broadcast Release 16 and is synergistic to 5G NR mixed-mode multicast unicast in future.

Enabling Multicast and Broadcast in the 5G Core for Converged Fixed and Mobile Networks

The fifth generation of cellular technologies, 5G, promises to deliver a wide variety of services. However, the first release of 5G (3GPP Rel-15) did not consider multicast capabilities in the 5G system including both access and core networks. This article fills the gap by describing solutions for 5G mobile core network architecture incorporating multicast and broadcast capabilities. Two innovative solutions to provision multicast and broadcast, based on 3GPP Rel-15 arechitecture, have been proposed and analyzed. One is 5G architecture friendly with low imprint over Rel-15 while the other is based on the evolution of LTE Broadcast architecture. The architectural solutions feature two new main transmission modes: transparent multicast transport and point-to-multipoint services. The former enables the 5G network as a network pipe supporting multicast traffic providing a cost-effective delivery mode for multimedia while the latter enables multicast and broadcast as a service. The paper also presents a convergent fixed wireline and mobile network architecture based on the proposed mobile network architecture altenatives. The proposed architecture alternatives are suitable for different verticals and applications such as Digital Terrestrial Television, Public Warning, Internet of Things, V2X (vehicle to everything) and mission critical communications (MCC).