Core Network Functions Research Articles

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.

Prospects on the adoption of a microservice-based architecture in 5G systems and beyond

The increasing softwarisation of mobile core network functions is fostering the evolution of the mobile network architecture itself, which in its fifth generation (5G) has moved towards a service provider/consumer framework and service-based interfaces. Moreover, the 5G architecture is suitable for the exploitation of the mobile technology for dedicated, non-public uses as an alternative to nation-wide deployments. The 5G core networks are a crucial part of this architectural paradigm shift, which aims at closing the gap between the telecommunications domain and the information technology world at large. The objective of this article is to discuss the adoption of software design concepts like microservices and cloud-nativeness in the context of mobile networks. Specifically, we will (i) advocate the need for a non-trivial adaptation of the 5G core network and a redesign of its functions into a microservice-based architecture, (ii) identify an approach to achieve this objective and put it into practice by decomposing three exemplary network functions, both theoretically and practically, in microservices in charge of distinct responsibilities, and (iii) propose ways forward towards the adoption and further extension of these concepts in beyond-5G mobile systems.

One-shot models for local core network placement and traffic optimization in self deployable networks

In recent years, we have witnessed a proliferation of core network (CN) virtualization, coupled with the emergence of self deployable architectures in cellular networks. These advances provide greater flexibility and allow CN functions to be virtualized and co-located with one or more base stations. However, they introduce new challenges, such as the optimal location of CN functions based on the physical architecture. In this paper, we tackle this problem and propose a new generic and efficient solution that locates the best base station to host the CN functions, while optimizing a given performance metric (e.g., maximum flow, max–min fairness, etc.) The core of our solution relies on jointly modeling the problem as a Mixed-Integer Nonlinear Programming (MINLP) and efficiently linearizing the nonlinear constraints. After presenting our model, we evaluate its performances, considering the global maximum flow as an optimization metric. The results showed that our joint modeling significantly reduces the run time compared to existing solutions while achieving the same optimal results. Furthermore, we highlight the genericity of our model, and we show how it can be easily adapted to handle different optimization metrics. We particularly consider some variants to address the fairness problem in conjunction with the local CN placement issue. We perform extensive simulations and discuss the obtained results. The latter allow us to derive insights regarding the tradeoff between maximum flow and fairness of proportional satisfaction, while highlighting the strengths of the proposed joint modeling.

From earth to space: A first deployment of 5G core network on satellite

Recent developments in the aerospace industry have led to a dramatic reduction in the manufacturing and launch costs of low Earth orbit satellites. The new trend enables the paradigm shift of satellite-terrestrial integrated networks with global coverage. In particular, the integration of 5G communication systems and satellites has the potential to restructure next-generation mobile networks. By leveraging the network function virtualization and network slicing, the satellite 5G core networks will facilitate the coordination and management of network functions in satellite-terrestrial integrated networks. We are the first to deploy a 5G core network on a real-world satellite to investigate its feasibility. We conducted experiments to validate the satellite 5G core network functions. The validated procedures include registration and session setup procedures. The results show that the satellite 5G core network can function normally and generate correct signaling.

Addressing ZSM Security Issues with Blockchain Technology

Undoubtedly, we are witnessing a new era of computer networks that aspire to support modern demanding applications by providing the highest Quality of Experience (QoE) to the end user. Next Generations Networks (NGNs) ensure that characteristics such as ultra-low latency, high availability and wide service coverage can be met across the network regardless of the network infrastructure ownership. To accomplish that, beyond the necessary improvements in the radio propagation field, changes have been made in the core network functions which are now characterized as programmable, and software defined. Software Defined Networks (SDNs) and Network Function Virtualization (NFV) are the keystones of the NGNs flexibility. The high expectations of NGNs’ performance and the continuous changes in the network conditions lead to the development of new network management frameworks that add elasticity and dynamicity and minimize human intervention. ETSI (the European Standards Organization) presents the Zero-touch Service Management (ZSM) framework that uses hyped technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to achieve full end-to-end automation of the network services’ management across one or many different domains. Focusing on multi-domain network service management, there are several security issues identified by the standardization team which mostly derive from the lack of trust among network providers. In the present research, we explore the suitability of blockchain technology adoption for facing these security issues. Blockchain technology inherently addresses security in trustless environments such as the infrastructures defined by the ZSM team. Our contribution is three-fold: (a) we define the architecture of a multi-domain network infrastructure that adopts the ZSM approach and integrates blockchain functionality, (b) we explore the adoption of different blockchain and distributed ledger technologies (DLT) approaches to address ZSM security needs and (c) we provide guidelines to prospective solution designers/implementers on the detailed requirements that this solution has to meet to maximize the offered value.

Design and Validation of an Open Source Cloud Native Mobile Network

Network technologies are embracing the cloud-native paradigm, following the current best practices in cloud computing. Cloud-native technologies might be applied to different types of network functions in a mobile network, but they are particularly relevant nowadays for core network functions, as the recent standard introduces the service-based architecture that matches modern cloud-native technologies such as Docker and Kubernetes. In parallel, a number of open source software initiatives already provide researchers and practitioners with usable software that implements the key functionality of a mobile network (for both LTE and 5G). These software solutions, however, are monolithic and not integrated into state-of-the-art cloud-native frameworks. In this article, we fill this gap by describing the implementation of a cloud-native mobile network, which supports channel emulation and provides an affordable and scalable way of testing orchestration algorithms with standardized VIM interfaces. Our experimental evaluation shows the applicability of our solution, which is released as open source and illustrates its flexibility.

As 5G Mobile Private Security Analysis Based on Cloud Computing

As time passes by, the increasing demands from 4G technology services to 5G technology are getting bigger. In line with the development of 5G, there are several open sources such as Free5gc, Open5gs which already provide core 5G network services so that they can help create and simulate private 5G networks. By using free5gc open source, it still needs more attention from the security side, especially from common attacks such as DDoS. Then the attack scenario is carried out from the inner network side, namely from the UE with the TCP SYN Flood and Ping of Death attack methods. The results of the attack contained comparisons for CPU and memory usage parameters. Then the TCP SYN Flood attack had an impact on the core network function components, namely Access and Mobility Management (AMF), which in 5G services experienced errors or crashes after receiving large packets. Thus, users cannot use the service because if an error occurs in the core network function (AMF), then the connection to gNB and the UE will also be lost

A network operator-biased approach for multi-service network function placement in a 5G network slicing architecture

The 5G communication standard is characterized by an increased softwarization, allowing a higher flexibility able to cope with different requirements and services. In particular, Network Function Virtualization (NFV) is a recently introduced technology that enables a software implementation of different network functions exploiting virtualization techniques, hence, enabling their flexible deployment upon system requirements. Boosted by NFV, the concept of network slicing is gaining great attention in 5G networks. The idea is that physical communication and computing resources are sliced in multiple end-to-end logical networks, each one tailored to best support a specific service. The advantages of NFV, in the network slicing context, are even more evident in distributed computing environments, such as the edge-to-cloud continuum, recently introduced for enabling a flexible deployment of multiple functions. In particular, thanks to the introduction of cloud-native technologies, based on the usage of containerization and microservice technologies, the virtual network functions (VNFs) deployment and their orchestration is an easy operation, allowing the on-the-fly network configuration. Gaining from the NFV, Network Slicing and Edge-to-Cloud continuum paradigms, we propose a new network function allocation problem for multi-service 5G networks, able to deploy network functions on a distributed computing environment depending on the service requests. The proposed approach jointly considers Radio Access Network (RAN) and Core Network (CN) functions and, differently from other approaches, introduces an option able to bias the function placement depending on the service requirements, allowing a fast-and-easy operator-side deployment of the network functions. We propose to solve the problem through a Genetic Algorithm able to approach the optimal solution but with reduced complexity and execution time. The performance is compared with two other heuristic algorithms and with an exhaustive search algorithm, introduced as benchmarks, showing the benefits of the selected solution in terms of performance, flexibility and complexity.

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.

Exploring efficacy of Chinese medicine injection for promoting blood circulation and removing blood stasis in treatment of acute cerebral infarction based on two complex network analysis methods

This study aims to explore the efficacy of Chinese medicine injections( CMIs) for promoting blood circulation and removing blood stasis for acute cerebral infarction from the perspectives of clinical medication and mechanism of action based on two complex network analysis methods. Firstly,the current 13 kinds of CMIs for acute cerebral infarction were obtained from 2019 List of medicines for national basic medical insurance,industrial injury insurance and maternity insurance with the method of network Meta-analysis. Secondly,with the use of network pharmacology,the mechanisms of top 2 CMIs with the highest therapeutic effect for acute cerebral infarction were explored from two levels including core target and network function enrichment. The result of network Meta-analysis showed Mailuoning Injection was superior to Danhong Injection in terms of total effectiveness rate for neurological deficit score and NIHSS score. The network pharmacology results showed that Mailuoning Injection had more core targets,interaction networks,enriched biological functions and more signaling pathways than Danhong Injection for cerebral infarction. Both two CMIs can play a role in treating cerebral infarction through core targets such as TP53 and NOS3,biological processes such as fibrinolysis,nitric oxide biosynthesis,nitric oxide-mediated signal transduction,negative regulation of apoptosis in endothelial cells and apoptosis process,as well as the signaling pathways such as PI3 K-Akt signaling pathway,HIF-1 signaling pathway and cell apoptosis signaling pathways. The results of pharmacological studies explained their differences in clinical efficacy to a certain extent. A research strategy based on curative effect should be advocated in efficacy evaluation of traditional Chinese medicine,where comparative research on clinical efficacy can be conducted firstly,and then mechanism research based on outstanding effective drugs to better provide references and basis for selection of similar competitive drugs for one disease in the clinical practice.

Toward an AI-Enabled O-RAN-based and SDN/NFV-driven 5Ｇ& IoT Network Era

Artificial Intelligent Technology has impacted tremendously in the areas of high performance computing, and network and communicatons industries. The advantages of a system applying AI includes performance improvent, optimization, and intelligent or smart AnFor intelligent fesure of 5G, network slicing, provided by Network equipment vendor by applying AI, softwarization and virtualization technologies to the network. For many other industries and applications such as healthcare, agriculture, finance, have benefited from AI technology in particular machine learning and deep learning within AL.With the integration of AI, 5G, and Inernet of Thngs, the industrial applications, smart farms, precision medicine.,smart city. This article focuses on the System architecture and design of open networking solution of the future of 5G, beyond-5G (B5G) or 6G. Among the challenges of an ON system solution, the propriety of radio access network (RAN) is one of essential challenges. The Open-RAN Alliance is formed through the integration of C-RAN Alliance and X-RAN Forum. The O-RAN Alliance mission’s is converting the radio access network industry to become an open networking intelligent, virtualized, and fully interoperable RAN. To realize B5G or 6G by applying O-RAN architecture and ecosystem is called O-RAN based B5G/6G The Integration of O-RAN based 5G RAN part and the SDN/NFV-based softwarization and virtualization of Core Network, Transport Network and Management functions, we can derive a stage of fully Open Networking architecture for the software (AI/M/DL) developers to work.

Bringing AI to the edge: A formal M&S specification to deploy effective IoT architectures

ABSTRACT Internet of Things applications are based on ubiquitous networks of multiple distributed devices, with limited computing resources and power, capable of collecting and storing data from heterogeneous sources in real-time. To avoid network saturation and delays, new architectures are needed to provide real-time Big Data and data analytics capabilities at the edge of the network, where energy efficiency needs to be considered to ensure a sustainable and effective deployment in areas of human activity. In this research, we present an IoT model based on the principles of Model-Based Systems Engineering. It covers the description of the entire architecture, from IoT devices to the processing units in edge data centres, and includes the location-awareness of user equipment, network, and computing infrastructures to optimise federated resource management in terms of delay and power consumption. We present a framework to assist the dimensioning and the dynamic operation of IoT data stream analytics applications. Abbreviations : ADAS Advanced Driver Assistance System. 20, 21, 24AI Artificial Intelligence. 2, 24ANN Artificial Neural Network. 2, 21AP Access Point. 5, 10, 11, 12, 13, 17, 18, 19, 20CNF Core Network Function. 5DEVS Discrete Event System Specification. 3, 5, 6, 7, 9, 10, 11, 12, 15, 16, 17, 18, 20, 21, 24, 27, 28EDC Edge Data Centre. 2, 3, 5, 6, 9, 10, 12, 13, 20, 21, 22, 23, 25FaaS Function-as-a-Service. 3, 4, 5, 6FDD Frequency Division Duplexing. 11, 18, 19FSPL Free-Space Path Loss. 20GPU Graphics Processing Unit. 2, 21IoT Internet of Things. 2, 3, 4, 5, 6, 14, 19, 23, 24ISP Internet Service Provider. 5, 10M&S Modelling and Simulation. 3, 24M&S&O Modelling, Simulation, and Optimisation. 21MBSE Model-Based Systems Engineering. 3, 4, 5, 24MCS Modulation and Codification Scheme. 12MDC Micro Data Centre. 2ML Machine Learning. 2, 20, 24, 25NR 5G New Radio. 12P2P Point-to-Point. 5, 10PBCH Physical Broadcast Channel. 19PDCCH Physical Downlink Control Channel. 19PDSCH Physical Downlink Shared Channel. 19PSS Primary Synchronisation Signal. 11, 14PU Processing Unit. 7, 9, 21, 22, 23, 24, 25PUCCH Physical Uplink Control Channel. 19PUSCH Physical Uplink Shared Channel. 19QoS Quality of Service. 2, 4, 5, 6, 12, 24RAN Radio Access Network. 5, 10, 13, 16, 17, 20SDN Software-Defined Network. 5, 10SNR Signal-to-Noise Ratio. 11, 12, 14, 17UE User Equipment. 4, 5, 6, 10, 11, 12, 13, 14, 16, 17, 18, 19, 21, 22, 23, 24, 25WSN Wireless Sensor Network. 4

IEEE Access Special Section Editorial: Software-Defined Networks for Energy Internet and Smart Grid Communication

A new network paradigm of software-defined networks (SDNs) is being widely adapted to efficiently monitor and manage the communication networks with a global perspective. SDN has a key networking feature that separates control and data plane. Today, due to its inherent benefits, SDN has been widely applied to various networking domains, including data centers, 5G Access and Core network functions, wide area network (WAN), enterprise, optical networks, underwater sensor networks (UWSNs), energy Internet (EI), and smart grid (SG).

ONVM-5G: a framework for realization of 5G core in a box using DPDK

Software Defined Networking (SDN) and Network Functions Virtualization (NFV) are the two fundamental paradigms that introduce concealed flexibility in communication systems. 5G has adopted SDN/NFV to make Network Functions (NFs) as virtualized software modules which run on top of commodity hardware. 5G aims to expand its reach to various business verticals and serve diverse users using network slicing which creates multiple logical networks on top of a shared infrastructure. This paper proposes a novel OpenNetVM based 5G framework (ONVM-5G) which uses Data Plane Development Kit for the communication between the core NFs and thus, is a candidate solution for implementing 5G core (5GC) in a box. The proposed framework based deployment of network slices enhances the user experience by reducing the latency involved in performing various User Equipment activities and increasing data plane throughput. Moreover, ONVM-5G also helps in achieving the 5G goals by allowing the network operators to efficiently serve the users in small dense cells and make service level isolation among the diverse users through network slicing by providing a network in a box like deployment of 5GC.

5G and the Need for Platform Integrity

Current cellular architecture will not be suitable for 5G because it will not scale to the anticipated number of connected endpoints and their rich diversity. The distribution of the previously centralized Core Network (CN) functionality, e.g., Access Authentication and Authorization, has to be decentralized, leading to the demise of the most utilized tool of network security engineering, Physical Security Perimeter. The asserted and attested Platform Integrity of the network nodes that comprise the edges of the network, the network cloud, “network fog”, and the endpoints will allow mobile network operators (MNOs) to create Virtual Network Perimeters and allow highly reliable, diverse, and flexible 5G networks. This article describes the reasons for such network transformation, provides references to applicable standardization activities, and uses the examples of support for Unmanned Aerial Vehicles (UAV) and connected automobiles by 5G networks to justify the need for Platform Integrity.

A congestion control framework for delay- and disruption tolerant networks

Delay and Disruption Tolerant Networks (DTNs) are networks that experience frequent and long-lived connectivity disruptions. Unlike traditional networks, such as TCP/IP Internet, DTNs are often subject to high latency caused by very long propagation delays (e.g., interplanetary communication) and/or intermittent connectivity. In DTNs there is no guarantee of end-to-end connectivity between source and destination. Such distinct features pose a number of technical challenges in designing core network functions such as routing and congestion control mechanisms. Detecting and dealing with congestion in DTNs is an important problem since congestion can significantly deteriorate DTN performance. Most existing DTN congestion control mechanisms have been designed for a specific DTN application domain and have been shown to exhibit inadequate performance when used in different DTN scenarios and conditions.In this paper, we introduce Smart-DTN-CC, a novel DTN congestion control framework that adjusts its operation automatically based on the dynamics of the underlying network and its nodes. Smart-DTN-CC is an adaptive and distributed congestion aware framework that mitigates congestion using reinforcement learning, a machine learning technique known to be well suited to problems where: (1) the environment, in this case the network, plays a crucial role; and (2) yet, no prior knowledge about the target environment can be assumed, i.e., the only way to acquire information about the environment is to interact with it through continuous online learning.Smart-DTN-CC nodes receive input from the environment (e.g., buffer occupancy, neighborhood membership, etc), and, based on that information, choose an action to take from a set of possible actions. Depending on the selected action’s effectiveness in controlling congestion, a reward will be given. Smart-DTN-CC’s goal is to maximize the overall reward which translates to minimizing congestion. To our knowledge, Smart-DTN-CC is the first DTN congestion control framework that has the ability to automatically and continuously adapt to the dynamics of the target environment. As demonstrated by our experimental evaluation, Smart-DTN-CC is able to consistently outperform existing DTN congestion control mechanisms under a wide range of network conditions and characteristics.

Dynamic Resource Trading in Sliced Mobile Networks

Expanding the market of mobile network services and defining solutions that are cost efficient are the key challenges for next generation mobile networks. Network slicing is commonly considered to be the main instrument to exploit the flexibility of the new radio interface and core network functions. It targets splitting resources among services with different requirements and tailoring system parameters according to their needs. Regulation authorities also recognize network slicing as a way of opening the market to new players who can specialize in providing new mobile services acting as “tenants” of the slices. Resources can also be distributed between infrastructure providers and tenants so that they meet the requirements of the services offered. In this paper, we propose a model for dynamic trading of mobile network resources in a market that enables automatic optimization of technical parameters and of economic prices according to high level policies defined by the tenants. We introduce a mathematical formulation for the problems of resource allocation and price definition and show how the proposed approach can cope with quite diverse service scenarios presenting a large set of numerical results.

Bayesian Coalition Formation Game for Virtual 5G Core Network Functions

The network function virtualization (NFV) enables the virtual network functions (VNFs) to run as software components upon a virtualization system hosted in a cloud. Recently, due to its high flexibility and elasticity in terms of network services and functions deploying, NFV has been considered as one of the key enabling techniques of the fifth-generation (5G) wireless systems. Moreover, the creation of the core network functions is the key to the implementation of 5G. Currently, there are extensive studies researching into how to deploy cooperatively VNFs over a federated cloud for multiple cloud providers (CPs) to create the 5G Core. These studies have a critical assumption that each CP participating in the cooperation is fully informed of all the information about other CPs. However, the assumption may not hold in practice. Since CPs adopt diverse virtualization technologies (e.g., XEN, KVM, or containers), the cost to create an instance of one VNF for each CP may be different. Moreover, the cost information is private and will not be shared with each other. Consequently, it is difficult for each CP to determine whether to cooperate with others and the way of cooperation. Therefore, in this paper, we utilize the Bayesian coalition formation game (BCFG) to tackle this challenging situation with the unknown information about CPs and formulate the optimal coalition that can deploy cooperatively each VNFs of 5G Core. Specifically, we address the optimal number of VNF instances to instantiate, the number of virtual resources for each instance, and the placement decision in a specific CP over a federated cloud. We propose a BCFG-based non-myopic and full negotiation approach for the creation of the virtual 5G Core network functions. Simulation results demonstrate the comprehensive performance of the proposed method in terms of convergence properties and the profits that each participating CP can obtain.

Core network function placement in self-deployable mobile networks

Emerging mobile network architectures (e.g., aerial networks, disaster relief networks) are disrupting the classical careful planning and deployment of mobile networks by requiring specific self-deployment strategies. Such networks, referred to as self-deployable, are formed by interconnected rapidly deployable base stations that have no dedicated backhaul connection towards a traditional core network. Instead, an entity providing essential core network functionalities is co-located with one of the base stations. In this work, we tackle the problem of placing this core network entity within a self-deployable mobile network, i.e., we determine with which of the base stations it must be co-located. We propose a novel centrality metric, the flow centrality, which measures a node capacity of receiving the total amount of flows in the network. We show that in order to maximize the amount of exchanged traffic between the base stations and the core network entity, under certain capacity and load distribution constraints, the latter should be co-located with the base station having the maximum flow centrality. We first compare our proposed metric to other state of the art centralities. Then, we highlight the significant traffic loss occurring when the core network entity is not placed on the node with the maximum flow centrality, which could reach 55% in some cases.

Improving Traffic Forecasting for 5G Core Network Scalability: A Machine Learning Approach

5G is expected to provide network connectivity to not only classical devices (i.e., tablets, smartphones, etc.) but also to the IoT, which will drastically increase the traffic load carried over the network. 5G will mainly rely on NFV and SDN to build flexible and on-demand instances of functional networking entities via VNFs. Indeed, 3GPP is devising a new architecture for the core network, which replaces point-to-point interfaces used in 3G and 4G by producer/consumer-based communication among 5G core network functions, facilitating deployment over a virtual infrastructure. One big advantage of using VNFs is the possibility of dynamic scaling, depending on traffic load (i.e., instantiate new resources to VNFs when the traffic load increases and reduce the number of resources when the traffic load decreases). In this article, we propose a novel mechanism to scale 5G core network resources by anticipating traffic load changes through forecasting via ML techniques. The traffic load forecast is achieved by using and training a neural network on a real dataset of traffic arrival in a mobile network. Two techniques were used and compared: RNN, more specifically LSTM; and DNN. Simulation results show that the forecast-based scalability mechanism outperforms the threshold-based solutions, in terms of latency to react to traffic change, and delay to have new resources ready to be used by the VNF to react to traffic increase.