5G Core Research Articles

BC-AKA: Blockchain based asymmetric authentication and key agreement protocol for distributed 5G core network

Secure authentication between user equipment and 5G core network is a critical issue for 5G system. However, the traditional authentication protocol 5G-AKA and the centralized key database are at risk of several security problems, e.g. key leakage, impersonation attack, MitM attack and single point of failure. In this paper, a blockchain based asymmetric authentication and key agreement protocol (BC-AKA) is proposed for distributed 5G core network. In particular, the key used in the authentication process is replaced from a symmetric key to an asymmetric key, and the database used to store keys in conventional 5G core network is replaced with a blockchain network. A proof of concept system for distributed 5G core network is built based on Ethereum and ECC-Secp256k1, and the efficiency and effectiveness of the proposed scheme are verified by the experiment results.

A Novel 5G-advanced Core Network Intelligent Operation and Maintenance System

The 5G core network adopts Network Functions Virtualization technology, which forms a variety of virtual resources for general computing, storage, and network hardware to achieve dynamic and flexible deployment of network functions and resources based on needs. With large-scale commercial deployment of cloud-native 5G core network with network function virtualization technology, the operation and maintenance of 5G core network is facing new challenges. This paper describes the architecture and deployment characteristics of 5G core network, and analyses the challenges brought to operation and maintenance by new network characteristics such as SBA, NFV, MEC and separated UPF, as well as the difficulties of operation and maintenance of virtual deployed 5G core network. Combined with the architecture evolution and function enhancement of 5G-Advanced, the architecture and scheme of A Novel Intelligent 5G-Advanced Core Network Operation and Maintenance System are proposed. The architecture and scheme of 5G core network operation and maintenance system construction were proposed, and suggestions on the organizational structure of 5G core network cloud-network integration operation and maintenance were put forward, so as to provide reference for 5G core network cloud-network integration operation and maintenance. The proposed scheme improves the performance of operation and maintenance of 5G core network significantly.

Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network.

The 5G networks aim to realize a massive Internet of Things (IoT) environment with low latency. IoT devices with weak security can cause Tbps-level Distributed Denial of Service (DDoS) attacks on 5G mobile networks. Therefore, interest in automatic network intrusion detection using machine learning (ML) technology in 5G networks is increasing. ML-based DDoS attack detection in a 5G environment should provide ultra-low latency. To this end, utilizing a feature-selection process that reduces computational complexity and improves performance by identifying features important for learning in large datasets is possible. Existing ML-based DDoS detection technology mostly focuses on DDoS detection learning models on the wired Internet. In addition, studies on feature engineering related to 5G traffic are relatively insufficient. Therefore, this study performed feature selection experiments to reduce the time complexity of detecting and analyzing large-capacity DDoS attacks in real time based on ML in a 5G core network environment. The results of the experiment showed that the performance was maintained and improved when the feature selection process was used. In particular, as the size of the dataset increased, the difference in time complexity increased rapidly. The experiments show that the real-time detection of large-scale DDoS attacks in 5G core networks is possible using the feature selection process. This demonstrates the importance of the feature selection process for removing noisy features before training and detection. As this study conducted a feature study to detect network traffic passing through the 5G core with low latency using ML, it is expected to contribute to improving the performance of the 5G network DDoS attack automation detection technology using AI technology.

ML-SLD: A message-level stateless design for cloud-native 5G core network

The Internet of Things (IoTs) has become an essential component of the 5th Generation (5G) network and beyond, accelerating the transition to digital society. The increasing signaling traffic generated by billions of IoT devices has placed significant strain on the 5G Core network (5GC) control plane. To address this issue, the 3rd Generation Partnership Project (3GPP) first proposed a Service-Based Architecture (SBA), intending to create a flexible, scalable, and agile cloud-native 5GC. However, considering the coupling of protocol states and functions, there are still many challenges to fully utilize the benefits of the cloud computing and orchestrate the 5GC in a cloud-native manner. We propose a Message-Level StateLess Design (ML-SLD) to provide a cloud-native 5GC from an architectural standpoint in this paper. Firstly, we propose an innovative mechanism for servitization of the N2 interface to maintain the connection between Radio Access Network (RAN) and the 5GC, avoiding interruptions and dropouts of large-scale user data. Furthermore, we propose an On-demand Message Forwarding (OMF) algorithm to reduce the impact of cloud fluctuations on the performance of cloud-native 5GC. Finally, we create a prototype that is based on the OpenAirInterface (OAI) 5G core network projects, with all Network Functions (NFs) packaged in dockers and deployed in a kubernetes-based cloud environment. Several experiments have been built with UERANSIM and Chaosblade simulation tools. The findings demonstrate the viability and efficiency of our proposed methods.

Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programmability.

The academic and professional community has recently started to develop the concept of 6G networks. The scientists have defined key performance indicators and pursued large-scale automation, ambient sensing intelligence, and pervasive artificial intelligence. They put great efforts into implementing new network access and edge computing solutions. However, further progress depends on developing a more flexible core infrastructure according to more complex QoS requirements. Our research aims to provide 5G/6G core flexibility by customizing and optimizing network slices and introducing a higher level of programmability. We bind similar services in a group, manage them as a single slice, and enable a higher level of programmability as a prerequisite for dynamic QoS. The current 5G solutions primarily use predefined queues, so we have developed highly flexible, dynamic queue management software and moved it entirely to the application layer (reducing dependence on the physical network infrastructure). Further, we have emulated a testbed environment as realistically as possible to verify the proposed model capabilities. Obtained results confirm the validity of the proposed dynamic QoS management model for configuring queues’ parameters according to the service management requirements. Moreover, the proposed solution can also be applied efficiently to 5G core networks to resolve complex service requirements.

Micro-service-based radio access network

The convergence of information, communication, and data technologies (ICDT) has been identified as one of the developing trends of the sixth generation(6G) network. Service-based architecture (SBA) as one of the promising information technology, has been preliminarily introduced into the fifth generation (5G) core network (CN) and successfully commercialized, which verifies its feasibility and effectiveness. However, SBA mainly focuses on the control plane of CN at present and the SBA-CN user plane is being studied by the industry. In addition to further evolving the SBA-CN, SBA radio access network (RAN) should also be considered to enable an end-to-end SBA, so as to meet more comprehensive and extreme requirements of future applications, as well as support fast rollout requirements of RAN devices.

QoS-Aware Multilayer UAV Deployment to Provide VoWiFi Service over 5G Networks

Drones equipped with wireless network cards can provide communication services in open areas. This paper proposes a hierarchical two-layered network architecture with two types of drones according to their communication equipment: Access and Distribution. While access drones provide WiFi access to ground users, distribution drones act as WiFi-to-5G relay forwarding packets into the 5G Core Network. In this context, we formulate a novel optimization problem for the 3-D initial placement of drones to provide Voice over WiFi (VoWiFi) service to ground users. Our optimization problem finds the minimum number of drones (and their type and location) to be deployed constrained to coverage and minimum voice speech quality. We have used a well-known metaheuristic algorithm (Particle Swarm Optimization) to solve our problem, examining the results obtained for different terrain sizes (from 25 m × 25 m to 100 m × 100 m ) and ground users (from 10 to 100 ). In the most demanding case, we were able to provide VoWiFi service with four distribution drones and five access drones. Our results show that the overall number of UAVs deployed grows with the terrain size (i.e., with users’ sparsity) and the number of ground users.

Modelling and Analysis of Performance Characteristics in a 60 Ghz 802.11ad Wireless Mesh Backhaul Network for an Urban 5G Deployment

With the widespread deployment of 5G gaining pace, there is increasing interest in deploying this technology beyond traditional Mobile Network Operators (MNO) into private and community scenarios. These deployments leverage the flexibility of 5G itself to support private networks that sit alongside or even on top of existing public 5G. By utilizing a range of virtualisation and slicing techniques in the 5G Core (5GC) and heterogeneous Radio Access Networks (RAN) at the edge, a wide variety of use cases can be supported by 5G. However, these non-typical deployments may experience different performance characteristics as they adapt to their specific scenario. In this paper we present the results of our work to model and predict the performance of millimeter wave (mmWave) backhaul links that were deployed as part of the Liverpool 5G network. Based on the properties of the 802.11ad protocol and the physical characteristics of the environment, we simulate how each link will perform with different signal-to-noise ratio (SNR) and Packet Error Rate (PER) values and verify them against real-world deployed links. Our results show good convergence between simulated and real results and provide a solid foundation for further network planning and optimization.

A Tutorial on Trusted and Untrusted Non-3GPP Accesses in 5G Systems—First Steps Toward a Unified Communications Infrastructure

Fifth-generation (5G) systems are designed to enable convergent access-agnostic service availability. This means that 5G services will be available over 5G New Radio air interface and also through other non-Third Generation Partnership Project (3GPP) access networks, e.g., IEEE 802.11 (Wi-Fi). 3GPP has recently published the Release 16 that includes trusted non-3GPP access network concept and wireless wireline convergence. The main goal of this tutorial is to present an overview of access to 5G core via non-3GPP access networks specified by 3GPP until Release 16 (i.e., untrusted, trusted, and wireline access). The tutorial describes aspects of the convergence of a 5G system and these non-3GPP access networks, such as the authentication and authorization procedures and the data session establishment from the point of view of protocol stack and exchanged messages between the network functions. In order to illustrate several concepts and part of 3GPP specification, we present a basic but fully operational implementation of untrusted non-3GPP access using WLAN. We perform experiments that demonstrate how a Wi-Fi user is authorized in a 5G core and establishes user plane connectivity to a data network. Moreover, we evaluate the performance of this access in terms of time consumed, number of messages, and protocol overhead to established data sessions.

A Proxy Signature-Based Swarm Drone Authentication With Leader Selection in 5G Networks

Drones are imperative for the 5G architecture as a mobile source to expand network coverage and support seamless services, particularly through enabling device-to-device (D2D) communication. Such deployment of drones in D2D settings raises various security threats in drone communication. While the existing D2D communication security standard within the 4G cellular architecture may address some of these issues, the standard includes heavy traffic toward the network core servers. If this security standard is to be adopted in the 5G D2D security services with the same traffic load, it may negatively impact the 5G network performance. Therefore, this paper proposes a lightweight proxy signature-based authentication mechanism for a swarm of drones compatible with the 5G D2D standard mechanisms. This paper proposes a distributed delegation-based authentication mechanism to reduce the traffic overhead toward the 5G core network. In this scheme, the legitimate drones are authorized as proxy delegated signers to perform authentication on behalf of the core network. Furthermore, we propose a mechanism to elect and relocate a new leader relay drone from the existing drone swarm. We implemented the proposed authentication algorithm in the 5G D2D-based communication package over NS-3 while performing the computational calculations on a RaspberryPi3 device to mimic the drone calculation process and delays. The performance of the proposed authentication shows a promising reduction in the authentication time and shows lightweight and reliable compatibility.

FlexiCell: 5G location-based context-aware agile manufacturing

Manufacturing machines need to be re-tooled approximately 15 times per week and in the future even more often because of decreasing batch sizes and increasing short-cyclic demands. Collaborative robots promise to offer a versatile automation approach for priorly manual tasks in small and medium-sized enterprises. However, their configuration needs to change at least as often as the re-tooling rate because different parts are produced by the machines or might require different handling in general. Therefore, it would be great if robots and autonomous factory systems, in general, would automatically adjust to these changes in an intelligent way. In our approach, we propose a context-aware and location-based approach for agile manufacturing, in which the manufacturing plant parts, especially the collaborative robots, store i) their constellation, ii) their configuration, and iii) their adaptation strategy, and can react to re-tooling changes and even re-location changes adaptively. For example, moving one collaborative robot to a different location next to the production machine will automatically load its new configuration and consult with the operator on the adaptation strategy (i.e., the safety requirements). Such an approach requires precise location information. To realize the localization and the network capabilities, we propose to use localization based on heterogeniclocalization technologies like ultrasound and wireless communications. We suggest a wireless small-cell-based approach around a nomadic 5G core network, which integrates multiple wireless and wired communication technologies as well as localization support combined with an intelligent asset management strategy. Such nomadic cells can operate on an island without a heavy operator backend and optimize end-to-end communication. Furthermore, these small cells can federate with each other and thus extend their coverage when getting into each other's range.

Session Management for Security Systems in 5G Standalone Network

As 5G telecom services evolve rapidly across a broad technological environment, network security in 5G landscape emerges as a critically challenging issue. One of typical network security tools is an intrusion prevention system (IPS) that monitors a network for malicious activity across the cyber-attack chain and takes action to prevent it. Vulnerabilities in 5G core networks become more varied and protocols become increasingly complex, whereby conventional Next Generation Firewall (NGFW) is not enough anymore to respond to cyber attacks. As a typical 5G vulnerability attack, PFCP-in-GTP and IPSec disable attack are highly complex to detect and cannot identify attackers without integrated session management. However, the 5G core network uses various protocols such as Non-Access Stratum (NAS), Hyper Text Transfer Protocol (HTTP), Packet Forwarding Control Protocol (PFCP), and GPRS Tunnelling Protocol (GTP), and packets of the interface used by each protocol are managed as identities that are difficult to identify. Analyzing the relationship of these interfaces in real time is an important key to integrated session management. In addition, unlike existing 4G, as 3rd Generation Partnership Project (3GPP) specs mandate encrypting 5G Standalone (SA) user IDs, it is much more difficult to identify from which user traffic has occurred in IPSs exclusive for cellular network. With regard to the above subject, this paper introduces an efficient session management scheme for users not affordable in conventional NFGW but necessarily useful for security systems in 5G SA. Furthermore, this study compared performances between conventional NGFWs and a 5G IPS system with the scheme employed, to ascertain that the scheme is feasibly implementable in 5G SA network. The actual test results show a detection rate of 99.7% and reasonable resource overhead (Memory usage 37.8%, CPU usage 42–44%).

Authentication and Handover Challenges and Methods for Drone Swarms

Drones are begin used for various purposes such as border security, surveillance, cargo delivery, visual shows and it is not possible to overcome such intensive tasks with a single drone. In order to expedite performing such tasks, drone swarms are employed. The number of drones in a swarm can be high depending on the assigned duty. The current solution to authenticate a single drone using a 5G new radio (NR) network requires the execution of two steps. The first step covers the authentication between a drone and the 5G core network, and the second step is the authentication between the drone and the drone control station. It is not feasible to authenticate each drone in a swarm with the current solution without causing a significant latency. Authentication keys between a base station (BS) and a user equipment (UE) must be shared with the new BS while performing handover. The drone swarms are heavily mobile and require several handovers from BS to a new BS. Sharing authentication keys for each drone as explained in 5G NR is not scalable for the drone swarms. Also, the drones can be used as a UE or a radio access node on board unmanned aerial vehicle (UxNB). A UxNB may provide service to a drone swarm in a rural area or emergency. The number of handovers may increase and the process of sharing authentication keys between UxNB to new UxNB may be vulnerable to eavesdropping due to the wireless connectivity. In this work, we present a method where the time and the number of the communication for the authentication of a new drone joining the swarm are less than 5G NR. In addition, group-based handover solutions for the scenarios in which the base stations are terrestrial or mobile are proposed to overcome the scalability and latency issues in the 5G NR.

Dynamic Network Slice Scaling Assisted by Attention-Based Prediction in 5G Core Network

Network slicing is a key technology in fifth-generation (5G) networks that allows network operators to create multiple logical networks over a shared physical infrastructure to meet the requirements of diverse use cases. Among core functions to implement network slicing, resource management and scaling are difficult challenges. Network operators must ensure the Service Level Agreement (SLA) requirements for latency, bandwidth, resources, etc for each network slice while utilizing the limited resources efficiently, i.e., optimal resource assignment and dynamic resource scaling for each network slice. Existing resource scaling approaches can be classified into reactive and proactive types. The former makes a resource scaling decision when the resource usage of virtual network functions (VNFs) exceeds a predefined threshold, and the latter forecasts the future resource usage of VNFs in network slices by utilizing classical statistical models or deep learning models. However, both have a trade-off between assurance and efficiency. For instance, the lower threshold in the reactive approach or more marginal prediction in the proactive approach can meet the requirements more certainly, but it may cause unnecessary resource wastage. To overcome the trade-off, we first propose a novel and efficient proactive resource forecasting algorithm. The proposed algorithm introduces an attention-based encoder-decoder model for multivariate time series forecasting to achieve high short-term and long-term prediction accuracies. It helps network slices be scaled up and down effectively and reduces the costs of SLA violations and resource overprovisioning. Using the attention mechanism, the model attends to every hidden state of the sequential input at every time step to select the most important time steps affecting the prediction results. We also designed an automated resource configuration mechanism responsible for monitoring resources and automatically adding or removing VNF instances of network slices, which helps network operators satisfy service requirements even when the traffic of end-user requests changes dynamically. Comprehensive experiments demonstrate that our proposed solution outperforms other solutions in terms of short-term and long-term predictions while reducing the cost of SLA violations and resource overprovisioning and enhancing the delay quality of network slices.

Detecting IoT Botnet in 5G Core Network Using Machine Learning

As Internet of Things (IoT) devices with security issues are connected to 5G mobile networks, the importance of IoT Botnet detection research in mobile network environments is increasing. However, the existing research focused on AI-based IoT Botnet detection research in wired network environments. In addition, the existing research related to IoT Botnet detection in ML-based mobile network environments have been conducted up to 4G. Therefore, this paper conducts a study on ML-based IoT Botnet traffic detection in the 5G core network. The binary and multiclass classification was performed to compare simple normal/malicious detection and normal/three-type IoT Botnet malware detection. In both classification methods, the IoT Botnet detection performance using only 5GC’s GTP-U packets decreased by at least 22.99% of accuracy compared to detection in wired network environment. In addition, by conducting a feature importance experiment, the importance of feature study for IoT Botnet detection considering 5GC network characteristics was confirmed. Since this paper analyzed IoT botnet traffic passing through the 5GC network using ML and presented detection results, think it will be meaningful as a reference for research to link AI-based security to the 5GC network.

Network Slicing in Virtualized 5g Core with Vnf Sharing

Network Slicing in Virtualized 5g Core with Vnf Sharing

An Optimization-Based Orchestrator for Resource Access and Operation Management in Sliced 5G Core Networks.

Network slicing is a promising technology that network operators can deploy the services by slices with heterogeneous quality of service (QoS) requirements. However, an orchestrator for network operation with efficient slice resource provisioning algorithms is essential. This work stands on Internet service provider (ISP) to design an orchestrator analyzing the critical influencing factors, namely access control, scheduling, and resource migration, to systematically evolve a sustainable network. The scalability and flexibility of resources are jointly considered. The resource management problem is formulated as a mixed-integer programming (MIP) problem. A solution approach based on Lagrangian relaxation (LR) is proposed for the orchestrator to make decisions to satisfy the high QoS applications. It can investigate the resources required for access control within a cost-efficient resource pool and consider allocating or migrating resources efficiently in each network slice. For high system utilization, the proposed mechanisms are modeled in a pay-as-you-go manner. Furthermore, the experiment results show that the proposed strategies perform the near-optimal system revenue to meet the QoS requirement by making decisions.

Trends in Intelligent Communication Systems: Review of Standards, Major Research Projects, and Identification of Research Gaps

The increasing complexity of communication systems, following the advent of heterogeneous technologies, services and use cases with diverse technical requirements, provide a strong case for the use of artificial intelligence (AI) and data-driven machine learning (ML) techniques in studying, designing and operating emerging communication networks. At the same time, the access and ability to process large volumes of network data can unleash the full potential of a network orchestrated by AI/ML to optimise the usage of available resources while keeping both CapEx and OpEx low. Driven by these new opportunities, the ongoing standardisation activities indicate strong interest to reap the benefits of incorporating AI and ML techniques in communication networks. For instance, 3GPP has introduced the network data analytics function (NWDAF) at the 5G core network for the control and management of network slices, and for providing predictive analytics, or statistics, about past events to other network functions, leveraging AI/ML and big data analytics. Likewise, at the radio access network (RAN), the O-RAN Alliance has already defined an architecture to infuse intelligence into the RAN, where closed-loop control models are classified based on their operational timescale, i.e., real-time, near real-time, and non-real-time RAN intelligent control (RIC). Different from the existing related surveys, in this review article, we group the major research studies in the design of model-aided ML-based transceivers following the breakdown suggested by the O-RAN Alliance. At the core and the edge networks, we review the ongoing standardisation activities in intelligent networking and the existing works cognisant of the architecture recommended by 3GPP and ETSI. We also review the existing trends in ML algorithms running on low-power micro-controller units, known as TinyML. We conclude with a summary of recent and currently funded projects on intelligent communications and networking. This review reveals that the telecommunication industry and standardisation bodies have been mostly focused on non-real-time RIC, data analytics at the core and the edge, AI-based network slicing, and vendor inter-operability issues, whereas most recent academic research has focused on real-time RIC. In addition, intelligent radio resource management and aspects of intelligent control of the propagation channel using reflecting intelligent surfaces have captured the attention of ongoing research projects.

Broadband Futures Forum: The Rise of 5G and the NBN

On 25 May 2021, TelSoc hosted the seventh Broadband Futures Forum, held online, to examine the relationship between 5G and Australia’s National Broadband Network (NBN). Two speakers discussed opportunities arising from the 5G core architecture for service creation and delivery via the NBN. Several 5G-related services that could be offered by the NBN to be used by its retail service providers were outlined. Discussion following the speeches included questions about potential service characteristics, possibilities for automation, and deployment scenarios.

A Comprehensive Overview of TCP Congestion Control in 5G Networks: Research Challenges and Future Perspectives.

In today’s data networks, the main protocol used to ensure reliable communications is the transmission control protocol (TCP). The TCP performance is largely determined by the used congestion control (CC) algorithm. TCP CC algorithms have evolved over the past three decades and a large number of CC algorithm variations have been developed to accommodate various network environments. The fifth-generation (5G) mobile network presents a new challenge for the implementation of the TCP CC mechanism, since networks will operate in environments with huge user device density and vast traffic flows. In contrast to the pre-5G networks that operate in the sub-6 GHz bands, the implementation of TCP CC algorithms in 5G mmWave communications will be further compromised with high variations in channel quality and susceptibility to blockages due to high penetration losses and atmospheric absorptions. These challenges will be particularly present in environments such as sensor networks and Internet of Things (IoT) applications. To alleviate these challenges, this paper provides an overview of the most popular single-flow and multy-flow TCP CC algorithms used in pre-5G networks. The related work on the previous examinations of TCP CC algorithm performance in 5G networks is further presented. A possible implementation of TCP CC algorithms is thoroughly analysed with respect to the specificities of 5G networks, such as the usage of high frequencies in the mmWave spectrum, the frequent horizontal and vertical handovers, the implementation of the 5G core network, the usage of beamforming and data buffering, the exploitation of edge computing, and the constantly transmitted always-on signals. Moreover, the capabilities of machine learning technique implementations for the improvement of TCPs CC performance have been presented last, with a discussion on future research opportunities that can contribute to the improvement of TCP CC implementation in 5G networks. This survey paper can serve as the basis for the development of novel solutions that will ensure the reliable implementation of TCP CC in different usage scenarios of 5G networks.