Network Slicing Research Articles

Physical layer encryption-based secure slicing in 5G RAN with hybrid-trusted links

In a 5G radio access network (RAN), network slicing enables dividing a single RAN infrastructure into multiple logical networks, efficiently accommodating services with diverse requirements. Although RAN slicing can help improve resource efficiency and reduce network costs, it is accompanied by various security risks. One of the security threats in RAN slicing is potential eavesdropping, resulting in the leakage of sensitive data within slices. Encryption technologies have been developed to address the eavesdropping problem at different layers in optical networks. We focus on physical layer encryption since it has been demonstrated beneficial in line-speed processing, low latency, and small encryption overhead. The problem of utilizing physical layer encryption technologies to achieve secure RAN slices remains unexplored since physical layer encryption introduces additional hardware costs. In this paper, we study how to realize secure RAN slicing based on physical layer encryption in a metro aggregation network that consists of hybrid-trusted links (i.e., links with different risks for eavesdropping). We propose an integer linear programming (ILP) model and an auxiliary graph-based heuristic for small-scale and large-scale networks, respectively. The objective is to maximize the number of deployed slices and minimize the total cost of secure slice deployment, which includes the costs of servers, line cards (LCs), encryption cards (ECs), and bandwidth resources. To evaluate the benefit of encryption, we compare it with a detour solution, which protects slices by routing through trusted links (i.e., where no additional hardware for encryption is deployed). Simulation results show that the encryption-based solution exhibits a lower cost than the benchmark when the same number of slices are deployed, and it can reduce the blocking ratio by up to 8.5% as slice requests increase. In addition, the average latency of slices is also reduced by up to 14.6%.

Transport SDN architecture for multi-layer transport slicing

The proposed architecture advances the concept of network slicing, crucial for beyond 5G services, by enabling dynamic resource allocation and customized partitioning in managed network infrastructures. This architecture addresses the challenges of provisioning end-to-end (E2E) slices across diverse network domains, which is complicated by technological heterogeneity and the variety of vendor solutions. By introducing a standardized transport network solution, we ensure seamless integration, equitable treatment of service requests, and the ability to meet diverse demands. The architecture is centered around a multi-layer transport network slicing architecture, which allows for the division of transport networks into virtual autonomous segments, each tailored for specific services or applications. This segmentation is essential for providing differentiated and personalized 5G services, optimizing network performance, and maximizing resource application. A key component of this architecture is the transport slice controller (TSC), which controls the provision and life-cycle management of transport slices, ensuring a standardized approach in the industry for the definition and realization of slices.

Research on reliability mapping of 5G low orbit constellation network slice based on deep reinforcement learning

Reliability mapping of 5G low orbit constellation network slice is an important means to ensure link network communication. The problem of state space explosion is a typical problem. The deep reinforcement learning method is introduced. Under the 5G low orbit constellation integrated network architecture based on software definition network (SDN) and network function virtualization (NFV), the resource requirements and resource constraints of the virtual network function (VNF) are comprehensively considered to build the 5G low orbit constellation network slice reliability mapping model, and the reliability mapping model parameters are trained and learned by using deep reinforcement learning, solve the problem of state space explosion in the reliability mapping process of 5G low orbit constellation network slices. In addition, node backup and link backup strategies based on importance are adopted to solve the problem that VNF/link reliability is difficult to meet in the reliability mapping process of 5G low orbit constellation network slice. The experimental results show that this method improves the network throughput, packet loss rate and intra slice traffic of 5G low orbit constellation, and can completely repair network faults within 0.3 s; For different number of 5G low orbit constellation network slicing requests, the reliability of this method remains above 98%; For SFC with different lengths, the average network delay of this method is less than 0.15 s.

Online Multi-Resource Allocation for Network Slicing in 5G with Distributed Algorithms

With the increasing scale of mobile cellular network applications, 5G mobile network infrastructure provides customizable services to users in the form of network slices. How to effectively allocate existing resources in real dynamic networks with time-varying network utility is a key issue that previous work did not consider. This paper first initializes the multi-resource allocation problem of network slicing in an online manner, where the utility function is set to change over time. Therefore, we propose Metis, an online network slicing resource allocation framework that combines the time-varying nature of the network utility function given bandwidth and processing power constraints with the requirement of virtual network function isolation. The goal is to maximize the cumulative network utility in the long term and specify multiple resource allocation problems by utilizing concave optimization methods. In addition, a distributed algorithm based on the online alternating direction method of multipliers with regret optimization has been developed to achieve optimal resource allocation. Our mathematical analysis proves that Metis can provably converge to the optimal solution and the result of experiments demonstrates a steady state behavior of Metis, which converges in dynamic network settings.

End-to-End Delay Performance Analysis for Delay-Sensitive Services in Polymorphic Networks

Abstract In the polymorphic network for smart grid, the end-to-end latency performance analysis of delay-sensitive power control services such as differential protection and distributed power regulation is a crucial component in the intelligent control and management of power network slicing. In this paper, based on a detailed analysis of end-to-end delay factors of power control service, the distributed power regulation pilot project is taken as a typical application scenario of power control service, and the end-to-end delay of such service is theoretically derived and analyzed in detail by using network calculus theory. Numerical verification shows that the end-to-end delay performance of the power control service is positively correlated with the number of concurrent data streams and the number of subsystems and negatively correlated with the system service rate. Compared with the burst degree of the current data streams, the burst degree of other data streams significantly impacts the end-to-end delay calculation results of the current data flow.

Cloud-Native Orchestration Framework for Network Slice Federation Across Administrative Domains in 5G/6G Mobile Networks

Cloud-Native Orchestration Framework for Network Slice Federation Across Administrative Domains in 5G/6G Mobile Networks

Network Slicing in 6G: A Strategic Framework for IoT in Smart Cities.

The emergence of 6G communication technologies brings both opportunities and challenges for the Internet of Things (IoT) in smart cities. In this paper, we introduce an advanced network slicing framework designed to meet the complex demands of 6G smart cities' IoT deployments. The framework development follows a detailed methodology that encompasses requirement analysis, metric formulation, constraint specification, objective setting, mathematical modeling, configuration optimization, performance evaluation, parameter tuning, and validation of the final design. Our evaluations demonstrate the framework's high efficiency, evidenced by low round-trip time (RTT), minimal packet loss, increased availability, and enhanced throughput. Notably, the framework scales effectively, managing multiple connections simultaneously without compromising resource efficiency. Enhanced security is achieved through robust features such as 256-bit encryption and a high rate of authentication success. The discussion elaborates on these findings, underscoring the framework's impressive performance, scalability, and security capabilities.

DRL‐based customised resource allocation for sub‐slices in 6G network slicing

DRL‐based customised resource allocation for sub‐slices in 6G network slicing

A trust distributed learning (D‐NWDAF) against poisoning and byzantine attacks in B5G networks

AbstractBeyond 5G networks, (B5G) introduce the emergence of a smart service and an automate management archetype of running network slices, such as the network data analytics function (NWDAF). These new architectures provide a large usage of advanced machine learning algorithms, in order to dynamically build efficient decisions. In this perspective, the Distributed Learning (D‐), known as A Distributed NWDAF Architecture (D‐NWDAF) has proved its efficiency in not only building collaborative deep learning models, among several network slices, but also ensuring the privacy and isolation of such network slices. Notwithstanding, FL is vulnerable to different attacks, where FL nodes (Leaf NWDAF: AI‐VNFs*) may upload malicious updates to the FL central (root NWDAF) entity so that it can cause a construction failure of the FL global model and affect the global performance. Moreover, attacks detection of FL‐based solutions give “black‐box” decisions about the performance of running network slices and the attacks detected. In other words, detection solutions do not provide any details about why and how such ML attacks decisions were made. Thus, such decisions cannot be properly trusted and comprehended by B5G slice managers. To resolve this issue, we leverage Dimensional Reduction (DR) and Explainable Artificial Intelligence (XAI) algorithms which aim to detect attacks and improve the transparency of black‐box FL attacks detection‐making process. In the present article, we design a novel DR‐XAI‐powered framework to detect attacks and explain the FL‐based attacks detection. We first build a deep learning model in a federated way, to predict key performance indicators (KPI) of network slices. Then, we try to detect the malicious FL nodes, using DR and several XAI models, such as RuleFit, in order to enhance the level of trustiness, transparency, and the explanation of the FL‐based attacks detection, while adhering the data privacy, to different B5G network stakeholders.

Enhancing honeynet-based protection with network slicing for massive Pre-6G IoT Smart Cities deployments

Internet of Things (IoT) coupled with 5G and upcoming pre-6G networks will provide the scalability and performance required to deploy a wide range of new digital services in Smart Cities. This new digital services will undoubtedly contribute to an improvement in the quality of life of citizens. However, security is a major concern in IoT where low-powered constrained devices are a target for attackers who identify them as a vulnerable entry point to exploit the network weaknesses. This concern is exacerbated in Smart Cities where it is expected to deploy millions of heterogeneous yet unattended and vulnerable IoT devices throughout vast urban areas. A security breach in a Smart City allows attackers to target critical services such as the power grid network or the road traffic control or to expose sensitive health data to intruders. Thus, the security and privacy of citizens could be seriously compromised. Honeynets are an effective security mechanism to distract attackers from legitimate targets and collect valuable information on how they operate. Meanwhile, current honeynets lack functionality to protect the real and lure networks from large-scale volumetric Distributed Denial of Service (DDoS) attacks. This paper provides a novel solution to empower honeynet security tools with Network Slicing capabilities as an innovative way to isolate and minimize the network resources available from attackers. The proposed system supports the ambitious IoT scalability requirements associated to 5G networks and the forthcoming 6G networks. The solution has been empirically evaluated in a emulated testbed where promising results have been achieved when dealing with mMTC and eMBB traffic profiles. In mMTC scenarios where scalability is a challenge, the solution is able to deal with up to 1000 slices and 1 Million IoT devices sending traffic simultaneously. In eMBB use cases, the solution is able to cope with up to 19 Gbps of combined bandwidth. The gathered results demonstrate that the proposed solution is suitable as a security tool in 5G IoT multi-tenant infrastructures as those expected in Smart Cities deployments.

Blockchain-Based Security Framework for VNF Package Protection in 5G Network Slicing Services

Abstract The demand for scalable, open and granular networks has enabled mobile networks such as 5G to adopt new concepts including NFV (Network Function Virtualization). NFV separates the dependency of network functions from the hardware component, allowing them to be deployed flexibly and dynamically across network slices. In this way, operators can deliver personalized services and optimize the use of network resources, contributing to greater operational efficiency and an enhanced end-user experience. However, there are several issues to be addressed, such as the security of VNFs and the way in which service provider customers instantiate NSSIs (Network Slice Subnet Instance). In this article, we present a new approach based on blockchain 2.0, which guarantees the immutability of VNFs and templates, as well as highly secure instantiation with access management and support for replay attacks.

Securing Breakneck Pace of 5G Networks Air Interfaces Through Proactive AI Monitoring

The promise of 5G networks enabling emerging technologies comes with formidable new security challenges. This paper proposes an AI-driven real-time security monitoring and incident response framework tailored to 5G infrastructure. A multi-layered architecture is presented using specialized deep learning models for radio access, edge, core, and network slice threat detection. Models including CNNs, RNNs, and transformers perform traffic analysis, signal classification, and log anomaly detection. A centralized controller aggregates model outputs into an integrated threat intelligence engine that deduces attack context and recommends mitigations. Further, a conversational bot interacts with security analysts in natural language to explain threats, suggest responses, and answer queries. The intelligent assistant is designed using dialog trees and transformer networks trained on security datasets. Evaluation on real-world 5G trial networks demonstrates 95% accuracy in classifying radio signal spoofing attacks and 98% precision in identifying malware infections. Analyst surveys confirm improved productivity and faster incident response with the AI assistant. As 5G matures, robust analytics and AI collaboration will grow increasingly critical for secure network operations. This research aims to provide both a conceptual framework and proven techniques as key enablers.

Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey

SummaryThe emergence of 5G networks has increased the demand for network resources, making efficient resource management crucial. Slice admission control (SAC) is a process that governs the creation and allocation of virtualized network environments, known as “network slices,” which can be tailored to meet specific user requirements. However, traditional SAC methods face dynamic and heterogeneous challenges in wireless networks, especially in cloud radio access networks (C‐RANs). To address this issue, machine learning (ML) techniques, particularly deep reinforcement learning (DRL), have been proposed as powerful tools for optimizing SAC. DRL‐based approaches enable SAC systems to learn from previous interactions with the network environment and dynamically adapt to changing network conditions. This review article comprehensively explains the current state‐of‐the‐art DRL‐based SAC, focusing on C‐RANs. The article identifies key challenges and future research directions and highlights the potential benefits of using DRL for SAC, including improved network performance and efficiency. However, deploying these systems in real‐world scenarios presents several challenges and trade‐offs that need to be carefully considered. Further research and development are required to address these challenges and ensure the successful deployment of DRL‐based SAC systems in wireless networks.

Blockchain technology integration in service migration to 6G communication networks: a comprehensive review

<p>The next generation of wireless networks, 6G is being designed with data-intensive applications. One of the key technologies that will enable 6G is blockchain technology. The emergence of blockchain technology and 6G networks has revolutionized service migration. Service migration in 6G networks is a complex process that requires the integration of new technologies, such as artificial intelligence (AI), edge computing, and network slicing. Motivated by these facts, this comprehensive review includes an overview of blockchain and service migration integration in 6G. First, state of art, development frame work and related works were introduced. Then, we used content analysis by WordStat software and bibliographic analysis by VOSviewer to analysis the current status of service migration and blockchain integration in 6G networks. Next, patterns and characteristics, benefits and challenges and potential cases were reviewed. Then, we proposed an architectural blockchain-based model including decentralized architecture, edge computing, network slicing, software-defined networking, and 5G-6G interworking in 6G. Finally, we described potential application service migration-based in 6G networks including digital twin (DT), holograms, robot avatar, high density internet of things (IoT), AR and VR in 6G and collected open research and future directions of service migration and blockchain.</p>

Deep Reinforcement Learning for Online Resource Allocation in Network Slicing

Deep Reinforcement Learning for Online Resource Allocation in Network Slicing

Community Detection-Empowered Self-Adaptive Network Slicing in Multi-Tier Edge-Cloud System

Community Detection-Empowered Self-Adaptive Network Slicing in Multi-Tier Edge-Cloud System

G-GANS for Adaptive Learning in Dynamic Network Slices

G-GANS for Adaptive Learning in Dynamic Network Slices

Resource allocation for cognitive network slicing in PD-SCMA system based on two-way deep reinforcement learning

Resource allocation for cognitive network slicing in PD-SCMA system based on two-way deep reinforcement learning

Network Slicing Resource Allocation Optimization Based on Multiactor-Attention-Critic Joint With Bidding in Heterogeneous Integrated Network

Network Slicing Resource Allocation Optimization Based on Multiactor-Attention-Critic Joint With Bidding in Heterogeneous Integrated Network

Intelligent Network Slicing for B5G and 6G: Resource Allocation, Service Provisioning, and Security

Intelligent Network Slicing for B5G and 6G: Resource Allocation, Service Provisioning, and Security