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Fifth-generation (5G) wireless networks are projected to bring a major transformation to the current fourth-generation network to support the billions of devices that will be connected to the Internet. 5G networks will enable new and powerful capabilities to support high-speed data rates, better connectivity and system capacity that are critical in designing applications in virtual reality, augmented reality and mobile online gaming. The infrastructure of a network that can support stringent application requirements needs to be highly dynamic and flexible. Network slicing can provide these dynamic and flexible characteristics to a network architecture. Implementing network slicing in 5G requires domain modification of the preexisting network architecture. A network slicing architecture is proposed for an existing 5G network with the aim of enhancing network dynamics and flexibility to support modern network applications. To enable network slicing in a 5G network, we established the virtualisation of the underlying physical 5G infrastructure by utilising technological advancements, such as software-defined networking and network function virtualisation. These virtual networks can fulfil the requirement of multiple use cases as required by creating slices of these virtual networks. Thus, abstracting from the physical resources to create virtual networks and then applying network slicing on these virtual networks enable the 5G network to address the increased demands for high-speed communication.

5G, the current generation of communication networks is based on the standards defined by 3GPP and other organizations (ETSI, ENISA, NGMN). These standards define virtual networks supported by three basic technologies, SDN, NFV, and Network Slicing. Virtual networks are primarily built using software and have clear advantages that appear to be reduced because of the corresponding loss in security due to the larger attack surface of this type of network. On the other hand, virtual networks can be made even more secure than hardware-based networks by leveraging the flexibility and adaptability of virtual functions and numerous articles have studied different aspects of their security. Current work goes from proposals for specific mechanisms to general studies of threats and defenses. Some of these are systematic literature reviews considering everything published on a specific theme. We prefer to analyze carefully selected papers considered significant and produce from them an overview of the status of the security of the network technologies used by 5G. After this analysis, we have found that although there are many studies of threats, they are not systematic and have confusions about concepts that may mislead implementers; we also found that the large variety of defenses can be confusing to designers. We have therefore conducted a critical analysis of threats and defenses to provide a clear perspective of how to secure these networks. Based on this perspective, we propose directions for research to improve or extend current defenses. We note that although virtual networks have special characteristics, they are examples of systems and much of the theory of systems security applies to them.

Next-generation networks must address challenges such as exponential user growth, escalating traffic demands, and the proliferation of diverse services requiring both high data rates and ultra-low latency. Network slicing has emerged as a critical solution, enabling resource isolation to improve service efficiency. While traditional hard slicing ensures strict resource partitioning, it often results in significant underutilization. To mitigate this limitation, soft slicing allows dynamic resource sharing across slices, improving overall utilization. However, this approach introduces challenges, including potential violations of Quality of Service (QoS) guarantees and reductions in allocated resources compared to initial provisions. This paper presents a comprehensive soft slicing framework that addresses these key challenges by (1) ensuring user-level QoS guarantees, (2) incorporating slice dissatisfaction into the optimization model, (3) implementing a holistic resource management strategy, and (4) supporting hybrid 6G use cases. The problem is formulated as a Mixed Integer Linear Programming (MILP) model, aiming to maximize network utilization while minimizing slice dissatisfaction. Given the NP-hard nature of the problem, we propose the Heuristic Resource Allocation for Soft Slicing (HRASS) algorithm, which achieves near-optimal performance with significantly reduced computational complexity. Experimental results demonstrate that HRASS effectively improves resource utilization while mitigating the limitations of hard slicing.

The fifth-generation mobile network presents a wide range of services which have different requirements in terms of performance, bandwidth, reliability, and latency. The legacy networks are not capable to handle these diverse services with the same physical infrastructure. In this way, network virtualization presents a reliable solution named network slicing that supports service heterogeneity and provides differentiated resources to each service. Network slicing enables network operators to create multiple logical networks over a common physical infrastructure. In this research article, we have designed and implemented an intent-based network slicing system that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just need to provide higher-level network configurations in the form of intents/contracts for a network slice, and in return, our system deploys and configures the requested resources accordingly. Further, our system grants the automation of the network configurations process and reduces the manual effort. It has an intent-based networking (IBN) tool which can control, manage, and monitor the network slice resources properly. Moreover, a deep learning model, the generative adversarial neural network (GAN), has been used for the management of network resources. Several tests have been carried out with our system by creating three slices, which shows better performance in terms of bandwidth and latency.

Network slicing is a vital component of the 5G system to support diverse network scenarios, creating virtual networks (slices) by mapping virtual network requests to real networks. The mapping is an arduous computing process, mathematically studied and known as the Virtual Network Embedding (VNE) problem, and its complexity is NP-Hard. The mapping process is oriented to respect the QoS demands from the virtual network requests and the available resources in the physical-substrate infrastructure. Meta-heuristic approaches are a suitable way to solve the VNE problems because of their capacity to escape from the local optimum and adapt the solution search to complex networks; these abilities are essential in 5G networks scenarios. This article presents a systematic review of meta-heuristics organized by application, development and problem-solving approaches to VNE. It also provides the standard parameters to model the infrastructure and virtual network requests to simulate network slicing as a service. Finally, our work proposes some future research based on the discovered gaps.

With the advent of fifth generation (5G) mobile communication network slicing technology, the range of application scenarios is expanding significantly. For 5G to function well, it necessitates little delay, a fast rate of data transfer, and the ability to handle a large number of connections. This demanding service requires the allocation of resources in a dynamic manner, while maintaining a very high level of reliability in terms of Quality of Service (QoS).The applications like autonomous driving, telesurgery, etc. have stringent QoS demands and the present design of slices is not suitable for these services. Therefore, latency has been regarded as a crucial factor in the design of the slices. Conventional optimization algorithms often lack robustness and adaptability to dynamic environments, getting stuck in local optima and failing to generalize to varying conditions. Our solution utilizes Reinforcement Learning (RL) to allocate resources to the slices. The utilization of restricted resources can be optimized through the reconfiguration of slices. The ability of RL to acquire knowledge from the surroundings enables our solution to adjust to varying network conditions, enhance the allocation of resources and improve quality of service over a period of time for different network slices. This study introduces the Deep Actor Critic Reinforcement Learning- Network Slicing (DACRL-NS) technique, which utilizes Deep Actor Critic Reinforcement learning for efficient resource allocation to network slices. The objective is to achieve optimal throughput in the network. If the slices fail to meet the minimum criteria, they will be omitted from the allocation. With increasing training episodes, our Actor-Critic algorithm enhances average cumulative rewards and resource allocation efficiency, demonstrating continuous learning and improved decision-making.The simulated suggested system demonstrates an average throughput improvement of 8.92% and 16.36% with respect to the rate requirement and latency requirement, respectively. The data also demonstrate a 17.14% increase in the overall network throughput.

The deployment of industrial robots in time‐critical applications demands ultra‐low latency and high reliability in communication systems. This study presents a novel delay optimisation framework for industrial robot control systems using 6G network slicing technologies. A Gale–Shapley (GS)‐based elastic switching model is proposed to dynamically match robot controllers to optimised network slices and base stations under latency‐sensitive conditions. To enhance resource adaptability, a long short‐term memory (LSTM)‐based encoder‐decoder structure is developed for predictive resource allocation across slices. The proposed integrated matching mechanism achieves a success rate of 91.16% for slice access and a base station access rate of 90.83%, outperforming conventional integrated and two‐stage schemes. The LSTM‐based resource allocation achieves a mean absolute error of 0.04 and a violation rate below 10%, with over 92% utilisation of both node and link resources. Experimental simulations demonstrate a consistent end‐to‐end latency below 7 ms and a throughput of 18.4 Mbit/s, validating the proposed models' effectiveness in ensuring robust, real‐time communication for industrial robot operations. This research contributes a scalable solution for dynamic 6G network resource management, providing a foundation for advanced industrial automation and intelligent manufacturing. A novel elastic switching model based on the Gale–Shapley (GS) algorithm and a resource allocation model grounded in an LSTM encoder‐decoder structure, tailored for 6G network slicing scenarios. Through extensive simulations, our model demonstrates a 91.16% slice access success rate, average latency below 5 ms, and resource utilisation exceeding 92%, outperforming conventional integrated matching and static allocation methods.

Maintaining Service Level Agreements (SLAs) and enabling mission-critical applications in dynamic 5G environments depends on accurate, proactive Quality of Service (QoS) prediction. However, existing approaches often rely on static, reactive models that fail to capture temporal traffic dynamics and struggle with the severe class imbalance inherent in network anomalies. To address these gaps, this study proposes a robust dual-stream architecture trained on high-fidelity data generated via Digital Twin Network Emulation. The methodology decomposes the prediction task into two specialized streams: a Bi-Directional LSTM (BiLSTM) regressor that leverages temporal lag features to predict continuous Packet Loss Rate (PLR), and a Residual MLP (ResNet-MLP) classifier that predicts Packet Delay. To overcome the critical issue of minority class neglect, we implement dynamic K-Means binning for target definition and utilize the Synthetic Minority Over-sampling Technique (SMOTE) combined with Focal Loss during training. Experimental results demonstrate that the proposed framework significantly outperforms state-of-the-art baselines, including XGBoost and Random Forest, achieving an   of 0.91 for PLR prediction and an overall accuracy of 81.1% for delay classification. More importantly, the framework addresses the blind spot to medium-latency events that were seen in previous studies, with a macro-recall of 0.86. Robustness testing confirms the model's stability under simulated signal noise, and SHAP interpretability analysis validates its alignment with physical network parameters, providing network operators with a reliable, transparent tool for automated slice management.

With the growing demand for massive access and data transmission requests, terrestrial communication systems are inefficient in providing satisfactory services. Compared with terrestrial communication networks, satellite communication networks have the advantages of wide coverage and support for massive access services. Satellite–terrestrial integrated networks are indispensable parts of future B5G/6G networks. Challenges arise for implementing and operating a successful satellite–terrestrial integrated network, including differentiated user requirements, infrastructure compatibility, limited resource constraints, and service provider incentives. In order to support diversified services, a multi-domain network slicing approach is proposed in this study, in which network resources from both terrestrial and satellite networks are combined to build alternative routes when serving the same slice request as virtual private networks. To improve the utilization efficiency of limited resources, slice admission control is formulated as a mechanism design problem. To encourage participation and cooperation among different service providers, a multi-sided ascending-price auction mechanism is further proposed as a game theory-based solution for slice admission control and resource allocation, in which multiple strategic service providers maximize their own utilities by trading bandwidth resources. The proposed auction mechanism is proven to be strongly budget-balanced, individually rational, and obviously truthful. To validate the effectiveness of the proposed approach, real-world historical traffic data are used in the simulation experiments and the results show that the proposed approach is asymptotically optimal with the increase in users and competitive with the polynomial-time optimal trade mechanism, in terms of admission ratio and service provider profit.

Network slicing is one 5G network enabler that may be used to enhance the requirements of mission-critical Machine Type Communications (mcMTC) in critical IoT applications. But, in applications with high mobility support, the network slicing will also be influenced by users’ movement, which is necessary to handle the dynamicity of the system, especially for critical slices that require fast and reliable delivery from End to End (E2E). To fulfill the desired service quality (QoS) of critical slices due to their users’ movement. This paper presents mobility awareness for such types of applications through mobility prediction, in which the network can determine which cell the user is in near real-time. Furthermore, the proposed next-cell mobility prediction framework is developed as a multi-classification task, where we exploited Long Short-Term Memory (LSTM) and the collected historical mobility profiles of moving users to allow more accurate short- and long-term predictions of the candidate next-cell. Then, within the scope of high mobility mission-critical use cases, we evaluate the effectiveness of the proposed LSTM classifier in vehicular networks. We have used a real vehicle mobility dataset that is obtained from SUMO deployed in Bejaia, Algeria urban environment. Ultimately, we conducted a set of experiments on the classifier using datasets with various history lengths, and the results have validated the effectiveness of the performed predictions on short-term mobility prediction. Our experiments show that the proposed classifier performs better on longer history datasets. While compared to traditional Machine Learning (ML) algorithms used for classification, the proposed LSTM model outperformed ML methods with the best accurate prediction results.