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Summary: The rapid evolution of user equipment (UE) and 5G networks drives significant transformations, bringing technology closer to end‐users. Managing resources in densely crowded areas such as airports, train stations, and bus terminals poses challenges due to diverse user demands. Integrating mobile edge computing (MEC) and network function virtualization (NFV) becomes vital when the service provider's (SP) primary goal is maximizing profitability while maintaining service level agreement (SLA). Considering these challenges, our study addresses an online resource allocation problem in an MEC network where computing resources are limited, and the SP aims to boost profit by securely admitting more UE requests at each time slot. Each UE request arrival rate is unknown, and the requirement is specific resources with minimum cost and delay. The optimization problem objective is achieved by allocating resources to requests at the MEC network in appropriate cloudlets, utilizing abandoned instances, reutilizing idle and soft slice instances to shorten delay and reduce costs, and immediately scaling inappropriate instances, thus minimizing the instantiation of new instances. This paper proposes a deep reinforcement learning (DRL) method for request prediction and resource allocation to mitigate unnecessary resource waste. Simulation results demonstrate that the proposed approach effectively accepts network slice requests to maximize profit by leveraging resource availability, reutilizing instantiated resources, and upholding goodwill and SLA. Through extensive simulations, we show that our proposed DRL‐based approach outperforms other state‐of‐the‐art techniques, namely, MaxSR, DQN, and DDPG, by 76%, 33%, and 23%, respectively. [ABSTRACT FROM AUTHOR]

The fifth-generation cellular network can handle extremely diverse services and user requirements including high transmission rates, low latency, and large connections. The concept of network slicing appears as an effective solution to satisfy these various needs, where different virtual networks for various service scenarios are implemented on the same physical infrastructure. The sharing technique has several issues, such as ensuring Quality of Service (QoS) satisfaction, fairness, and performance resource allocation across different slices. The variability of these slices mostly resides in their number of demands and the priority of each slice. Slices that have high demands and priority are often allocated a significant number of resources. However, due to the limitation of resources management strategies of network slicing. Moreover, the resource allocation strategy excludes the slice requests of low priority with its demands from the slice. In this scenario, some slices with low demand and priority may be suffering from starvation. The primary focus of this study is to address and overcome these issues in core network slicing. This paper proposes a resource allocation management model Based on Markov decision process and adaptive fairness resources, that considers the Quality of service of each slice based on the changing demands per slice at a specific decision epoch. The experiment results show that the proposed solution with maximizing utilization policy has outperformed by 40% compared to exist scheme in terms of QoS, which proposed a prioritized dynamic allocation scheme. Moreover, fair resource allocation ensures maximizing the overall network while maintaining the requirements of each slice and QoS guarantees. [ABSTRACT FROM AUTHOR]

The development of communication network connects all over the world in device driven technology through 6G. With the development of 6G defense, the problems in the Network of resources in edge computing cause latency dependencies. The main problem is switch-to-switch way has connection disappointment, and numerous packets can get lost. Along these lines, some kinds of connection quality checking and way recuperation plans are expected to conquer the connection disappointment. To propose a Proactive Delay Aware Self-Scheduling Protocol (PDAS2P) for Edge Enabled network slicing in 6G Networks. The proposed method initially analysis the Congestion Aware Neighbor Discovery (CAND) based on optimized energy levels in the node for efficient data transfer. Then the Hyper Intensive Link Stability Rate (HILSR) estimates table-driven routing information to stabilize the transmission route. Based on that estimation, the Pipelined Time Variant Transmission Rate (PTVTR) is calculated by transmission in each duty cycle to improve the transmission delays by assigning priorities on operative routing in multi-point consideration. Then Proactive Delay Aware Self-Scheduling Protocol (PDAS2P) is attained to the frequency limits of transmission rate to allocate the route and improve Quality of Service (QoS). The proposed 6G-QoS framework has been implemented in the 6G network communication-based scheduling. The simulation result throughput performance is 99% and routing performance is 99% for 100 nodes. It provides high accuracy of QoS prediction and has been validated by comparing the predicted QoS values with other methods like Energy Efficient Resource Allocation Model (EERAM), Main Task Off-loading Scheduling Algorithm (MTOSA), and Improved Multi Objective Cuckoo Search (IMOCS). [ABSTRACT FROM AUTHOR]

Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks. [ABSTRACT FROM AUTHOR]

Next-generation mobile networks, such as those beyond the 5th generation (B5G) and 6th generation (6G), have diverse network resource demands. Network slicing (NS) and device-to-device (D2D) communication have emerged as promising solutions for network operators. NS is a candidate technology for this scenario, where a single network infrastructure is divided into multiple (virtual) slices to meet different service requirements. Combining D2D and NS can improve spectrum utilization, providing better performance and scalability. This paper addresses the challenging problem of dynamic resource allocation with wireless network slices and D2D communications using deep reinforcement learning (DRL) techniques. More specifically, we propose an approach named DDPG-KRP based on deep deterministic policy gradient (DDPG) with K-nearest neighbors (KNNs) and reward penalization (RP) for undesirable action elimination to determine the resource allocation policy maximizing long-term rewards. The simulation results show that the DDPG-KRP is an efficient solution for resource allocation in wireless networks with slicing, outperforming other considered DRL algorithms. [ABSTRACT FROM AUTHOR]

The sixth generation (6G) networks demand high security, low latency, and highly dependable standards and capacity. One of the essential components of 6G networks is flexible wireless network slicing. In this paper, we propose a hybrid model that combines a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM). The hybrid model is applied to the Unicauca IP Flow Version2 dataset. The CNN handles the automated feature section, while the BiLSTM is utilized for categorizing the suitable network slices. This hybrid model is capable of offering a reliable and effective network slice to the end user. The proposed hybrid model has an overall recognition rate of 97.21%, which reflects the applicability of the proposed approach. A stratified 10-fold cross-validation is used to assess the applicability of the proposed model. The main challenge for network service providers is to assign slices correctly. A clever method is needed to make a standard for accurately assigning network slices to an unidentified device when it asks for them. For each incoming request for new traffic, the proposed model forecasts the suitable network slice [ABSTRACT FROM AUTHOR]

Summary: The 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. [ABSTRACT FROM AUTHOR]

This study examined the integrated benefits of 5G New Radio, network slicing, and reinforcement learning (RL) mechanisms in addressing the challenges associated with the increasing proliferation of intelligent objects in communication networks. This study proposed an innovative architecture that initially employed network slicing to efficiently segregate and manage various service types. Subsequently, this architecture was enhanced by applying RL to optimize the subchannel and power allocation strategies. This dual approach was proven through simulation studies conducted in a suburban setting, highlighting the effectiveness of the method for optimizing the use of available frequency bands. The results highlighted significant improvements in mitigating interference and adapting to the dynamic conditions of the network, thereby ensuring efficient dynamic resource allocation. Further, the application of an RL algorithm enabled the system to adjust resources adaptively based on real-time network conditions, thereby proving the flexibility and responsiveness of the scheme to changing network scenarios. [ABSTRACT FROM AUTHOR]

The Open-Radio Access Network (O-RAN) alliance is leading the evolution of telecommunications towards a greater intelligence, openness, virtualization, and interoperability within mobile networks. The O-RAN standard incorporates of many components the Open-Central Unit (O-CU) and Open-Distributed Unit (O-DU), network slicing and heterogeneous base stations (BS). Together, these innovations have given rise to a three-tiered user association (UA) relationship in a type of network called heterogeneous network (HetNet) with network slicing-enabled. There is an absence of efficient UA schemes for achieving fair resource allocation in such network scenario. Hence, this study formulates the fairness-aware UA problem as a utility-based combinatorial optimization problem, which is computationally hard to solve. Hence, an efficient Binary Particle Swarm Optimization (BPSO)-based UA scheme is proposed to solve the problem. Through simulations of an O-RAN based HetNet with network slicing-enabled, performance of the proposed BPSO-UA scheme is compared against two other baseline UA schemes. Results demonstrate the effectiveness of the proposed BPSO-UA scheme in achieving high fairness through equitable network slicing resource allocation, thereby leading to higher user connectivity rate and comparable average spectral efficiency. This innovative approach sheds light on the potential of metaheuristic algorithms in tackling intricate UA challenges, offering valuable insights for the future design and optimization of mobile networks. [ABSTRACT FROM AUTHOR]

Cloud-native technologies are widely considered the ideal candidates for the future of vertical application development due to their boost in flexibility, scalability, and especially cost efficiency. Since multi-site support is paramount for 5G, we employ a multi-cluster model that scales on demand, shifting the boundaries of both horizontal and vertical scaling for shared resources. Our approach is based on the liquid computing paradigm, which has the benefit of adapting to the changing environment. Despite being a decentralized deployment shared across data centers, the 5G mobile core can be managed as a single cluster entity running in a public cloud. We achieve this by following the cloud-native patterns for declarative configuration based on Kubernetes APIs and on-demand resource allocation. Moreover, in our setup, we analyze the offloading of both the Open5GS user and control plane functions under two different peering scenarios. A significant improvement in terms of latency and throughput is achieved for the in-band peering, considering the traffic between clusters is ensured by the Liqo control plane through a VPN tunnel. We also validate three end-to-end network slicing use cases, showcasing the full 5G core automation and leveraging the capabilities of Kubernetes multi-cluster deployments and inter-service monitoring through the applied service mesh solution. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks.

Industrial Internet combines the industrial system with Internet connectivity to build a new manufacturing and service system covering the entire industry chain and value chain. Its highly heterogeneous network structure and diversified application requirements call for the applying of network slicing technology. Guaranteeing robust network slicing is essential for Industrial Internet, but it faces the challenge of complex slice topologies caused by the intricate interaction relationships among Network Functions (NFs) composing the slice. Existing works have not concerned the strengthening problem of industrial network slicing regarding its complex network properties. Towards this end, we aim to study this issue by intelligently selecting a subset of most valuable NFs with the minimum cost to satisfy the strengthening requirements. State-of-the-art AlphaGo series of algorithms and the advanced graph neural network technology are combined to build the solution. Simulation results demonstrate the superior performance of our scheme compared to the benchmark schemes.

Abstract At present, social networks have become an indispensable medium in people's daily life and work. However, concerns about personal privacy leakage and identity information theft have also emerged. Therefore, a communication network system based on network slicing is constructed to strengthen the protection of communication network privacy. The chameleon hash algorithm is used to optimize attribute-based encryption and enhance the privacy protection of communication networks. On the basis of optimizing the combination of attribute encryption and homomorphic encryption,, a communication network privacy protection method using homomorphic encryption for network slicing and attribute is designed. The results show that the designed network energy consumption is low, the average energy consumption calculation is reduced by 8.69%, and the average energy consumption calculation is reduced by 14.3%. During data transmission, the throughput of the designed network can reach about 700 Mbps at each stage, which has a high efficiency.. The above results demonstrate that the designed communication network provides effective privacy protection. Encrypted data can be decrypted and tracked in the event of any security incident. This is to protect user privacy and provide strong technical support for communication network security.

Abstract Network slicing is crucial to the 5G architecture because it enables the virtualization of network resources into a logical network. Network slices are created, isolated, and managed using software-defined networking (SDN) and network function virtualization (NFV). The virtual network function (VNF) manager must devise strategies for all stages of network slicing to ensure optimal allocation of physical infrastructure (PI) resources to high-acceptance virtual service requests (VSRs). This paper investigates two independent network slicing frameworks named as dual-slice isolation and management strategy (D-SIMS) and recommends the best of the two based on performance measurements. D-SIMS places VNFs for network slicing using self-sustained resource reservation (SSRR) and master-sliced resource reservation (MSRR), with some flexibility for the VNF manager to choose between them based on the degree to which the underlying physical infrastructure has been sliced. The present research work consists of two phases: the first deals with the creation of slices, and the second with determining the most efficient way to distribute resources among them. A deep neural network (DNN) technique is used in the first stage to generate slices for both PI and VSR. Then, in the second stage, we propose D-SIMS for resource allocation, which uses both the fuzzy-PROMETHEE method for node mapping and Dijkstra’s algorithm for link mapping. During the slice creation phase, the proposed DNN training method’s classification performance is evaluated using accuracy, precision, recall, and F1 score measures. To assess the success of resource allocation, metrics such as acceptance rate and resource effectiveness are used. The performance benefit is investigated under various network conditions and VSRs. Finally, to demonstrate the importance of the proposed work, we compare the simulation results to those in the academic literature.

Abstract Within the evolving landscape of fifth‐generation (5G) wireless networks, the introduction of network‐slicing protocols has become pivotal, enabling the accommodation of diverse application needs while fortifying defences against potential security breaches. This study endeavours to construct a comprehensive network‐slicing model integrated with an attack detection system within the 5G framework. Leveraging software‐defined networking (SDN) along with deep learning techniques, this approach seeks to fortify security measures while optimizing network performance. This undertaking introduces network slicing predicated on SDN with the OpenFlow protocol and Ryu control technology, complemented by a neural network model for attack detection using deep learning methodologies. Additionally, the proposed convolutional neural networks‐long short‐term memory approach demonstrates superiority over conventional ML algorithms, signifying its potential for real‐time attack detection. Evaluation of the proposed system using a 5G dataset showcases an impressive accuracy of 99%, surpassing previous studies, and affirming the efficacy of the approach. Moreover, network slicing significantly enhances quality of service by segmenting services based on bandwidth. Future research will concentrate on real‐world implementation, encompassing diverse dataset evaluations, and assessing the model's adaptability across varied scenarios.

[Introduction] In response to the inability of existing communication conditions to meet the intelligent O&M and ecological monitoring needs of offshore wind farm, this article explores and proposes a 5G customized network scheme to solve the problems of poor signal accessibility, incomplete network coverage, and low smoothness in offshore wind farms. [Methods] In this paper, a comprehensive O&M and monitoring scheme was proposed by using 5G customized network technology, which was as follows: through the deployment of 5G macro base stations outdoors, 5G indoor distribution in towers, and underwater laying of optical networks, the 3D coverage of wind farm communication networks was realized; Based on 5G slicing technology, one network could be used for multiple purposes to meet the needs of offshore wind farms for network differentiation; computing nodes were deployed in the centralized control center computer room, and private network data was forwarded through the edge UPF (user plane function) to achieve computing-network integration. [Result] The intelligent management and ecological environment monitoring scheme for offshore wind farms based on 5G technology proposed in this article has been piloted and tested based on the project. The test results show that the maximum effective coverage radius of 5G base stations reaches 11.3 km, and the stable transmission uplink rate reaches 5 Mbps, meeting the needs of observation data return and unmanned ship video return in the sea area. By deploying two 2.1G 8TR enhanced base stations on the booster station and wind turbine to enhance sea area coverage, the pull-net test around the wind farm verified that the 5G private network can effectively cover wind farms, with a coverage rate of 98.4%, which can basically meet the coverage needs of the entire wind farm. [Conclusion] This scheme utilizes a 5G private network to cover the sea area of the wind farm and achieves underwater communication through STN (Smart Transport Network) and underwater optical networks. Consequently, it innovatively constructs a 3D ocean monitoring and communication network, laying the communication foundation for the intelligent management and ecological environment monitoring of offshore wind farms.

Abstract 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.

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At present, social networks have become an indispensable medium in people's daily life and work. However, concerns about personal privacy leakage and identity information theft have also emerged. Therefore, a communication network system based on network slicing is constructed to strengthen the protection of communication network privacy. The chameleon hash algorithm is used to optimize attribute-based encryption and enhance the privacy protection of communication networks. On the basis of optimizing the combination of attribute encryption and homomorphic encryption,, a communication network privacy protection method using homomorphic encryption for network slicing and attribute is designed. The results show that the designed network energy consumption is low, the average energy consumption calculation is reduced by 8.69%, and the average energy consumption calculation is reduced by 14.3%. During data transmission, the throughput of the designed network can reach about 700 Mbps at each stage, which has a high efficiency.. The above results demonstrate that the designed communication network provides effective privacy protection. Encrypted data can be decrypted and tracked in the event of any security incident. This is to protect user privacy and provide strong technical support for communication network security. [ABSTRACT FROM AUTHOR]

The ongoing evolution of network softwarization is particularly evident in mobile networks. The 5G standard defines core network functions as discrete processes, facilitating seamless virtualization. The next crucial step is to enable cloud-based deployments independent of specific hardware and hypervisors. In this work, we propose a testbed designed for cloud-based 5G network deployment. Our primary objective is to create an environment conducive to experimenting with cloud-based 5G core deployments and facilitating future research in this domain. We rigorously verified the deployment's correctness, identified key issues, and developed effective solutions to create a robust environment for emerging applications. Additionally, we introduce an innovative extension to a widely used 5G core network implementation by creating a network function that replicates the functionalities of the Network Exposure Function (NEF). This new component facilitates advanced analytics and AI-based optimization, significantly enhancing cloud-based deployments of virtualized 5G networks. [ABSTRACT FROM AUTHOR]

Network slicing is crucial to the 5G architecture because it enables the virtualization of network resources into a logical network. Network slices are created, isolated, and managed using software-defined networking (SDN) and network function virtualization (NFV). The virtual network function (VNF) manager must devise strategies for all stages of network slicing to ensure optimal allocation of physical infrastructure (PI) resources to high-acceptance virtual service requests (VSRs). This paper investigates two independent network slicing frameworks named as dual-slice isolation and management strategy (D-SIMS) and recommends the best of the two based on performance measurements. D-SIMS places VNFs for network slicing using self-sustained resource reservation (SSRR) and master-sliced resource reservation (MSRR), with some flexibility for the VNF manager to choose between them based on the degree to which the underlying physical infrastructure has been sliced. The present research work consists of two phases: the first deals with the creation of slices, and the second with determining the most efficient way to distribute resources among them. A deep neural network (DNN) technique is used in the first stage to generate slices for both PI and VSR. Then, in the second stage, we propose D-SIMS for resource allocation, which uses both the fuzzy-PROMETHEE method for node mapping and Dijkstra's algorithm for link mapping. During the slice creation phase, the proposed DNN training method's classification performance is evaluated using accuracy, precision, recall, and F1 score measures. To assess the success of resource allocation, metrics such as acceptance rate and resource effectiveness are used. The performance benefit is investigated under various network conditions and VSRs. Finally, to demonstrate the importance of the proposed work, we compare the simulation results to those in the academic literature. [ABSTRACT FROM AUTHOR]

Within the evolving landscape of fifth‐generation (5G) wireless networks, the introduction of network‐slicing protocols has become pivotal, enabling the accommodation of diverse application needs while fortifying defences against potential security breaches. This study endeavours to construct a comprehensive network‐slicing model integrated with an attack detection system within the 5G framework. Leveraging software‐defined networking (SDN) along with deep learning techniques, this approach seeks to fortify security measures while optimizing network performance. This undertaking introduces network slicing predicated on SDN with the OpenFlow protocol and Ryu control technology, complemented by a neural network model for attack detection using deep learning methodologies. Additionally, the proposed convolutional neural networks‐long short‐term memory approach demonstrates superiority over conventional ML algorithms, signifying its potential for real‐time attack detection. Evaluation of the proposed system using a 5G dataset showcases an impressive accuracy of 99%, surpassing previous studies, and affirming the efficacy of the approach. Moreover, network slicing significantly enhances quality of service by segmenting services based on bandwidth. Future research will concentrate on real‐world implementation, encompassing diverse dataset evaluations, and assessing the model's adaptability across varied scenarios. [ABSTRACT FROM AUTHOR]

C-RAN (Cloud Radio Access Network) is a 5G architecture that consists of sites and three-layer Data Centers (DCs), which include the central office DC, local DC, and regional DC. Network slicing, which enables infrastructure providers (InP) to create independent logical networks, is essential in this architecture. By utilizing this technology, InPs can maximize the utility of the network by providing slices to service providers in response to their slice requests. However, almost all of the recent research on slice admission control (SAC) schemes has only considered one or two layers of DCs, which limits the efficiency of the slicing process and decreases network utility. To address these issues, this paper proposes an intelligent SAC scheme called ISAC that considers all three-layer DCs. Instead of relying on reinforcement learning algorithms like Q-learning, which are effective in discrete environments with limited state space but give poor performance in continuous environments, ISAC employs the Approximate Reinforcement Learning (ARL) algorithm. ARL is better suited for 5G network modeling because it can adapt to continuous environments, allowing for a more accurate representation of the underlying physical processes. Extensive simulation studies demonstrate that ISAC significantly improves performance in terms of slice request rejection rates, InP revenue, accepting more slices, and optimizing resource utilization. [ABSTRACT FROM AUTHOR]

The immense growth of mobile networks leads to versatile applications and new demands. The improved concert, transferability, flexibility, and performance of innovative network services are applied in diversified fields. More unique networking concepts are incorporated into state-of-the-art mobile technologies to expand these dynamic features further. This paper presents a novel system architecture of slicing and pairing networks with intra-layer and inter-layer functionalities in 5th generation (5G) mobile networks. The radio access network layer slices and the core network layer slices are paired up using the network slicing pairing functionalities. The physical network elements of such network slices will be logically assigned entities called softwarization of the network. Such a novel system architecture called network sliced softwarization of 5G mobile networks (NSS-5G) has shown better performances in terms of end-to-end delay, total throughput, and resource utilization when compared to traditional mobile networks. Thus, effective resource management is achieved using NSS-5G. This study will pave the way for future softwarization of heterogeneous mobile applications. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach towards space–ground integrated communication systems by combining terrestrial cellular networks, UAV networks, and satellite networks, leveraging advanced slicing technology. The proposed architecture addresses the challenges posed by future user surges and aims to reduce network overhead effectively. Central to our approach is the introduction of a marginal mobile station (MS)-assisted network resource allocation decision architecture. Building upon this foundation, we introduce the DP-DQN model, an enhanced decision-making algorithm tailored for MSs in dynamic network environments. Furthermore, this study introduces a feedback mechanism to ensure the accuracy and adaptability of the marginalization model over time. Through extensive simulations and experimental validations, our DP-DQN-based edge decision method demonstrates substantial potential in alleviating core network overhead while improving success access ratios compared to conventional methods. [ABSTRACT FROM AUTHOR]

Zero-touch networks (ZTNs) can provide autonomous network solutions by integrating software-based solutions for various emerging 5G and 6G applications. The current literature does not provide any suitable end-to-end network management and resource-slicing solutions for service function chaining (SFC) and user intent-based (time and cost preference) 6G/non-6G application execution over ZTNs enabled by mobile edge computing, network function virtualization, and software-defined networking. To tackle these challenges, this work initiates an end-to-end network management and user intent-aware intelligent network resource-slicing scheme for SFC-based 6G/non-6G application execution over ZTNs, taking into account various virtual and physical resources, task workloads, service requirements, and task numbers. The results depicted that at least 25.27% average task implementation delay gain, 6.15% energy gain, and 11.52% service monetary gain are realized in the proposed scheme over the compared schemes. [ABSTRACT FROM AUTHOR]

Summary: Network architects and engineers face challenges in meeting the increasing complexity and low‐latency requirements of various services. To tackle these challenges, multi‐access edge computing (MEC) has emerged as a solution, bringing computation and storage resources closer to the network's edge. This proximity enables low‐latency data access, reduces network congestion, and improves quality of service. Effective resource allocation is crucial for leveraging MEC capabilities and overcoming limitations. However, traditional approaches lack intelligence and adaptability. This study explores the use of deep reinforcement learning (DRL) as a technique to enhance resource allocation in MEC. DRL has gained significant attention due to its ability to adapt to changing network conditions and handle complex and dynamic environments more effectively than traditional methods. The study presents the results of applying DRL for efficient and dynamic resource allocation in MEC Computing, optimizing allocation decisions based on real‐time environment and user demands. By providing an overview of the current research on resource allocation in MEC using DRL, including components, algorithms, and the performance metrics of various DRL‐based schemes, this review article demonstrates the superiority of DRL‐based resource allocation schemes over traditional methods in diverse MEC conditions. The findings highlight the potential of DRL‐based approaches in addressing challenges associated with resource allocation in MEC. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This research work analyzes economic alternatives for the provision of ultra-reliable low latency communication (URLLC) and enhanced mobile broadband (eMBB) services by mobile network operators over the same fifth-generation (5G) network. Two business models are proposed to provide the two services to end users. Concretely, a monopoly is a single operator who offers both services, and a duopoly is two different operators that share network resources and offer one service each. In addition, two types of network scenarios for resource sharing are studied. Specifically, a shared network (SN) is a type of network scenario allowing resources to be shared between the two services without priority. A differentiated network (DN) is a type of network scenario that allows resources to be shared between the two services with a priority to URLLC service using network slicing (NS). Regarding the economic aspects, the incentive is modeled through the user’s utility and the operator’s benefit. At the same time, game theory is used to model the strategic interaction between users and operators, and queuing theory is used to model the interaction between the two services. We conclude that the monopoly social welfare (SW) is closer to the SW of the social optimum than the duopoly SW. In addition, the DN scenario to offer the services through NS is more suitable than the SN scenario since the point of view of service prices, user utilities, and operator benefit. [ABSTRACT FROM AUTHOR]

Network communication has grown rapidly with massive demands of services. Moreover, resource allocation in networking is a fundamental and crucial issue that cannot ensure the network's stability and efficiency with the myriad requirements of different services. Various vertical businesses may seek varied network services, particularly in the Fifth-Generation networks and Beyond (5G+). The pros of Fifth-Generation communication networks are to outperform 4G in performance by having higher bandwidth, minimum latency, more capacity, and QoS (Quality of Service). Software-Defined Network (SDN) and Network Function Virtualization (NFV) are two technologies that are combined in the next generation cellular network to provide improved network management. The primary idea behind resource allocation (RA) in the next generation network is the concept of network slicing where the network resources are virtually partitioning into many separate networks. Each separated network must satisfy the unique needs of the required service to achieve the required QoS. In this survey, we focus on resource management issues related to network slicing and tackling the biggest obstacles in this field while offering a thorough and up-to-date overview of this field. Thus, thorough analysis of the allocation of resources on the access side and core side of the network communication was sought. Also, demonstrates how revolutionary techniques that are used to support the management of sliced networks which are based on Machine Learning (ML) and Artificial Intelligence (AI). Importantly, use appropriate ML techniques such as deep learning for predicting the network condition and Reinforcement learning to learn optimal allocation policy without depending on prior knowledge and other techniques such as classification and clustering to aggregate the similar needs of users into separate slices. This could help to enhance resource utilization by allocating a sufficient amount of resource as needed based on ML algorithms and optimal utilization of resources and reducing operational costs by real-time adjustment of it based on user demands and network conditions. [ABSTRACT FROM AUTHOR]

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The rapid development of 5G networks and the anticipation of 6G technologies have ushered in an era of highly customizable network environments facilitated by the innovative concept of network slicing. This technology allows the creation of multiple virtual networks on the same physical infrastructure, each optimized for specific service requirements. Despite its numerous benefits, network slicing introduces significant security vulnerabilities that must be addressed to prevent exploitation by increasingly sophisticated cyber threats. This review explores the application of cutting-edge technologies—Artificial Intelligence (AI), specifically Machine Learning (ML), Software-Defined Networking (SDN), and Network Functions Virtualization (NFV)—in crafting advanced security solutions tailored for network slicing. AI's predictive threat detection and automated response capabilities are analysed, highlighting its role in maintaining service integrity and resilience. Meanwhile, SDN and NFV are scrutinized for their ability to enforce flexible security policies and manage network functionalities dynamically, thereby enhancing the adaptability of security measures to meet evolving network demands. Thoroughly examining the current literature and industry practices, this paper identifies critical research gaps in security frameworks and proposes innovative solutions. We advocate for a holistic security strategy integrating ML, SDN, and NFV to enhance data confidentiality, integrity, and availability across network slices. The paper concludes with future research directions to develop robust, scalable, and efficient security frameworks capable of supporting the safe deployment of network slicing in next-generation networks. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) of mobile networks is emerging as a key enabler of modern factory automation (FA) applications that ensure timely and reliable data exchange between network components. Network slicing (NS), which shares an underlying infrastructure with different applications and ensures application isolation, is the key 5G technology to support the diverse quality of service requirements of modern FA applications. In this article, an end-to-end (E2E) NS solution is proposed for FA applications in a 5G network. Regression approaches are used to construct a performance model for each slice to map the service level agreement to the network attributes. Interference coordination approaches for switched beam systems are proposed to optimize radio access network (RAN) performance models. A case study of a non-public network is used to show the proposed NS solution. Simulation result shows that for services with different QoS requirements, different IC approaches should be used as optimization methods. Design prediction using regression approach has been evaluated and shows that the prediction successful rate increases when more existing data are used. [ABSTRACT FROM AUTHOR]

Aiming at the problem of uneven inter-domain delay in slicing in multi-domain networks, this paper proposed an end-to-end dynamic cooperative slicing method based on inter-domain delay game. This method used game theory methods to allocate end-to-end delay constraints to different network domains, and deployed slices within the domain to obtain correspon-ding game benefits. It used the DDPG algorithm to continuously update the game strategy, and finally obtained the optimal delay allocation ratio and slice deployment solution. Experiments show that compared with the traditional static allocation algorithm, the proposed algorithm has obvious advantages. Compared with the empirically iterative DSDP method and the DQN-SNAF algorithm, the IDGA algorithm increased the slice deployment success rate by about 8% and 3% respectively under 100 slice requests. At the same time, the node resource utilization rate increased by about 5.75% and 1.96%. There are also significant advantages in reducing deployment costs. [ABSTRACT FROM AUTHOR]

The relentless growth of wireless networks coupled with the burgeoning demand for dynamic resource allocation has spurred research into innovative solutions. This paper presents an evaluation of Cloud-based Dynamic Network Scaling and Slicing (CDNSS) as a promising approach to meet the evolving demands of wireless networks. By leveraging cloud infrastructure and slicing techniques, CDNSS offers the flexibility to dynamically scale resources and allocate network slices tailored to diverse service requirements. The evaluation encompasses the performance of CDNSS in terms of scalability, resource utilisation and Quality of Service (QoS) provisioning. Through extensive simulations and analyses, the efficacy of CDNSS in addressing the challenges of resource management and service differentiation in wireless networks is demonstrated. The findings underscore the potential of CDNSS as a pivotal technology to enhance the efficiency and adaptability of wireless network architectures in the era of dynamic connectivity demands. [ABSTRACT FROM AUTHOR]

Integration of sensing and communication (ISAC) is pivotal for realizing the full spectrum of 6G networks, where aerial networks composed of unmanned aerial vehicles (UAVs) emerge as significant enhancers by establishing line-of-sight connections and increasing the coverage of terrestrial networks. Although network slicing can cater to diverse ISAC applications, strategies specifically for ISAC remain unexplored. This article proposes a lightweight scheme for network slicing in an aerial network with ISAC capability to maximize the service-level agreement (SLA) satisfaction ratio (SSR) by jointly optimizing the 3D placement and bandwidth resource allocation of UAVs. The scheme determines the 3D positions of the UAVs based on the locations of users served by high-priority slices and the SSRs of all slice requests. Then the scheme uses a minimum-weight matching algorithm for energy-efficient UAV path planning. Extensive simulations reveal our proposed scheme’s efficiencies in terms of resource allocation, cost savings, and energy savings. [ABSTRACT FROM AUTHOR]

Copyright of Informacije MIDEM: Journal of Microelectronics, Electronic Components & Materials is the property of MIDEM Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)