In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Se...In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.展开更多
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 structu...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.展开更多
Numerous Internet of Things (IoT) devices are being connected to the net-works to offer services. To cope with a large diversity and number of IoT ser-vices, operators must meet those needs with a more flexible and ef...Numerous Internet of Things (IoT) devices are being connected to the net-works to offer services. To cope with a large diversity and number of IoT ser-vices, operators must meet those needs with a more flexible and efficient net-work architecture. Network slicing in 5G promises a feasible solution for this issue with network virtualization and programmability enabled by NFV (Net-work Functions Virtualization). In this research, we use virtualized IoT plat-forms as the Virtual Network Functions (VNFs) and customize network slices enabled by NFV with different QoS to support various kinds of IoT services for their best performance. We construct three different slicing systems including: 1) a single slice system, 2) a multiple customized slices system and 3) a single but scalable network slice system to support IoT services. Our objective is to compare and evaluate these three systems in terms of their throughput, aver-age response time and CPU utilization in order to identify the best system de-sign. Validated with our experiments, the performance of the multiple slicing system is better than those of the single slice systems whether it is equipped with scalability or not.展开更多
With the rapid development of wireless network technologies and the growing de⁃mand for a high quality of service(QoS),the effective management of network resources has attracted a lot of attention.For example,in a pr...With the rapid development of wireless network technologies and the growing de⁃mand for a high quality of service(QoS),the effective management of network resources has attracted a lot of attention.For example,in a practical scenario,when a network shock oc⁃curs,a batch of affected flows needs to be rerouted to respond to the network shock to bring the entire network deployment back to the optimal state,and in the process of rerouting a batch of flows,the entire response time needs to be as short as possible.Specifically,we re⁃duce the time consumed for routing by slicing,but the routing success rate after slicing is re⁃duced compared with the unsliced case.In this context,we propose a two-stage dynamic net⁃work resource allocation framework that first makes decisions on the slices to which flows are assigned,and coordinates resources among slices to ensure a comparable routing suc⁃cess rate as in the unsliced case,while taking advantage of the time efficiency gains from slicing.展开更多
We investigate the design of satellite network slicing for the first time to provide customized services for the diversified applications,and propose a novel scheme for satellite end-to-end(E2E) network slicing based ...We investigate the design of satellite network slicing for the first time to provide customized services for the diversified applications,and propose a novel scheme for satellite end-to-end(E2E) network slicing based on 5G technology,which provides a view of common satellite network slicing and supports flexible network deployment between the satellite and the ground.Specifically,considering the limited satellite network resource and the characteristics of the satellite channel,we propose a novel satellite E2E network slicing architecture.Therein,the deployment of the network functions between the satellite and the ground is coordinately considered.Subsequently,the classification and the isolation technologies of satellite network sub-slices are proposed adaptively based on 5G technology to support resource allocation on demand.Then,we develop the management technologies for the satellite E2E network slicing including slicing key performance indicator(KPI) design,slicing deployment,and slicing management.Finally,the analysis of the challenges and future work shows the potential research in the future.展开更多
With emerging large volume and diverse heterogeneity of Internet of Things (IoT) applications, the one-size-fits-all design of the current 4G networks is no longer adequate to serve various types of IoT applications. ...With emerging large volume and diverse heterogeneity of Internet of Things (IoT) applications, the one-size-fits-all design of the current 4G networks is no longer adequate to serve various types of IoT applications. Consequently, the concepts of network slicing enabled by Network Function Virtualization (NFV) have been proposed in the upcoming 5G networks. 5G network slicing allows IoT applications of different QoS requirements to be served by different virtual networks. Moreover, these network slices are equipped with scalability that allows them to grow or shrink their instances of Virtual Network Functions (VNFs) when needed. However, all current research only focuses on scalability on a single network slice, which is the scalability at the VNF level only. Such a design will eventually reach the capacity limit of a single slice under stressful incoming traffic, and cause the breakdown of an IoT system. Therefore, we propose a new IoT scalability architecture in this research to provide scalability at the NS level and design a testbed to implement the proposed architecture in order to verify its effectiveness. For evaluation, three systems are compared for their throughput, response time, and CPU utilization under three different types of IoT traffic, including the single slice scaling system, the multiple slices scaling system and the hybrid scaling system where both single slicing and multiple slicing can be simultaneously applied. Due to the balanced tradeoff between slice scalability and resource availability, the hybrid scaling system turns out to perform the best in terms of throughput and response time with medium CPU utilization.展开更多
5G use cases,for example enhanced mobile broadband(eMBB),massive machine-type communications(mMTC),and an ultra-reliable low latency communication(URLLC),need a network architecture capable of sustaining stringent lat...5G use cases,for example enhanced mobile broadband(eMBB),massive machine-type communications(mMTC),and an ultra-reliable low latency communication(URLLC),need a network architecture capable of sustaining stringent latency and bandwidth requirements;thus,it should be extremely flexible and dynamic.Slicing enables service providers to develop various network slice architectures.As users travel from one coverage region to another area,the callmust be routed to a slice thatmeets the same or different expectations.This research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced networks.Rules of thumb which indicates the accuracy regarding the training data classification schemes within machine learning should be considered for validation and selection of the appropriate machine learning strategies.Therefore,this study discusses the network model’s design and implementation of self-optimization Fuzzy Qlearning of the decision-making algorithm for slice handover.The algorithm’s performance is assessed by means of connection-level metrics considering the Quality of Service(QoS),specifically the probability of the new call to be blocked and the probability of a handoff call being dropped.Hence,within the network model,the call admission control(AC)method is modeled by leveraging supervised learning algorithm as prior knowledge of additional capacity.Moreover,to mitigate high complexity,the integration of fuzzy logic as well as Fuzzy Q-Learning is used to discretize state and the corresponding action spaces.The results generated from our proposal surpass the traditional methods without the use of supervised learning and fuzzy-Q learning.展开更多
With various service types including massive machine-type communication(mMTC)and ultra-reliable low-latency communication(URLLC),fifth generation(5G)networks require advanced resources management strategies.As a metho...With various service types including massive machine-type communication(mMTC)and ultra-reliable low-latency communication(URLLC),fifth generation(5G)networks require advanced resources management strategies.As a method to segment network resources logically,network slicing(NS)addresses the challenges of heterogeneity and scalability prevalent in these networks.Traditional software-defined networking(SDN)technologies,lack the flexibility needed for precise control over network resources and fine-grained packet management.This has led to significant developments in programmable switches,with programming protocol-independent packet processors(P4)emerging as a transformative programming language.P4 endows network devices with flexibility and programmability,overcoming traditional SDN limitations and enabling more dynamic,precise network slicing implementations.In our work,we leverage the capabilities of P4 to forge a groundbreaking closed-loop architecture that synergizes the programmable data plane with an intelligent control plane.We set up a token bucket-based bandwidth management and traffic isolation mechanism in the data plane,and use the generative diffusion model to generate the key configuration of the strategy in the control plane.Through comprehensive experimentation,we validate the effectiveness of our architecture,underscoring its potential as a significant advancement in 5G network traffic management.展开更多
The advent of 5G technology has revolutionized network communication by introducing network slicing(NS)and virtualization to allow multiple network service providers(NSPs)to share infrastructure,thereby reducing deplo...The advent of 5G technology has revolutionized network communication by introducing network slicing(NS)and virtualization to allow multiple network service providers(NSPs)to share infrastructure,thereby reducing deployment costs and accelerating 5G adoption.While this new open marketplace enables NSPs to trade resources dynamically,it also exposes the system to security concerns,such as front-running and selfish-validation attacks,which can lead to market manipulation and strategy leakage.This paper presents TRADE-5G,a secure blockchainbased marketplace for 5G resource trading that mitigates these attacks and ensures fair,transparent resource allocation while preserving the cofidentiality of NSP strategies.Through extensive simulations,TRADE-5G demonstrates a substantial 18%improvement in user satisfaction and a 36%reduction in wasted resources compared to traditional models.Additionally,it opens new profit opportunities for NSPs through unused resources,establishing a more competitive,secure,and transparent 5G trading environment that exceeds the capabilities of traditional mobile networks.展开更多
This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method u...This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.展开更多
The integration of network slicing into a Device-to-Device(D2D)network is a promising technological approach for efficiently accommodating Enhanced Mobile Broadband(eMBB)and Ultra Reliable Low Latency Communication(UR...The integration of network slicing into a Device-to-Device(D2D)network is a promising technological approach for efficiently accommodating Enhanced Mobile Broadband(eMBB)and Ultra Reliable Low Latency Communication(URLLC)services.In this work,we aim to optimize energy efficiency and resource allocation in a D2D underlay cellular network by jointly optimizing beamforming and Resource Sharing Unit(RSU)selection.The problem of our investigation involves a Mixed-Integer Nonlinear Program(MINLP).To solve the problem effectively,we utilize the concept of the Dinkelbach method,the iterative weightedℓ1-norm technique,and the principles of Difference of Convex(DC)programming.To simplify the solution,we merge these methods into a two-step process using Semi-Definite Relaxation(SDR)and Successive Convex Approximation(SCA).The integration of network slicing and the optimization of short packet transmission are the proposed strategies to enhance spectral efficiency and satisfy the demand for low-latency and high-data-rate requirement applications.The Simulation results validate that the proposed method outperforms the benchmark schemes,demonstrating higher throughput ranging from 11.79%to 28.67%for URLLC users,and 13.67%to 35.89%for eMBB users,respectively.展开更多
As different requirements on mobility support will be introduced by diversified communication scenarios in the fifth generation （5G）, on demand mobility management is put forward to simplify signaling process, reduc...As different requirements on mobility support will be introduced by diversified communication scenarios in the fifth generation （5G）, on demand mobility management is put forward to simplify signaling process, reduce terminal power consumption, improve network efficiency and so on. In order to enable on demand mobility management in 5G networks, a mobility driven network slicing （MDNS） was proposed, which takes individual mobility support requirements into account while customizing networks for different mobile services. Within the MDNS framework, the actual levels of required mobility support are determined by a mobility description system, and network slice templates with the corresponding mobility management schemes are defined by a network slice description function. By instantiating the network slices, each mobile terminal could be directed to the network slice with the most appropriate mobility management scheme. Based on this, a prototype was implemented to validate the feasibility of MDNS framework, i.e. creating multiple network slices with different mobility management schemes. In addition, the performance evaluation on average cost of processing a mobility event is conducted for the proposed MDNS framework and the long term evolution （LTE） system, and operating benefits are analyzed including efficiency and scalability.展开更多
Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resou...Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures.Nonetheless,sustaining elevated Quality of Service(QoS)in dynamic,resource-limited systems poses significant hurdles.This study introduces an innovative packet-based proactive end-to-end(ETE)resource management system that facilitates network slicing with improved resilience and proactivity.To get around the drawbacks of conventional reactive systems,we develop a cost-efficient slice provisioning architecture that takes into account limits on radio,processing,and transmission resources.The optimization issue is non-convex,NP-hard,and requires online resolution in a dynamic setting.We offer a hybrid solution that integrates an advanced Deep Reinforcement Learning(DRL)methodology with an Improved Manta-Ray Foraging Optimization(ImpMRFO)algorithm.The ImpMRFO utilizes Chebyshev chaotic mapping for the formation of a varied starting population and incorporates Lévy flight-based stochastic movement to avert premature convergence,hence facilitating improved exploration-exploitation trade-offs.The DRL model perpetually acquires optimum provisioning strategies via agent-environment interactions,whereas the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.The DRL model perpetually acquires optimum provisioning methods via agent-environment interactions,while the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.Experimental findings reveal that the proactive ETE system outperforms DRL models and non-resilient provisioning techniques.Our technique increases PSSRr,decreases average latency,and optimizes resource use.These results demonstrate that the hybrid architecture for robust,real-time,and scalable slice management in future 6G networks is feasible.展开更多
Network slicing has gained popularity as a result of the advances in the fifth generation(5G)mobile network.Network slicing facilitates the support of different service types with varying requirements,which brings int...Network slicing has gained popularity as a result of the advances in the fifth generation(5G)mobile network.Network slicing facilitates the support of different service types with varying requirements,which brings into light the slicing-aware next generation mobile network architecture.While allowing resource sharing among multiple stakeholders,there is a long list of administrative negotiations among parties that have not established mutual trust.Distributed ledger technology may be a solution to mitigate the above issues by taking its decentralized yet immutable and auditable ledger,which may help to ease administrative negotiations and build mutual trust among multi-stakeholders.There have been many research interests in this direction which focus on handling various problems in network slicing.This paper aims at constructing this area of knowledge by introducing network slice from a standardization point of view to start with,and presenting security,privacy,and trust challenges of network slicing in 5G and beyond networks.Furthermore,this paper covers distributed ledger technologies basics and related approaches that tackle security,privacy,and trust threats in network slicing for 5G and beyond networks.The various proposals proposed in the literature are compared and presented.Lastly,limitations of current work and open challenges are illustrated as well.展开更多
With the acceleration of the intelligent transformation of power systems,the requirements for communication technology are increasingly stringent.The application of 5G mobile communication technology in power communic...With the acceleration of the intelligent transformation of power systems,the requirements for communication technology are increasingly stringent.The application of 5G mobile communication technology in power communication is analyzed.In this study,5G technology features,application principles,and practical strategies are discussed,and methods such as network slicing,customized deployment,edge computing collaborative application,communication equipment integration and upgrading,and multi-technology collaboration and complementation are proposed.It aims to effectively improve the efficiency,reliability,and security of power communication,solve the problem that traditional communication technology is difficult to meet the diversified needs of power business,and achieve the effect of optimizing the power communication network and supporting the intelligent development of the power system.展开更多
As an indispensable component of the emerging 6G networks,Space-Air-Ground Inte-grated Networks(SAGINs)are envisioned to provide ubiquitous network connectivity and services by integrating satellite networks,aerial ne...As an indispensable component of the emerging 6G networks,Space-Air-Ground Inte-grated Networks(SAGINs)are envisioned to provide ubiquitous network connectivity and services by integrating satellite networks,aerial networks,and terrestrial networks.In 6G SAGINs,a wide variety of network services with the features of diverse requirements,complex mobility,and multi-dimensional resources will pose great challenges to service provisioning,which urges the develop-ment of service-oriented SAGINs.In this paper,we conduct a comprehensive review of 6G SAGINs from a new perspective of service-oriented network.First,we present the requirements of service-oriented networks,and then propose a service-oriented SAGINs management architec-ture.Two categories of critical technologies are presented and discussed,i.e.,heterogeneous resource orchestration technologies and the cloud-edge synergy technologies,which facilitate the interoperability of different network segments and cooperatively orchestrate heterogeneous resources across different domains,according to the service features and requirements.In addition,the potential future research directions are also presented and discussed.展开更多
To solve the contradiction between limited spectrum resources and increasing communication demand,this paper proposes a wireless resource allocation scheme based on the Deep Q Network(DQN)to allocate radio resources i...To solve the contradiction between limited spectrum resources and increasing communication demand,this paper proposes a wireless resource allocation scheme based on the Deep Q Network(DQN)to allocate radio resources in a downlink multi-user cognitive radio(CR)network with slicing.Secondary users(SUs)are multiplexed using non-orthogonal multiple access(NOMA).The SUs use the hybrid spectrum access mode to improve the spectral efficiency(SE).Considering the demand for multiple services,the enhanced mobile broadband(eMBB)slice and ultrareliable low-latency communication(URLLC)slice were established.The proposed scheme can maximize the SE while ensuring Quality of Service(QoS)for the users.This study established a mapping relationship between resource allocation and the DQN algorithm in the CR-NOMA network.According to the signal-to-interference-plusnoise ratio(SINR)of the primary users(PUs),the proposed scheme can output the optimal channel selection and power allocation.The simulation results reveal that the proposed scheme can converge faster and obtain higher rewards compared with the Q-Learning scheme.Additionally,the proposed scheme has better SE than both the overlay and underlay only modes.展开更多
With the advent of 5G era,the rise of cloud services,virtual reality/virtual reality(AR/VR),vehicle networking and other technologies has put forward new requirements for the bandwidth and delay of the bearer network....With the advent of 5G era,the rise of cloud services,virtual reality/virtual reality(AR/VR),vehicle networking and other technologies has put forward new requirements for the bandwidth and delay of the bearer network.Traditional Ethernet technology cannot meet the new requirements very well.Flex Ethernet(FlexE)technology has emerged as the times require.This paper introduces the background,standardization process,functional principle,application mode and technical advantages of FlexE technology,and finally analyses its application prospects and shortcomings in 5G mobile transport network.展开更多
Network slicing is one of the most important features in 5G which enables a large variety of services with diverse performance requirements by network virtualization. Traditionally, the network can be viewed as a one-...Network slicing is one of the most important features in 5G which enables a large variety of services with diverse performance requirements by network virtualization. Traditionally, the network can be viewed as a one-size-fits-all slice and its services are bundled with proprietary hardware supported by telecom equipment providers. Now with the network virtualization technology in 5G, open networking software can be deployed flexibly on commodity hardware to offer a multi-slice network where each slice can offer a different set of network services. In this research, we propose a multi-slice 5G core architecture by provisioning its User Plane Functions (UPFs) with different QoS requirements. We compare the performance of such a multi-slice system with that of one-size-fits-all single slice architecture under the same resource assignment. Our research objective is to compare the performance of a network slicing architecture with that of a “one-size-fits-all” architecture and validate that the former can achieve better performance with the same underlying infrastructure. The results validate that our proposed system can achieve better performance by slicing one UPF into three with proper resource allocation.展开更多
To satisfy diversified service demands of vertical industries,network slicing enables efficient resource allocation of a common infrastructure by creating isolated logical networks.However,uncertainty and dynamics of ...To satisfy diversified service demands of vertical industries,network slicing enables efficient resource allocation of a common infrastructure by creating isolated logical networks.However,uncertainty and dynamics of service demands will cause performance degradation.Due to operation costs and resource constraints,it is challenging to maintain high quality of user experience while obtaining high revenue for service providers(SPs).This paper develops an optimal and fast slice reconfiguration(OFSR)framework based on reinforcement learning,where a novel scheme is proposed to offer optimal decisions for reconfiguring diverse slices.A demand prediction model is proposed to capture changes in resource requirements,based on which the OFSR scheme is triggered to determine whether to perform slice reconfiguration.Considering the large state and action spaces generated from uncertain service time and resource requirements,deep dueling architecture is adopted to improve the convergence rate.Extensive simulations validate the effectiveness of the proposed framework in achieving higher long-term revenue for SPs.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61971057).
文摘In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.
基金supported by National Key R&D Program of China(2022YFB3104200)in part by National Natural Science Foundation of China(62202386)+2 种基金in part by Basic Research Programs of Taicang(TC2021JC31)in part by Fundamental Research Funds for the Central Universities(D5000210817)in part by Xi’an Unmanned System Security and Intelligent Communications ISTC Center,and in part by Special Funds for Central Universities Construction of World-Class Universities(Disciplines)and Special Development Guidance(0639022GH0202237 and 0639022SH0201237).
文摘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.
文摘Numerous Internet of Things (IoT) devices are being connected to the net-works to offer services. To cope with a large diversity and number of IoT ser-vices, operators must meet those needs with a more flexible and efficient net-work architecture. Network slicing in 5G promises a feasible solution for this issue with network virtualization and programmability enabled by NFV (Net-work Functions Virtualization). In this research, we use virtualized IoT plat-forms as the Virtual Network Functions (VNFs) and customize network slices enabled by NFV with different QoS to support various kinds of IoT services for their best performance. We construct three different slicing systems including: 1) a single slice system, 2) a multiple customized slices system and 3) a single but scalable network slice system to support IoT services. Our objective is to compare and evaluate these three systems in terms of their throughput, aver-age response time and CPU utilization in order to identify the best system de-sign. Validated with our experiments, the performance of the multiple slicing system is better than those of the single slice systems whether it is equipped with scalability or not.
文摘With the rapid development of wireless network technologies and the growing de⁃mand for a high quality of service(QoS),the effective management of network resources has attracted a lot of attention.For example,in a practical scenario,when a network shock oc⁃curs,a batch of affected flows needs to be rerouted to respond to the network shock to bring the entire network deployment back to the optimal state,and in the process of rerouting a batch of flows,the entire response time needs to be as short as possible.Specifically,we re⁃duce the time consumed for routing by slicing,but the routing success rate after slicing is re⁃duced compared with the unsliced case.In this context,we propose a two-stage dynamic net⁃work resource allocation framework that first makes decisions on the slices to which flows are assigned,and coordinates resources among slices to ensure a comparable routing suc⁃cess rate as in the unsliced case,while taking advantage of the time efficiency gains from slicing.
文摘We investigate the design of satellite network slicing for the first time to provide customized services for the diversified applications,and propose a novel scheme for satellite end-to-end(E2E) network slicing based on 5G technology,which provides a view of common satellite network slicing and supports flexible network deployment between the satellite and the ground.Specifically,considering the limited satellite network resource and the characteristics of the satellite channel,we propose a novel satellite E2E network slicing architecture.Therein,the deployment of the network functions between the satellite and the ground is coordinately considered.Subsequently,the classification and the isolation technologies of satellite network sub-slices are proposed adaptively based on 5G technology to support resource allocation on demand.Then,we develop the management technologies for the satellite E2E network slicing including slicing key performance indicator(KPI) design,slicing deployment,and slicing management.Finally,the analysis of the challenges and future work shows the potential research in the future.
文摘With emerging large volume and diverse heterogeneity of Internet of Things (IoT) applications, the one-size-fits-all design of the current 4G networks is no longer adequate to serve various types of IoT applications. Consequently, the concepts of network slicing enabled by Network Function Virtualization (NFV) have been proposed in the upcoming 5G networks. 5G network slicing allows IoT applications of different QoS requirements to be served by different virtual networks. Moreover, these network slices are equipped with scalability that allows them to grow or shrink their instances of Virtual Network Functions (VNFs) when needed. However, all current research only focuses on scalability on a single network slice, which is the scalability at the VNF level only. Such a design will eventually reach the capacity limit of a single slice under stressful incoming traffic, and cause the breakdown of an IoT system. Therefore, we propose a new IoT scalability architecture in this research to provide scalability at the NS level and design a testbed to implement the proposed architecture in order to verify its effectiveness. For evaluation, three systems are compared for their throughput, response time, and CPU utilization under three different types of IoT traffic, including the single slice scaling system, the multiple slices scaling system and the hybrid scaling system where both single slicing and multiple slicing can be simultaneously applied. Due to the balanced tradeoff between slice scalability and resource availability, the hybrid scaling system turns out to perform the best in terms of throughput and response time with medium CPU utilization.
基金This work was supported partially by the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education(NRF5199991514504)by theMSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2023-2018-0-01431)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘5G use cases,for example enhanced mobile broadband(eMBB),massive machine-type communications(mMTC),and an ultra-reliable low latency communication(URLLC),need a network architecture capable of sustaining stringent latency and bandwidth requirements;thus,it should be extremely flexible and dynamic.Slicing enables service providers to develop various network slice architectures.As users travel from one coverage region to another area,the callmust be routed to a slice thatmeets the same or different expectations.This research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced networks.Rules of thumb which indicates the accuracy regarding the training data classification schemes within machine learning should be considered for validation and selection of the appropriate machine learning strategies.Therefore,this study discusses the network model’s design and implementation of self-optimization Fuzzy Qlearning of the decision-making algorithm for slice handover.The algorithm’s performance is assessed by means of connection-level metrics considering the Quality of Service(QoS),specifically the probability of the new call to be blocked and the probability of a handoff call being dropped.Hence,within the network model,the call admission control(AC)method is modeled by leveraging supervised learning algorithm as prior knowledge of additional capacity.Moreover,to mitigate high complexity,the integration of fuzzy logic as well as Fuzzy Q-Learning is used to discretize state and the corresponding action spaces.The results generated from our proposal surpass the traditional methods without the use of supervised learning and fuzzy-Q learning.
基金supported by the funding from the National Natural Science Foundation of China(Nos.62325203 and U22B2033)in part by the General Artificial Intelligence computing Chip project for training in 2022(No.CEIEC-2022-ZM02-0244)from Kunlunxin(Beijing)Technology Co.,LTDtin part by the BUPT Excellent Ph.D.Students Foundation(No.CX2023147).
文摘With various service types including massive machine-type communication(mMTC)and ultra-reliable low-latency communication(URLLC),fifth generation(5G)networks require advanced resources management strategies.As a method to segment network resources logically,network slicing(NS)addresses the challenges of heterogeneity and scalability prevalent in these networks.Traditional software-defined networking(SDN)technologies,lack the flexibility needed for precise control over network resources and fine-grained packet management.This has led to significant developments in programmable switches,with programming protocol-independent packet processors(P4)emerging as a transformative programming language.P4 endows network devices with flexibility and programmability,overcoming traditional SDN limitations and enabling more dynamic,precise network slicing implementations.In our work,we leverage the capabilities of P4 to forge a groundbreaking closed-loop architecture that synergizes the programmable data plane with an intelligent control plane.We set up a token bucket-based bandwidth management and traffic isolation mechanism in the data plane,and use the generative diffusion model to generate the key configuration of the strategy in the control plane.Through comprehensive experimentation,we validate the effectiveness of our architecture,underscoring its potential as a significant advancement in 5G network traffic management.
文摘The advent of 5G technology has revolutionized network communication by introducing network slicing(NS)and virtualization to allow multiple network service providers(NSPs)to share infrastructure,thereby reducing deployment costs and accelerating 5G adoption.While this new open marketplace enables NSPs to trade resources dynamically,it also exposes the system to security concerns,such as front-running and selfish-validation attacks,which can lead to market manipulation and strategy leakage.This paper presents TRADE-5G,a secure blockchainbased marketplace for 5G resource trading that mitigates these attacks and ensures fair,transparent resource allocation while preserving the cofidentiality of NSP strategies.Through extensive simulations,TRADE-5G demonstrates a substantial 18%improvement in user satisfaction and a 36%reduction in wasted resources compared to traditional models.Additionally,it opens new profit opportunities for NSPs through unused resources,establishing a more competitive,secure,and transparent 5G trading environment that exceeds the capabilities of traditional mobile networks.
基金supported by an Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(RS-2024-00438156,Development of Security Resilience Technology Based on Network Slicing Services in a 5G Specialized Network).
文摘This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.
文摘The integration of network slicing into a Device-to-Device(D2D)network is a promising technological approach for efficiently accommodating Enhanced Mobile Broadband(eMBB)and Ultra Reliable Low Latency Communication(URLLC)services.In this work,we aim to optimize energy efficiency and resource allocation in a D2D underlay cellular network by jointly optimizing beamforming and Resource Sharing Unit(RSU)selection.The problem of our investigation involves a Mixed-Integer Nonlinear Program(MINLP).To solve the problem effectively,we utilize the concept of the Dinkelbach method,the iterative weightedℓ1-norm technique,and the principles of Difference of Convex(DC)programming.To simplify the solution,we merge these methods into a two-step process using Semi-Definite Relaxation(SDR)and Successive Convex Approximation(SCA).The integration of network slicing and the optimization of short packet transmission are the proposed strategies to enhance spectral efficiency and satisfy the demand for low-latency and high-data-rate requirement applications.The Simulation results validate that the proposed method outperforms the benchmark schemes,demonstrating higher throughput ranging from 11.79%to 28.67%for URLLC users,and 13.67%to 35.89%for eMBB users,respectively.
基金supported by the National Science and Technology Major Project of China (2017ZX03001014)the National Natural Science Foundation of China for Distinguished Young Scholar (61425012)
文摘As different requirements on mobility support will be introduced by diversified communication scenarios in the fifth generation （5G）, on demand mobility management is put forward to simplify signaling process, reduce terminal power consumption, improve network efficiency and so on. In order to enable on demand mobility management in 5G networks, a mobility driven network slicing （MDNS） was proposed, which takes individual mobility support requirements into account while customizing networks for different mobile services. Within the MDNS framework, the actual levels of required mobility support are determined by a mobility description system, and network slice templates with the corresponding mobility management schemes are defined by a network slice description function. By instantiating the network slices, each mobile terminal could be directed to the network slice with the most appropriate mobility management scheme. Based on this, a prototype was implemented to validate the feasibility of MDNS framework, i.e. creating multiple network slices with different mobility management schemes. In addition, the performance evaluation on average cost of processing a mobility event is conducted for the proposed MDNS framework and the long term evolution （LTE） system, and operating benefits are analyzed including efficiency and scalability.
文摘Next-generation 6G networks seek to provide ultra-reliable and low-latency communications,necessitating network designs that are intelligent and adaptable.Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures.Nonetheless,sustaining elevated Quality of Service(QoS)in dynamic,resource-limited systems poses significant hurdles.This study introduces an innovative packet-based proactive end-to-end(ETE)resource management system that facilitates network slicing with improved resilience and proactivity.To get around the drawbacks of conventional reactive systems,we develop a cost-efficient slice provisioning architecture that takes into account limits on radio,processing,and transmission resources.The optimization issue is non-convex,NP-hard,and requires online resolution in a dynamic setting.We offer a hybrid solution that integrates an advanced Deep Reinforcement Learning(DRL)methodology with an Improved Manta-Ray Foraging Optimization(ImpMRFO)algorithm.The ImpMRFO utilizes Chebyshev chaotic mapping for the formation of a varied starting population and incorporates Lévy flight-based stochastic movement to avert premature convergence,hence facilitating improved exploration-exploitation trade-offs.The DRL model perpetually acquires optimum provisioning strategies via agent-environment interactions,whereas the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.The DRL model perpetually acquires optimum provisioning methods via agent-environment interactions,while the ImpMRFO enhances policy performance for effective slice provisioning.The solution,developed in Python,is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements.Experimental findings reveal that the proactive ETE system outperforms DRL models and non-resilient provisioning techniques.Our technique increases PSSRr,decreases average latency,and optimizes resource use.These results demonstrate that the hybrid architecture for robust,real-time,and scalable slice management in future 6G networks is feasible.
基金This work was supported by the National Key R&D Program of China under Grant 2022YFB2902201.
文摘Network slicing has gained popularity as a result of the advances in the fifth generation(5G)mobile network.Network slicing facilitates the support of different service types with varying requirements,which brings into light the slicing-aware next generation mobile network architecture.While allowing resource sharing among multiple stakeholders,there is a long list of administrative negotiations among parties that have not established mutual trust.Distributed ledger technology may be a solution to mitigate the above issues by taking its decentralized yet immutable and auditable ledger,which may help to ease administrative negotiations and build mutual trust among multi-stakeholders.There have been many research interests in this direction which focus on handling various problems in network slicing.This paper aims at constructing this area of knowledge by introducing network slice from a standardization point of view to start with,and presenting security,privacy,and trust challenges of network slicing in 5G and beyond networks.Furthermore,this paper covers distributed ledger technologies basics and related approaches that tackle security,privacy,and trust threats in network slicing for 5G and beyond networks.The various proposals proposed in the literature are compared and presented.Lastly,limitations of current work and open challenges are illustrated as well.
文摘With the acceleration of the intelligent transformation of power systems,the requirements for communication technology are increasingly stringent.The application of 5G mobile communication technology in power communication is analyzed.In this study,5G technology features,application principles,and practical strategies are discussed,and methods such as network slicing,customized deployment,edge computing collaborative application,communication equipment integration and upgrading,and multi-technology collaboration and complementation are proposed.It aims to effectively improve the efficiency,reliability,and security of power communication,solve the problem that traditional communication technology is difficult to meet the diversified needs of power business,and achieve the effect of optimizing the power communication network and supporting the intelligent development of the power system.
基金supported by the National Key Research and Development Program of China(No.2020YFB1807700).
文摘As an indispensable component of the emerging 6G networks,Space-Air-Ground Inte-grated Networks(SAGINs)are envisioned to provide ubiquitous network connectivity and services by integrating satellite networks,aerial networks,and terrestrial networks.In 6G SAGINs,a wide variety of network services with the features of diverse requirements,complex mobility,and multi-dimensional resources will pose great challenges to service provisioning,which urges the develop-ment of service-oriented SAGINs.In this paper,we conduct a comprehensive review of 6G SAGINs from a new perspective of service-oriented network.First,we present the requirements of service-oriented networks,and then propose a service-oriented SAGINs management architec-ture.Two categories of critical technologies are presented and discussed,i.e.,heterogeneous resource orchestration technologies and the cloud-edge synergy technologies,which facilitate the interoperability of different network segments and cooperatively orchestrate heterogeneous resources across different domains,according to the service features and requirements.In addition,the potential future research directions are also presented and discussed.
基金the National Natural Science Foundation of China(Grant No.61971057).
文摘To solve the contradiction between limited spectrum resources and increasing communication demand,this paper proposes a wireless resource allocation scheme based on the Deep Q Network(DQN)to allocate radio resources in a downlink multi-user cognitive radio(CR)network with slicing.Secondary users(SUs)are multiplexed using non-orthogonal multiple access(NOMA).The SUs use the hybrid spectrum access mode to improve the spectral efficiency(SE).Considering the demand for multiple services,the enhanced mobile broadband(eMBB)slice and ultrareliable low-latency communication(URLLC)slice were established.The proposed scheme can maximize the SE while ensuring Quality of Service(QoS)for the users.This study established a mapping relationship between resource allocation and the DQN algorithm in the CR-NOMA network.According to the signal-to-interference-plusnoise ratio(SINR)of the primary users(PUs),the proposed scheme can output the optimal channel selection and power allocation.The simulation results reveal that the proposed scheme can converge faster and obtain higher rewards compared with the Q-Learning scheme.Additionally,the proposed scheme has better SE than both the overlay and underlay only modes.
文摘With the advent of 5G era,the rise of cloud services,virtual reality/virtual reality(AR/VR),vehicle networking and other technologies has put forward new requirements for the bandwidth and delay of the bearer network.Traditional Ethernet technology cannot meet the new requirements very well.Flex Ethernet(FlexE)technology has emerged as the times require.This paper introduces the background,standardization process,functional principle,application mode and technical advantages of FlexE technology,and finally analyses its application prospects and shortcomings in 5G mobile transport network.
文摘Network slicing is one of the most important features in 5G which enables a large variety of services with diverse performance requirements by network virtualization. Traditionally, the network can be viewed as a one-size-fits-all slice and its services are bundled with proprietary hardware supported by telecom equipment providers. Now with the network virtualization technology in 5G, open networking software can be deployed flexibly on commodity hardware to offer a multi-slice network where each slice can offer a different set of network services. In this research, we propose a multi-slice 5G core architecture by provisioning its User Plane Functions (UPFs) with different QoS requirements. We compare the performance of such a multi-slice system with that of one-size-fits-all single slice architecture under the same resource assignment. Our research objective is to compare the performance of a network slicing architecture with that of a “one-size-fits-all” architecture and validate that the former can achieve better performance with the same underlying infrastructure. The results validate that our proposed system can achieve better performance by slicing one UPF into three with proper resource allocation.
基金This work is supported by National Key R&D Program of China(2019YFB1803304)the National Natural Science Foundation of China(62101031)+3 种基金Beijing Natural Science Foundation(L212004),111 Project(No.B170003)the Fundamental Research Funds for the Central Universities(FRF-TP-19-002C1,FRF-TP-19-051A1,RC1631)Beijing Top Discipline for Artificial Intelligent Science and Engineering,University of Science and Technology Beijingthe Open Research Project of the State Key Laboratory of Media Convergence and Communication,Communication University of China,China(No.SKLMCC2020KF010).
文摘To satisfy diversified service demands of vertical industries,network slicing enables efficient resource allocation of a common infrastructure by creating isolated logical networks.However,uncertainty and dynamics of service demands will cause performance degradation.Due to operation costs and resource constraints,it is challenging to maintain high quality of user experience while obtaining high revenue for service providers(SPs).This paper develops an optimal and fast slice reconfiguration(OFSR)framework based on reinforcement learning,where a novel scheme is proposed to offer optimal decisions for reconfiguring diverse slices.A demand prediction model is proposed to capture changes in resource requirements,based on which the OFSR scheme is triggered to determine whether to perform slice reconfiguration.Considering the large state and action spaces generated from uncertain service time and resource requirements,deep dueling architecture is adopted to improve the convergence rate.Extensive simulations validate the effectiveness of the proposed framework in achieving higher long-term revenue for SPs.
