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
Wireless transmission method in wireless sensor networks has put forward higher requirements for private protection technology. According to the packet loss problem of private protection algorithm based on slice techn...Wireless transmission method in wireless sensor networks has put forward higher requirements for private protection technology. According to the packet loss problem of private protection algorithm based on slice technology, this paper proposes the data private protection algorithm with redundancy mechanism, which ensures privacy by privacy homomorphism mechanism and guarantees redundancy by carrying hidden data. Moreover,it selects the routing tree generated by CTP(Collection Tree Protocol) as routing path for data transmission. By dividing at the source node, it adds the hidden information and also the privacy homomorphism. At the same time,the information feedback tree is established between the destination node and the source node. In addition, the destination node immediately sends the packet loss information and the encryption key via the information feedback tree to the source node. As a result,it improves the reliability and privacy of data transmission and ensures the data redundancy.展开更多
In the 5th generation(5G)wireless communication networks,network slicing emerges where network operators(NPs)form isolated logical slices by the same cellular network infrastructure and spectrum resource.In coverage r...In the 5th generation(5G)wireless communication networks,network slicing emerges where network operators(NPs)form isolated logical slices by the same cellular network infrastructure and spectrum resource.In coverage regions of access points(APs)shared by slices,device to device(D2D)communication can occur among different slices,i.e.,one device acts as D2D relay for another device serving by a different slice,which is defined as slice cooperation in this paper.Since selfish slices will not help other slices by cooperation voluntarily and unconditionally,this paper designs a novel resource allocation scheme to stimulate slice cooperation.The main idea is to encourage slice to perform cooperation for other slices by rewarding it with higher throughput.The proposed incentive scheme for slice cooperation is formulated by an optimal problem,where cooperative activities are introduced to the objective function.Since optimal solutions of the formulated problem are long term statistics,though can be obtained,a practical online slice scheduling algorithm is designed,which can obtain optimal solutions of the formulated maximal problem.Lastly,the throughput isolation indexes are defined to evaluate isolation performance of slice.According to simulation results,the proposed incentive scheme for slice cooperation can stimulate slice cooperation effectively,and the isolation of slice is also simulated and discussed.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
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
Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-toler...Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-tolerant (SDT) traffic, we design a dynamic switching strategy based on a traffic-Qo S-aware soft slicing (TQASS) scheme and a resource-efficiency-aware soft slicing (REASS) scheme.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 past decade has seen a growing interest in ocean sensor networks because of their wide applications in marine research,oceanography,ocean monitoring,offshore exploration,and defense or homeland security.Ocean sens...The past decade has seen a growing interest in ocean sensor networks because of their wide applications in marine research,oceanography,ocean monitoring,offshore exploration,and defense or homeland security.Ocean sensor networks are generally formed with various ocean sensors,autonomous underwater vehicles,surface stations,and research vessels.To make ocean sensor network applications viable,efficient communication among all devices and components is crucial.Due to the unique characteristics of underwater acoustic channels and the complex deployment environment in three dimensional(3D) ocean spaces,new efficient and reliable communication and networking protocols are needed in design of ocean sensor networks.In this paper,we aim to provide an overview of the most recent advances in network design principles for 3D ocean sensor networks,with focuses on deployment,localization,topology design,and position-based routing in 3D ocean spaces.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
Software-Defined Network(SDN)empowers the evolution of Internet with the OpenFlow,Network Virtualization and Service Slicing strategies.With the fast increasing requirements of Mobile Internet services,the Internet an...Software-Defined Network(SDN)empowers the evolution of Internet with the OpenFlow,Network Virtualization and Service Slicing strategies.With the fast increasing requirements of Mobile Internet services,the Internet and Mobile Networks go to the convergence.Mobile Networks can also get benefits from the SDN evolution to fulfill the 5th Generation(5G)capacity booming.The article implements SDN into Frameless Network Architecture(FNA)for 5G Mobile Network evolution with proposed Mobile-oriented OpenFlow Protocol(MOFP).The Control Plane/User Plane(CP/UP)separation and adaptation strategy is proposed to support the User-Centric scenario in FNA.The traditional Base Station is separated with Central Processing Entity(CPE)and Antenna Element(AE)to perform the OpenFlow and Network Virtualization.The AEs are released as new resources for serving users.The mobile-oriented Service Slicing with different Quality of Service(QoS)classification is proposed and Resource Pooling based Virtualized Radio Resource Management(VRRM)is optimized for the Service Slicing strategy with resource-limited feature in Mobile Networks.The capacity gains are provided to show the merits of SDN based FNA.And the MiniNet based Trial Network with Service Slicing is implemented with experimental results.展开更多
Simultaneous multisite recording using multi-electrode arrays（MEAs） in cultured and acutely-dissociated brain slices and other tissues is an emerging technique in the field of network electrophysiology.Over the past...Simultaneous multisite recording using multi-electrode arrays（MEAs） in cultured and acutely-dissociated brain slices and other tissues is an emerging technique in the field of network electrophysiology.Over the past 40 years,great efforts have been made by both scientists and commercial concerns,to advance this technique.The MEA technique has been widely applied to many regions of the brain,retina,heart and smooth muscle in various studies at the network level.The present review starts from the development of MEA techniques and their uses in brain preparations,and then specifically concentrates on the use of MEA recordings in studies of synaptic plasticity at the network level in both the temporal and spatial domains.Because the MEA technique helps bridge the gap between single-cell recordings and behavioral assays,its wide application will undoubtedly shed light on the mechanisms underlying brain functions and dysfunctions at the network level that remained largely unknown due to the technical difficulties before it matured.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
Since virtualization technology enables the abstraction and sharing of resources in a flexible management way, the overall expenses of network deployment can be significantly reduced. Therefore, the technology has bee...Since virtualization technology enables the abstraction and sharing of resources in a flexible management way, the overall expenses of network deployment can be significantly reduced. Therefore, the technology has been widely applied in the core network. With the tremendous growth in mobile traffic and services, it is natural to extend virtualization technology to the cloud computing based radio access networks(CCRANs) for achieving high spectral efficiency with low cost.In this paper, the virtualization technologies in CC-RANs are surveyed, including the system architecture, key enabling techniques, challenges, and open issues. The enabling key technologies for virtualization in CC-RANs mainly including virtual resource allocation, radio access network(RAN) slicing, mobility management, and social-awareness have been comprehensively surveyed to satisfy the isolation, customization and high-efficiency utilization of radio resources. The challenges and open issues mainly focus on virtualization levels for CC-RANs, signaling design for CC-RAN virtualization, performance analysis for CC-RAN virtualization, and network security for virtualized CC-RANs.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
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
The electrical excitability of neural networks is influenced by different environmental factors. Effective and simple methods are required to objectively and quantitatively evaluate the influence of such factors, incl...The electrical excitability of neural networks is influenced by different environmental factors. Effective and simple methods are required to objectively and quantitatively evaluate the influence of such factors, including variations in temperature and pharmaceutical dosage. The aim of this paper was to introduce ‘the voltage threshold measurement method＇, which is a new method using microelectrode arrays that can quantitatively evaluate the influence of different factors on the electrical excitability of neural networks. We sought to verify the feasibility and efficacy of the method by studying the effects of acetylcholine, ethanol, and temperature on hippocampal neuronal networks and hippocampal brain slices. First, we determined the voltage of the stimulation pulse signal that elicited action potentials in the two types of neural networks under normal conditions. Second, we obtained the voltage thresholds for the two types of neural networks under different concentrations of acetylcholine, ethanol, and different temperatures. Finally, we obtained the relationship between voltage threshold and the three influential factors. Our results indicated that the normal voltage thresholds of the hippocampal neuronal network and hippocampal slice preparation were 56 and 31 m V, respectively. The voltage thresholds of the two types of neural networks were inversely proportional to acetylcholine concentration, and had an exponential dependency on ethanol concentration. The curves of the voltage threshold and the temperature of the medium for the two types of neural networks were U-shaped. The hippocampal neuronal network and hippocampal slice preparations lost their excitability when the temperature of the medium decreased below 34 and 33°C or increased above 42 and 43°C, respectively. These results demonstrate that the voltage threshold measurement method is effective and simple for examining the performance/excitability of neuronal networks.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
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sponsored by the National Key R&D Program of China(No.2018YFB1003201)the National Natural Science Foundation of China(No.61672296,No.61602261)Major Natural Science Research Projects in Colleges and Universities of Jiangsu Province(No.18KJA520008)
文摘Wireless transmission method in wireless sensor networks has put forward higher requirements for private protection technology. According to the packet loss problem of private protection algorithm based on slice technology, this paper proposes the data private protection algorithm with redundancy mechanism, which ensures privacy by privacy homomorphism mechanism and guarantees redundancy by carrying hidden data. Moreover,it selects the routing tree generated by CTP(Collection Tree Protocol) as routing path for data transmission. By dividing at the source node, it adds the hidden information and also the privacy homomorphism. At the same time,the information feedback tree is established between the destination node and the source node. In addition, the destination node immediately sends the packet loss information and the encryption key via the information feedback tree to the source node. As a result,it improves the reliability and privacy of data transmission and ensures the data redundancy.
基金supported by Beijing Natural Science Foundation under Grant number L172049the National Science and CAS Engineering Laboratory for Intelligent Agricultural Machinery Equipment GC201907-02
文摘In the 5th generation(5G)wireless communication networks,network slicing emerges where network operators(NPs)form isolated logical slices by the same cellular network infrastructure and spectrum resource.In coverage regions of access points(APs)shared by slices,device to device(D2D)communication can occur among different slices,i.e.,one device acts as D2D relay for another device serving by a different slice,which is defined as slice cooperation in this paper.Since selfish slices will not help other slices by cooperation voluntarily and unconditionally,this paper designs a novel resource allocation scheme to stimulate slice cooperation.The main idea is to encourage slice to perform cooperation for other slices by rewarding it with higher throughput.The proposed incentive scheme for slice cooperation is formulated by an optimal problem,where cooperative activities are introduced to the objective function.Since optimal solutions of the formulated problem are long term statistics,though can be obtained,a practical online slice scheduling algorithm is designed,which can obtain optimal solutions of the formulated maximal problem.Lastly,the throughput isolation indexes are defined to evaluate isolation performance of slice.According to simulation results,the proposed incentive scheme for slice cooperation can stimulate slice cooperation effectively,and the isolation of slice is also simulated and discussed.
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.
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 Liaoning Revitalization Talents Program(XLYC2203148)
文摘Dear Editor,This letter proposes a dynamic switching soft slicing strategy for industrial mixed traffic in 5G networks. Considering two types of traffic, periodic delay-sensitive (PDS) traffic and sporadic delay-tolerant (SDT) traffic, we design a dynamic switching strategy based on a traffic-Qo S-aware soft slicing (TQASS) scheme and a resource-efficiency-aware soft slicing (REASS) scheme.
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.
基金Y. Wang was supported in part by the US National Science Foundation (NSF) under Grant Nos.CNS-0721666,CNS-0915331,and CNS-1050398Y. Liu was partially supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61074092+1 种基金by the Shandong Provincial Natural Science Foundation,China under Grant No.Q2008E01Z. Guo was partially supported by the NSFC under Grant Nos. 61170258 and 6093301
文摘The past decade has seen a growing interest in ocean sensor networks because of their wide applications in marine research,oceanography,ocean monitoring,offshore exploration,and defense or homeland security.Ocean sensor networks are generally formed with various ocean sensors,autonomous underwater vehicles,surface stations,and research vessels.To make ocean sensor network applications viable,efficient communication among all devices and components is crucial.Due to the unique characteristics of underwater acoustic channels and the complex deployment environment in three dimensional(3D) ocean spaces,new efficient and reliable communication and networking protocols are needed in design of ocean sensor networks.In this paper,we aim to provide an overview of the most recent advances in network design principles for 3D ocean sensor networks,with focuses on deployment,localization,topology design,and position-based routing in 3D ocean spaces.
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This material is supported by the National Natural Science Foundation of China under Grant No.61001116 and 61121001Beijing Nova Programme No.Z131101000413030+1 种基金the National Major Project No.2013ZX03003002Program for Changjiang Scholars and Innovative Research Team in University No.IRT1049
文摘Software-Defined Network(SDN)empowers the evolution of Internet with the OpenFlow,Network Virtualization and Service Slicing strategies.With the fast increasing requirements of Mobile Internet services,the Internet and Mobile Networks go to the convergence.Mobile Networks can also get benefits from the SDN evolution to fulfill the 5th Generation(5G)capacity booming.The article implements SDN into Frameless Network Architecture(FNA)for 5G Mobile Network evolution with proposed Mobile-oriented OpenFlow Protocol(MOFP).The Control Plane/User Plane(CP/UP)separation and adaptation strategy is proposed to support the User-Centric scenario in FNA.The traditional Base Station is separated with Central Processing Entity(CPE)and Antenna Element(AE)to perform the OpenFlow and Network Virtualization.The AEs are released as new resources for serving users.The mobile-oriented Service Slicing with different Quality of Service(QoS)classification is proposed and Resource Pooling based Virtualized Radio Resource Management(VRRM)is optimized for the Service Slicing strategy with resource-limited feature in Mobile Networks.The capacity gains are provided to show the merits of SDN based FNA.And the MiniNet based Trial Network with Service Slicing is implemented with experimental results.
基金supported by grants from the National Natural Science Foundation of China(30770668,81070899,81171049)973 program (2011CB504100) to J.C
文摘Simultaneous multisite recording using multi-electrode arrays（MEAs） in cultured and acutely-dissociated brain slices and other tissues is an emerging technique in the field of network electrophysiology.Over the past 40 years,great efforts have been made by both scientists and commercial concerns,to advance this technique.The MEA technique has been widely applied to many regions of the brain,retina,heart and smooth muscle in various studies at the network level.The present review starts from the development of MEA techniques and their uses in brain preparations,and then specifically concentrates on the use of MEA recordings in studies of synaptic plasticity at the network level in both the temporal and spatial domains.Because the MEA technique helps bridge the gap between single-cell recordings and behavioral assays,its wide application will undoubtedly shed light on the mechanisms underlying brain functions and dysfunctions at the network level that remained largely unknown due to the technical difficulties before it matured.
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Since virtualization technology enables the abstraction and sharing of resources in a flexible management way, the overall expenses of network deployment can be significantly reduced. Therefore, the technology has been widely applied in the core network. With the tremendous growth in mobile traffic and services, it is natural to extend virtualization technology to the cloud computing based radio access networks(CCRANs) for achieving high spectral efficiency with low cost.In this paper, the virtualization technologies in CC-RANs are surveyed, including the system architecture, key enabling techniques, challenges, and open issues. The enabling key technologies for virtualization in CC-RANs mainly including virtual resource allocation, radio access network(RAN) slicing, mobility management, and social-awareness have been comprehensively surveyed to satisfy the isolation, customization and high-efficiency utilization of radio resources. The challenges and open issues mainly focus on virtualization levels for CC-RANs, signaling design for CC-RAN virtualization, performance analysis for CC-RAN virtualization, and network security for virtualized CC-RANs.
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.
基金supported by the National Natural Sciences Foundation of China,No.61534003,61076118the Innovation Foundation for State Key Laboratory of the Ministry of Science and Technology,China,No.2016-2018a grant from the Open Projects of Key Laboratory of Child Development and Learning of the Ministry of Education of China,No.CDLS201205
文摘The electrical excitability of neural networks is influenced by different environmental factors. Effective and simple methods are required to objectively and quantitatively evaluate the influence of such factors, including variations in temperature and pharmaceutical dosage. The aim of this paper was to introduce ‘the voltage threshold measurement method＇, which is a new method using microelectrode arrays that can quantitatively evaluate the influence of different factors on the electrical excitability of neural networks. We sought to verify the feasibility and efficacy of the method by studying the effects of acetylcholine, ethanol, and temperature on hippocampal neuronal networks and hippocampal brain slices. First, we determined the voltage of the stimulation pulse signal that elicited action potentials in the two types of neural networks under normal conditions. Second, we obtained the voltage thresholds for the two types of neural networks under different concentrations of acetylcholine, ethanol, and different temperatures. Finally, we obtained the relationship between voltage threshold and the three influential factors. Our results indicated that the normal voltage thresholds of the hippocampal neuronal network and hippocampal slice preparation were 56 and 31 m V, respectively. The voltage thresholds of the two types of neural networks were inversely proportional to acetylcholine concentration, and had an exponential dependency on ethanol concentration. The curves of the voltage threshold and the temperature of the medium for the two types of neural networks were U-shaped. The hippocampal neuronal network and hippocampal slice preparations lost their excitability when the temperature of the medium decreased below 34 and 33°C or increased above 42 and 43°C, respectively. These results demonstrate that the voltage threshold measurement method is effective and simple for examining the performance/excitability of neuronal networks.
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.
