TISPAN,from a fixed access perspective,proposes Resource and Admission Control Subsystem[0](RACS) as a solution to Quality of Service(QoS) problem for NGN bearer network.In contrast,3GPP has an approach to this from t...TISPAN,from a fixed access perspective,proposes Resource and Admission Control Subsystem[0](RACS) as a solution to Quality of Service(QoS) problem for NGN bearer network.In contrast,3GPP has an approach to this from the perspective of mobile access.In the latest 3GPP R7 draft,integration of Policy Control Function(PCF) with Flow Based Charging(FBC) function of the R6 brought forward policy control and charging.With the development of fixed mobile convergence,the inconsistence in architectures and interfaces of different resource and admission control[0] solutions will have a huge impact on manufacture and network implementation of NGN related equipment.To solve this problem,both 3GPP and TISPAN have been working on the convergence of Gq'/Rx reference points.Harmonized Policy Control and Charging(PCC) proposed by the Next Generation Mobile Network(NGMN) forum,i.e.cooperative resource control architecture for heterogeneous networks,represents an evolutional sign post for resource control technology for heterogeneous network architecture.展开更多
To identify industrial control equipment is often a key step in network mapping,categorizing network resources,and attack defense.For example,if vulnerable equipment or devices can be discovered in advance and the att...To identify industrial control equipment is often a key step in network mapping,categorizing network resources,and attack defense.For example,if vulnerable equipment or devices can be discovered in advance and the attack path canbe cut off,security threats canbe effectively avoided and the stable operationof the Internet canbe ensured.The existing rule-matching method for equipment identification has limitations such as relying on experience and low scalability.This paper proposes an industrial control device identification method based on PCA-Adaboost,which integrates rule matching and machine learning.We first build a rule base from network data collection and then use single andmulti-protocol rule-matchingmethods to identify the type of industrial control devices.Finally,we utilize PCA-Adaboost to identify unlabeled data.The experimental results show that the recognition rate of this method is better than that of the traditional Nmap device recognitionmethod and the device recognition accuracy rate reaches 99%.The evaluation effect of the test data set is significantly enhanced.展开更多
Connected autonomous vehicles(CAVs)are a promising paradigm for implementing intelligent transportation systems.However,in CAVs scenarios,the sensing blind areas cause serious safety hazards.Existing vehicle-to-vehicl...Connected autonomous vehicles(CAVs)are a promising paradigm for implementing intelligent transportation systems.However,in CAVs scenarios,the sensing blind areas cause serious safety hazards.Existing vehicle-to-vehicle(V2V)technology is difficult to break through the sensing blind area and ensure reliable sensing information.To overcome these problems,considering infrastructures as a means to extend the sensing range is feasible based on the integrated sensing and communication(ISAC)technology.The mmWave base station(mmBS)transmits multiple beams consisting of communication beams and sensing beams.The sensing beams are responsible for sensing objects within the CAVs blind area,while the communication beams are responsible for transmitting the sensed information to the CAVs.To reduce the impact of inter-beam interference,a joint multiple beamwidth and power allocation(JMBPA)algorithm is proposed.By maximizing the communication transmission rate under the sensing constraints.The proposed non-convex optimization problem is transformed into a standard difference of two convex functions(D.C.)problem.Finally,the superiority of the lutions.The average transmission rate of communication beams remains over 3.4 Gbps,showcasing a significant improvement compared to other algorithms.Moreover,the satisfaction of sensing services remains steady.展开更多
Multimedia broadcast multicast service(MBMS)with inherently low requirement for network resources has been proposed as a candidate solution for using such resources in a more efficient manner.On the other hand,the Nex...Multimedia broadcast multicast service(MBMS)with inherently low requirement for network resources has been proposed as a candidate solution for using such resources in a more efficient manner.On the other hand,the Next Generation Mobile Network(NGMN)combines multiple radio access technologies(RATs)to optimize overall network performance.Handover performance is becoming a vital indicator of the quality experience of mobile user equipment(UE).In contrast to the conventional vertical handover issue,the problem we are facing is how to seamlessly transmit broadcast/multicast sessions among heterogeneous networks.In this paper,we propose a new network entity,media independent broadcast multicast service center(MIBM-SC),to provide seamless handover for broadcast/multicast sessions over heterogeneous networks,by extensions and enhancements of MBMS and media independent information service(MIIS)architectures.Additionally,a network selection scheme and a cell transmission mode selection scheme are proposed for selecting the best target network and best transmission mode.Both schemes are based on a load-aware network capacity estimation algorithm.Simulation results show that the proposed approach has the capability to provide MBMS over heterogeneous networks,with improved handover performance in terms of packet loss rate,throughput,handover delay,cell load,bandwidth usage,and the peak signal-to-noise ratio(PSNR).展开更多
Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service(QoS)in the 5th Generation(5G)-beyond and the 6th Generation(6G)networks.However,effective slicing of Radio...Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service(QoS)in the 5th Generation(5G)-beyond and the 6th Generation(6G)networks.However,effective slicing of Radio Access Network(RAN)is very challenging due to the diverse QoS requirements and dynamic conditions in the 6G networks.In this paper,we propose a self-sustained RAN slicing framework,which integrates the self-management of network resources with multiple granularities,the self-optimization of slicing control performance,and self-learning together to achieve an adaptive control strategy under unforeseen network conditions.The proposed RAN slicing framework is hierarchically structured,which decomposes the RAN slicing control into three levels,i.e.,network-level slicing,next generation NodeB(gNodeB)-level slicing,and packet scheduling level slicing.At the network level,network resources are assigned to each gNodeB at a large timescale with coarse resource granularity.At the gNodeB-level,each gNodeB adjusts the configuration of each slice in the cell at the large timescale.At the packet scheduling level,each gNodeB allocates radio resource allocation among users in each network slice at a small timescale.Furthermore,we utilize the transfer learning approach to enable the transition from a model-based control to an autonomic and self-learning RAN slicing control.With the proposed RAN slicing framework,the QoS performance of emerging services is expected to be dramatically enhanced.展开更多
文摘TISPAN,from a fixed access perspective,proposes Resource and Admission Control Subsystem[0](RACS) as a solution to Quality of Service(QoS) problem for NGN bearer network.In contrast,3GPP has an approach to this from the perspective of mobile access.In the latest 3GPP R7 draft,integration of Policy Control Function(PCF) with Flow Based Charging(FBC) function of the R6 brought forward policy control and charging.With the development of fixed mobile convergence,the inconsistence in architectures and interfaces of different resource and admission control[0] solutions will have a huge impact on manufacture and network implementation of NGN related equipment.To solve this problem,both 3GPP and TISPAN have been working on the convergence of Gq'/Rx reference points.Harmonized Policy Control and Charging(PCC) proposed by the Next Generation Mobile Network(NGMN) forum,i.e.cooperative resource control architecture for heterogeneous networks,represents an evolutional sign post for resource control technology for heterogeneous network architecture.
基金funded in part by the National Key R&D Program of China(Grant No.2022YFB3102901)the National Natural Science Foundation of China(Grant Nos.61976064,61871140,62272119,62072130)the Guangdong Province Key Research and Development Plan(Grant No.2019B010137004).
文摘To identify industrial control equipment is often a key step in network mapping,categorizing network resources,and attack defense.For example,if vulnerable equipment or devices can be discovered in advance and the attack path canbe cut off,security threats canbe effectively avoided and the stable operationof the Internet canbe ensured.The existing rule-matching method for equipment identification has limitations such as relying on experience and low scalability.This paper proposes an industrial control device identification method based on PCA-Adaboost,which integrates rule matching and machine learning.We first build a rule base from network data collection and then use single andmulti-protocol rule-matchingmethods to identify the type of industrial control devices.Finally,we utilize PCA-Adaboost to identify unlabeled data.The experimental results show that the recognition rate of this method is better than that of the traditional Nmap device recognitionmethod and the device recognition accuracy rate reaches 99%.The evaluation effect of the test data set is significantly enhanced.
基金China Tele-com Research Institute Project(Grants No.HQBYG2200147GGN00)National Key R&D Program of China(2020YFB1807600)National Natural Science Foundation of China(NSFC)(Grant No.62022020).
文摘Connected autonomous vehicles(CAVs)are a promising paradigm for implementing intelligent transportation systems.However,in CAVs scenarios,the sensing blind areas cause serious safety hazards.Existing vehicle-to-vehicle(V2V)technology is difficult to break through the sensing blind area and ensure reliable sensing information.To overcome these problems,considering infrastructures as a means to extend the sensing range is feasible based on the integrated sensing and communication(ISAC)technology.The mmWave base station(mmBS)transmits multiple beams consisting of communication beams and sensing beams.The sensing beams are responsible for sensing objects within the CAVs blind area,while the communication beams are responsible for transmitting the sensed information to the CAVs.To reduce the impact of inter-beam interference,a joint multiple beamwidth and power allocation(JMBPA)algorithm is proposed.By maximizing the communication transmission rate under the sensing constraints.The proposed non-convex optimization problem is transformed into a standard difference of two convex functions(D.C.)problem.Finally,the superiority of the lutions.The average transmission rate of communication beams remains over 3.4 Gbps,showcasing a significant improvement compared to other algorithms.Moreover,the satisfaction of sensing services remains steady.
基金Project supported by the Ministry of Science,Technology and Innovation of Malaysia under the eScienceFund(No.01-01-03-SF0782)
文摘Multimedia broadcast multicast service(MBMS)with inherently low requirement for network resources has been proposed as a candidate solution for using such resources in a more efficient manner.On the other hand,the Next Generation Mobile Network(NGMN)combines multiple radio access technologies(RATs)to optimize overall network performance.Handover performance is becoming a vital indicator of the quality experience of mobile user equipment(UE).In contrast to the conventional vertical handover issue,the problem we are facing is how to seamlessly transmit broadcast/multicast sessions among heterogeneous networks.In this paper,we propose a new network entity,media independent broadcast multicast service center(MIBM-SC),to provide seamless handover for broadcast/multicast sessions over heterogeneous networks,by extensions and enhancements of MBMS and media independent information service(MIIS)architectures.Additionally,a network selection scheme and a cell transmission mode selection scheme are proposed for selecting the best target network and best transmission mode.Both schemes are based on a load-aware network capacity estimation algorithm.Simulation results show that the proposed approach has the capability to provide MBMS over heterogeneous networks,with improved handover performance in terms of packet loss rate,throughput,handover delay,cell load,bandwidth usage,and the peak signal-to-noise ratio(PSNR).
文摘Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service(QoS)in the 5th Generation(5G)-beyond and the 6th Generation(6G)networks.However,effective slicing of Radio Access Network(RAN)is very challenging due to the diverse QoS requirements and dynamic conditions in the 6G networks.In this paper,we propose a self-sustained RAN slicing framework,which integrates the self-management of network resources with multiple granularities,the self-optimization of slicing control performance,and self-learning together to achieve an adaptive control strategy under unforeseen network conditions.The proposed RAN slicing framework is hierarchically structured,which decomposes the RAN slicing control into three levels,i.e.,network-level slicing,next generation NodeB(gNodeB)-level slicing,and packet scheduling level slicing.At the network level,network resources are assigned to each gNodeB at a large timescale with coarse resource granularity.At the gNodeB-level,each gNodeB adjusts the configuration of each slice in the cell at the large timescale.At the packet scheduling level,each gNodeB allocates radio resource allocation among users in each network slice at a small timescale.Furthermore,we utilize the transfer learning approach to enable the transition from a model-based control to an autonomic and self-learning RAN slicing control.With the proposed RAN slicing framework,the QoS performance of emerging services is expected to be dramatically enhanced.
