The Internet of Things(IoTs)has become an essential component of the 5th Generation(5G)network and beyond,accelerating the transition to digital society.The increasing signaling traffic generated by billions of IoT de...The Internet of Things(IoTs)has become an essential component of the 5th Generation(5G)network and beyond,accelerating the transition to digital society.The increasing signaling traffic generated by billions of IoT devices has placed significant strain on the 5G Core network(5GC)control plane.To address this issue,the 3rd Gener-ation Partnership Project(3GPP)first proposed a Service-Based Architecture(SBA),intending to create a flexible,scalable,and agile cloud-native 5GC.However,considering the coupling of protocol states and functions,there are still many challenges to fully utilize the benefits of the cloud computing and orchestrate the 5GC in a cloud-native manner.We propose a Message-Level StateLess Design(ML-SLD)to provide a cloud-native 5GC from an architectural standpoint in this paper.Firstly,we propose an innovative mechanism for servitization of the N2 interface to maintain the connection between Radio Access Network(RAN)and the 5GC,avoiding interruptions and dropouts of large-scale user data.Furthermore,we propose an On-demand Message Forwarding(OMF)al-gorithm to reduce the impact of cloud fluctuations on the performance of cloud-native 5GC.Finally,we create a prototype that is based on the OpenAirInterface(OAI)5G core network projects,with all Network Functions(NFs)packaged in dockers and deployed in a kubernetes-based cloud environment.Several experiments have been built with UERANSIM and Chaosblade simulation tools.The findings demonstrate the viability and efficiency of our proposed methods.展开更多
Artificial intelligence(AI)has made a profound impact on our daily life.The 6 th generation mobile networks(6G)should be designed to enable AI services.The native intelligence is introduced as an important feature in ...Artificial intelligence(AI)has made a profound impact on our daily life.The 6 th generation mobile networks(6G)should be designed to enable AI services.The native intelligence is introduced as an important feature in 6G.6G native AI network is realized by the philosophy of federated learning(FL)to ensure data security and privacy.Federated learning over wireless communication networks is treated as a potential solution to realize native AI.However,introducing FL in the 6G will lead to expansive communication cost and unstable FL convergence with unreliable air interface.In this paper,we propose a solution for FL over wireless networks and analyze the training efficiency.To make full use of the advantages of the proposed network,we introduce a communication-FL joint optimization(CFJO)algorithm by jointly considering the effects of uplink resource,energy consumption and latency constraints.CFJO derives a transmission strategy with resource allocation and retransmissions to reduce the wireless transmission interruption probability and model upload latency.The simulation results show that CFJO significantly improves the model training efficiency and convergence performance with lower interruption probability under the latency constraint.展开更多
基金funded by the National Key Research and Development Program of China under Grant 2019YFB1803301Beijing Natural Science Foundation(L202002).
文摘The Internet of Things(IoTs)has become an essential component of the 5th Generation(5G)network and beyond,accelerating the transition to digital society.The increasing signaling traffic generated by billions of IoT devices has placed significant strain on the 5G Core network(5GC)control plane.To address this issue,the 3rd Gener-ation Partnership Project(3GPP)first proposed a Service-Based Architecture(SBA),intending to create a flexible,scalable,and agile cloud-native 5GC.However,considering the coupling of protocol states and functions,there are still many challenges to fully utilize the benefits of the cloud computing and orchestrate the 5GC in a cloud-native manner.We propose a Message-Level StateLess Design(ML-SLD)to provide a cloud-native 5GC from an architectural standpoint in this paper.Firstly,we propose an innovative mechanism for servitization of the N2 interface to maintain the connection between Radio Access Network(RAN)and the 5GC,avoiding interruptions and dropouts of large-scale user data.Furthermore,we propose an On-demand Message Forwarding(OMF)al-gorithm to reduce the impact of cloud fluctuations on the performance of cloud-native 5GC.Finally,we create a prototype that is based on the OpenAirInterface(OAI)5G core network projects,with all Network Functions(NFs)packaged in dockers and deployed in a kubernetes-based cloud environment.Several experiments have been built with UERANSIM and Chaosblade simulation tools.The findings demonstrate the viability and efficiency of our proposed methods.
基金supported by the National Key R&D Program of China(2020YFB1806800)funded by Beijing University of Posts and Telecommunications-China Mobile Reserch Institute Joint Innovation Center。
文摘Artificial intelligence(AI)has made a profound impact on our daily life.The 6 th generation mobile networks(6G)should be designed to enable AI services.The native intelligence is introduced as an important feature in 6G.6G native AI network is realized by the philosophy of federated learning(FL)to ensure data security and privacy.Federated learning over wireless communication networks is treated as a potential solution to realize native AI.However,introducing FL in the 6G will lead to expansive communication cost and unstable FL convergence with unreliable air interface.In this paper,we propose a solution for FL over wireless networks and analyze the training efficiency.To make full use of the advantages of the proposed network,we introduce a communication-FL joint optimization(CFJO)algorithm by jointly considering the effects of uplink resource,energy consumption and latency constraints.CFJO derives a transmission strategy with resource allocation and retransmissions to reduce the wireless transmission interruption probability and model upload latency.The simulation results show that CFJO significantly improves the model training efficiency and convergence performance with lower interruption probability under the latency constraint.
