NGN is the current hot topic of the fixed network development,while 3G is the trend of the mobile communication in the next few years. Since the emerging all-service operators will soon have new de- mands on communica...NGN is the current hot topic of the fixed network development,while 3G is the trend of the mobile communication in the next few years. Since the emerging all-service operators will soon have new de- mands on communication network technologies,it is foreseeable that mobile and fixed NGNs converge.The article mainly analyzes the solutions to the convergence of mobile and fixed NGNs,and points out that convergence is a long-term goal.展开更多
Fixed Mobile Convergence (FMC) is the focus of the future communications network development,on which the industry has kept strengthening its relevant research. However,before a large scale of implementation,there are...Fixed Mobile Convergence (FMC) is the focus of the future communications network development,on which the industry has kept strengthening its relevant research. However,before a large scale of implementation,there are still lots of key issues to be explored. The adoption of IP Multimedia Subsystem (IMS) architecture to replace the real-time services in the traditional application needs to solve the Quality of Service (QoS) problem in the IP domain,unify the method that the fixed and mobile users of the traditional circuit domain access IMS,complete the mapping of circuit domain service flow to IMS,guarantee the security of IMS bearer network,transform the traditional operation mode,and set up a new pattern adaptable to the convergence.展开更多
Federated learning(FL)is a distributed machine learning(ML)framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data.In FL,the limited...Federated learning(FL)is a distributed machine learning(ML)framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data.In FL,the limited number of participants for model aggregation and communication latency are two major bottlenecks.Hierarchical federated learning(HFL),with a cloud-edge-client hierarchy,can leverage the large coverage of cloud servers and the low transmission latency of edge servers.There are growing research interests in implementing FL in vehicular networks due to the requirements of timely ML training for intelligent vehicles.However,the limited number of participants in vehicular networks and vehicle mobility degrade the performance of FL training.In this context,HFL,which stands out for lower latency,wider coverage and more participants,is promising in vehicular networks.In this paper,we begin with the background and motivation of HFL and the feasibility of implementing HFL in vehicular networks.Then,the architecture of HFL is illustrated.Next,we clarify new issues in HFL and review several existing solutions.Furthermore,we introduce some typical use cases in vehicular networks as well as our initial efforts on implementing HFL in vehicular networks.Finally,we conclude with future research directions.展开更多
Fixed-Mobile Convergence (FMC) has become a hot topic in the telecom industry. Telecom operators are paying close attention to discovering a road of convergence based on their existing networks. However,there are no u...Fixed-Mobile Convergence (FMC) has become a hot topic in the telecom industry. Telecom operators are paying close attention to discovering a road of convergence based on their existing networks. However,there are no unified strategies regarding the implementation of FMC at present. This article analyzes the deployment of FMC strategies for different types of operators,as well as different network layers,while assessing the impact of FMC on the existing operating modes. The operators are suggested to take the existing network structure,operation and maintenance system and business modes into planning and find out their proper roads to network convergence.展开更多
文摘NGN is the current hot topic of the fixed network development,while 3G is the trend of the mobile communication in the next few years. Since the emerging all-service operators will soon have new de- mands on communication network technologies,it is foreseeable that mobile and fixed NGNs converge.The article mainly analyzes the solutions to the convergence of mobile and fixed NGNs,and points out that convergence is a long-term goal.
文摘Fixed Mobile Convergence (FMC) is the focus of the future communications network development,on which the industry has kept strengthening its relevant research. However,before a large scale of implementation,there are still lots of key issues to be explored. The adoption of IP Multimedia Subsystem (IMS) architecture to replace the real-time services in the traditional application needs to solve the Quality of Service (QoS) problem in the IP domain,unify the method that the fixed and mobile users of the traditional circuit domain access IMS,complete the mapping of circuit domain service flow to IMS,guarantee the security of IMS bearer network,transform the traditional operation mode,and set up a new pattern adaptable to the convergence.
基金sponsored in part by the National Key R&D Program of China under Grant No. 2020YFB1806605the National Natural Science Foundation of China under Grant Nos. 62022049, 62111530197, and 61871254+1 种基金OPPOsupported by the Fundamental Research Funds for the Central Universities under Grant No. 2022JBXT001
文摘Federated learning(FL)is a distributed machine learning(ML)framework where several clients cooperatively train an ML model by exchanging the model parameters without directly sharing their local data.In FL,the limited number of participants for model aggregation and communication latency are two major bottlenecks.Hierarchical federated learning(HFL),with a cloud-edge-client hierarchy,can leverage the large coverage of cloud servers and the low transmission latency of edge servers.There are growing research interests in implementing FL in vehicular networks due to the requirements of timely ML training for intelligent vehicles.However,the limited number of participants in vehicular networks and vehicle mobility degrade the performance of FL training.In this context,HFL,which stands out for lower latency,wider coverage and more participants,is promising in vehicular networks.In this paper,we begin with the background and motivation of HFL and the feasibility of implementing HFL in vehicular networks.Then,the architecture of HFL is illustrated.Next,we clarify new issues in HFL and review several existing solutions.Furthermore,we introduce some typical use cases in vehicular networks as well as our initial efforts on implementing HFL in vehicular networks.Finally,we conclude with future research directions.
文摘Fixed-Mobile Convergence (FMC) has become a hot topic in the telecom industry. Telecom operators are paying close attention to discovering a road of convergence based on their existing networks. However,there are no unified strategies regarding the implementation of FMC at present. This article analyzes the deployment of FMC strategies for different types of operators,as well as different network layers,while assessing the impact of FMC on the existing operating modes. The operators are suggested to take the existing network structure,operation and maintenance system and business modes into planning and find out their proper roads to network convergence.
