This paper applies the perspective of business ecosystem to mobile communications industry,trying to help mobile network operators improve their strategies in the era of the third generation mobile communications(3G)....This paper applies the perspective of business ecosystem to mobile communications industry,trying to help mobile network operators improve their strategies in the era of the third generation mobile communications(3G).According to the definition of the business ecosystem,the ecosystem structure of mobile network operators is analyzed.As an important hub in the ecosystem,mobile network operators are advised to take a keystone strategy.The key points of the strategy are summarized.Finally,suggestions for Chinese mobile network operators are given based on the analysis.展开更多
Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence ...Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence of the sixth generation of mobile communications technology(6G).However,the existing studies just focus on the spatio-temporal modeling of traffic data of single network service,such as short message,call,or Internet.It is not conducive to accurate prediction of traffic data,characterised by diverse network service,spatio-temporality and supersize volume.To address this issue,a novel multi-task deep learning framework is developed for citywide cellular network traffic prediction.Functionally,this framework mainly consists of a dual modular feature sharing layer and a multi-task learning layer(DMFS-MT).The former aims at mining long-term spatio-temporal dependencies and local spatio-temporal fluctuation trends in data,respectively,via a new combination of convolutional gated recurrent unit(ConvGRU)and 3-dimensional convolutional neural network(3D-CNN).For the latter,each task is performed for predicting service-specific traffic data based on a fully connected network.On the real-world Telecom Italia dataset,simulation results demonstrate the effectiveness of our proposal through prediction performance measure,spatial pattern comparison and statistical distribution verification.展开更多
文摘This paper applies the perspective of business ecosystem to mobile communications industry,trying to help mobile network operators improve their strategies in the era of the third generation mobile communications(3G).According to the definition of the business ecosystem,the ecosystem structure of mobile network operators is analyzed.As an important hub in the ecosystem,mobile network operators are advised to take a keystone strategy.The key points of the strategy are summarized.Finally,suggestions for Chinese mobile network operators are given based on the analysis.
基金supported in part by the Science and Technology Project of Hebei Education Department(No.ZD2021088)in part by the S&T Major Project of the Science and Technology Ministry of China(No.2017YFE0135700)。
文摘Spatio-temporal cellular network traffic prediction at wide-area level plays an important role in resource reconfiguration,traffic scheduling and intrusion detection,thus potentially supporting connected intelligence of the sixth generation of mobile communications technology(6G).However,the existing studies just focus on the spatio-temporal modeling of traffic data of single network service,such as short message,call,or Internet.It is not conducive to accurate prediction of traffic data,characterised by diverse network service,spatio-temporality and supersize volume.To address this issue,a novel multi-task deep learning framework is developed for citywide cellular network traffic prediction.Functionally,this framework mainly consists of a dual modular feature sharing layer and a multi-task learning layer(DMFS-MT).The former aims at mining long-term spatio-temporal dependencies and local spatio-temporal fluctuation trends in data,respectively,via a new combination of convolutional gated recurrent unit(ConvGRU)and 3-dimensional convolutional neural network(3D-CNN).For the latter,each task is performed for predicting service-specific traffic data based on a fully connected network.On the real-world Telecom Italia dataset,simulation results demonstrate the effectiveness of our proposal through prediction performance measure,spatial pattern comparison and statistical distribution verification.
