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
Long Term Evolution (LTE) is designed to revolutionize mobile broadband technology with key considerations of higher data rate, improved power efficiency, low latency and better quality of service. This work analyzes ...Long Term Evolution (LTE) is designed to revolutionize mobile broadband technology with key considerations of higher data rate, improved power efficiency, low latency and better quality of service. This work analyzes the impact of resource scheduling algorithms on the performance of LTE (4G) and WCDMA (3G) networks. In this paper, a full illustration of LTE system is given together with different scheduling algorithms. Thereafter, 3G WCDMA and 4G LTE networks were simulated using Simulink simulator embedded in MATLAB and performance evaluations were carried out. The performance metrics used for the evaluations are average system throughput, packet delay, latency and allocation of fairness using Round Robin, Best CQI and Proportional fair Packet Scheduling Algorithms. The results of the evaluations on both networks were analysed. The results showed that 4G LTE network performs better than 3G WCDMA network in all the three scheduling algorithms used.展开更多
A 3G system simulation platform is designed to research on and evaluate theeffects of various techniques of the 3G network performance. The basic structure and principle ofthe simulation platform is presented and the ...A 3G system simulation platform is designed to research on and evaluate theeffects of various techniques of the 3G network performance. The basic structure and principle ofthe simulation platform is presented and the simulation process is addressed in detail.展开更多
It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computin...It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computing industries,the rapid convergence of 5th generation mobile communication technology(5G)and AI is beginning to significantly transform the core communication infrastructure,network management,and vertical applications.The paper first outlined the individual roadmaps of mobile communications and AI in the early stage,with a concentration to review the era from 3rd generation mobile communication technology(3G)to 5G when AI and mobile communications started to converge.With regard to telecommunications AI,the progress of AI in the ecosystem of mobile communications was further introduced in detail,including network infrastructure,network operation and management,business operation and management,intelligent applications towards business supporting system(BSS)&operation supporting system(OSS)convergence,verticals and private networks,etc.Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations.Towards the next decade,the prospective roadmap of telecommunications AI was forecasted.In line with 3rd generation partnership project(3GPP)and International Telecommunication Union Radiocommunication Sector(ITU-R)timeline of 5G&6th generation mobile communication technology(6G),the network intelligence following 3GPP and open radio access network(O-RAN)routes,experience and intent-based network management and operation,network AI signaling system,intelligent middle-office based BSS,intelligent customer experience management and policy control driven by BSS&OSS convergence,evolution from service level agreement(SLA)to experience level agreement(ELA),and intelligent private network for verticals were further explored.The paper is concluded with the vision that AI will reshape the future beyond 5G(B5G)/6G landscape,and we need pivot our research and development(R&D),standardizations,and ecosystem to fully take the unprecedented opportunities.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.
文摘Long Term Evolution (LTE) is designed to revolutionize mobile broadband technology with key considerations of higher data rate, improved power efficiency, low latency and better quality of service. This work analyzes the impact of resource scheduling algorithms on the performance of LTE (4G) and WCDMA (3G) networks. In this paper, a full illustration of LTE system is given together with different scheduling algorithms. Thereafter, 3G WCDMA and 4G LTE networks were simulated using Simulink simulator embedded in MATLAB and performance evaluations were carried out. The performance metrics used for the evaluations are average system throughput, packet delay, latency and allocation of fairness using Round Robin, Best CQI and Proportional fair Packet Scheduling Algorithms. The results of the evaluations on both networks were analysed. The results showed that 4G LTE network performs better than 3G WCDMA network in all the three scheduling algorithms used.
文摘A 3G system simulation platform is designed to research on and evaluate theeffects of various techniques of the 3G network performance. The basic structure and principle ofthe simulation platform is presented and the simulation process is addressed in detail.
文摘It has been an exciting journey since the mobile communications and artificial intelligence(AI)were conceived in 1983 and 1956.While both fields evolved independently and profoundly changed communications and computing industries,the rapid convergence of 5th generation mobile communication technology(5G)and AI is beginning to significantly transform the core communication infrastructure,network management,and vertical applications.The paper first outlined the individual roadmaps of mobile communications and AI in the early stage,with a concentration to review the era from 3rd generation mobile communication technology(3G)to 5G when AI and mobile communications started to converge.With regard to telecommunications AI,the progress of AI in the ecosystem of mobile communications was further introduced in detail,including network infrastructure,network operation and management,business operation and management,intelligent applications towards business supporting system(BSS)&operation supporting system(OSS)convergence,verticals and private networks,etc.Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations.Towards the next decade,the prospective roadmap of telecommunications AI was forecasted.In line with 3rd generation partnership project(3GPP)and International Telecommunication Union Radiocommunication Sector(ITU-R)timeline of 5G&6th generation mobile communication technology(6G),the network intelligence following 3GPP and open radio access network(O-RAN)routes,experience and intent-based network management and operation,network AI signaling system,intelligent middle-office based BSS,intelligent customer experience management and policy control driven by BSS&OSS convergence,evolution from service level agreement(SLA)to experience level agreement(ELA),and intelligent private network for verticals were further explored.The paper is concluded with the vision that AI will reshape the future beyond 5G(B5G)/6G landscape,and we need pivot our research and development(R&D),standardizations,and ecosystem to fully take the unprecedented opportunities.
