The continuous growth of smart devices needing processing has led to moving storage and computation from cloud to the network edges,giving rise to the edge computing paradigm.Owing to the limited capacity of edge comp...The continuous growth of smart devices needing processing has led to moving storage and computation from cloud to the network edges,giving rise to the edge computing paradigm.Owing to the limited capacity of edge computing nodes,the presence of popular applications in the edge nodes results in significant improvements in users’satisfaction and service accomplishment.However,the high variability in the content requests makes prediction demand not trivial and,typically,the majority of the classical prediction approaches require the gathering of personal users’information at a central unit,giving rise to many users’privacy issues.In this context,federated learning gained attention as a solution to perform learning procedures from data disseminated across multiple users,keeping the sensitive data protected.This study applies federated learning to the demand prediction problem,to accurately forecast the more popular application types in the network.The proposed framework reaches high accuracy levels on the predicted applications demand,aggregating in a global and weighted model the feedback received by users,after their local training.The validity of the proposed approach is verified by performing a virtual machine replica copies and comparison with the alternative forecasting approach based on chaos theory and deep learning.展开更多
Nowadays,the emerging paradigm of semantic communications seems to offer an attractive opportunity to improve the transmission reliability and efficiency in new generation communication systems.In particular,focusing ...Nowadays,the emerging paradigm of semantic communications seems to offer an attractive opportunity to improve the transmission reliability and efficiency in new generation communication systems.In particular,focusing on spectrum scarcity,expected to afflict the upcoming sixth generation(6G)networks,this paper analyses the semantic communications behavior in the context of a cell-dense scenario,in which users belonging to different small base station areas may be allocated on a same channel giving rise to a non-negligible interference that severely affects the communications reliability.In such a context,artificial intelligence methodologies are of paramount importance in order to speed up the switch from traditional communication to the novel semantic communication paradigm.As a consequence,a deep-convolution neural networks based encoder-decoder architecture has been exploited here in the definition of the proposed semantic communications framework.Finally,extensive numerical simulations have been performed to test the advantages of the proposed framework in different interfering scenarios and in comparison with different traditional or semantic alternatives.展开更多
文摘The continuous growth of smart devices needing processing has led to moving storage and computation from cloud to the network edges,giving rise to the edge computing paradigm.Owing to the limited capacity of edge computing nodes,the presence of popular applications in the edge nodes results in significant improvements in users’satisfaction and service accomplishment.However,the high variability in the content requests makes prediction demand not trivial and,typically,the majority of the classical prediction approaches require the gathering of personal users’information at a central unit,giving rise to many users’privacy issues.In this context,federated learning gained attention as a solution to perform learning procedures from data disseminated across multiple users,keeping the sensitive data protected.This study applies federated learning to the demand prediction problem,to accurately forecast the more popular application types in the network.The proposed framework reaches high accuracy levels on the predicted applications demand,aggregating in a global and weighted model the feedback received by users,after their local training.The validity of the proposed approach is verified by performing a virtual machine replica copies and comparison with the alternative forecasting approach based on chaos theory and deep learning.
基金This work was supported by the PNRR-Mission 4-Next Generation EU 1.3-contract PE0000001-research and innovation on future telecommunications systems and networks,to make Italy more smart.
文摘Nowadays,the emerging paradigm of semantic communications seems to offer an attractive opportunity to improve the transmission reliability and efficiency in new generation communication systems.In particular,focusing on spectrum scarcity,expected to afflict the upcoming sixth generation(6G)networks,this paper analyses the semantic communications behavior in the context of a cell-dense scenario,in which users belonging to different small base station areas may be allocated on a same channel giving rise to a non-negligible interference that severely affects the communications reliability.In such a context,artificial intelligence methodologies are of paramount importance in order to speed up the switch from traditional communication to the novel semantic communication paradigm.As a consequence,a deep-convolution neural networks based encoder-decoder architecture has been exploited here in the definition of the proposed semantic communications framework.Finally,extensive numerical simulations have been performed to test the advantages of the proposed framework in different interfering scenarios and in comparison with different traditional or semantic alternatives.
