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Multi-User Semantic Communications System with Spectrum Scarcity

Journal of Communications and Information Networks 2022年第4期7卷 375-382页

作者： romano fantacci Benedetta Picano Via di Santa Marta 3 Florence 50139Italy

Nowadays,the emerging paradigm of semantic communications seems to offer an attractive opportunity to improve the transmission reliability and efficiency in new generation communication *** 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 *** 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 *** a consequence,a deep-convolution neural networks based encoder-decoder architecture has been exploited here in the definition of the proposed semantic communications ***,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.

关键词： semantic communication terahertz communications machine learning

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Federated learning framework for mobile edge computing networks

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CAAI Transactions on Intelligence Technology 2020年第1期5卷 15-21页

作者： romano fantacci Benedetta Picano Department of Information Engineering University of FlorenceFlorenceItaly

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 *** 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 ***,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 *** this context,federated learning gained attention as a solution to perform learning procedures from data disseminated across multiple users,keeping the sensitive data *** study applies federated learning to the demand prediction problem,to accurately forecast the more popular application types in 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 *** 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.

关键词： prediction forecasting framework

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