publication . Conference object . 2018

Mobile Traffic Prediction from Raw Data Using LSTM Networks

Hoang Duy Trinh; Lorenza Giupponi; Paolo Dini;
Open Access English
  • Published: 13 Sep 2018
Abstract
Predictive analysis on mobile network traffic is becoming of fundamental importance for the next generation cellular network. Proactively knowing the user demands, allows the system for an optimal resource allocation. In this paper, we study the mobile traffic of an LTE base station and we design a system for the traffic prediction using Recurrent Neural Networks. The mobile traffic information is gathered from the Physical Downlink Control CHannel (PDCCH) of the LTE using the passive tool presented in [1]. Using this tool we are able to collect all the control information at 1 ms resolution from the base station. This information comprises the resource blocks, ...
Subjects
free text keywords: Control channel, Base station, Resource allocation, Multilayer perceptron, Cellular network, Real-time computing, EnodeB, Recurrent neural network, Telecommunications link, Computer science
Funded by
EC| SCAVENGE
Project
SCAVENGE
Sustainable CellulAr networks harVEstiNG ambient Energy
  • Funder: European Commission (EC)
  • Project Code: 675891
  • Funding stream: H2020 | MSCA-ITN-ETN
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publication . Conference object . 2018

Mobile Traffic Prediction from Raw Data Using LSTM Networks

Hoang Duy Trinh; Lorenza Giupponi; Paolo Dini;