Machine-learning based traffic forecasting for resource management in C-RAN
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Document typeConference report
Defense date2020
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
Abstract
The assumption of a fixed computational capacityat the Baseband Unit (BBU) pools in a Cloud Radio Access Network (C-RAN) deployment results in underutilized resourcesor unsatisfied users depending on traffic requirements. In thispaper a new strategy to predict the required resources based on Machine Learning techniques is proposed and analysed. SupportVector Machine (SVM), Time-Delay Neural Network (TDNN),and Long Short-Term Memory (LSTM) have been tested andcompared to select the best predicting approach. Instead of usinga regular synthetic scenario a realistic dense cell deployment overVienna city is used to validate the results. Authors show that theproposed solution reduces the unused resources average by 96 %
CitationGuerra, R. [et al.]. Machine-learning based traffic forecasting for resource management in C-RAN. A: European Conference on Networks and Communications. "2020 European Conference on Networks and Communications (EuCNC) - EuCNC 2020: European Conference on Networks and Communications, Dubrovnik, Croatia, June 15-18". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 1-5. ISBN 978-1-72814-355-2.
ISBN978-1-72814-355-2
Publisher versionhttps://ieeexplore.ieee.org/document/9200958
Collections
- Doctorat en Teoria del Senyal i Comunicacions - Ponències/Comunicacions de congressos [161]
- WiComTec - Grup de recerca en Tecnologies i Comunicacions Sense Fils - Ponències/Comunicacions de congressos [175]
- Departament de Teoria del Senyal i Comunicacions - Ponències/Comunicacions de congressos [3.186]
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