Mostra el registre d'ítem simple
Machine-learning based traffic forecasting for resource management in C-RAN
| dc.contributor.author | Guerra Gómez, Rolando |
| dc.contributor.author | Ruiz Boqué, Sílvia |
| dc.contributor.author | García Lozano, Mario |
| dc.contributor.author | Olmos Bonafé, Juan José |
| dc.contributor.other | Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions |
| dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
| dc.date.accessioned | 2021-05-13T10:53:40Z |
| dc.date.issued | 2020 |
| dc.identifier.citation | Guerra, 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. |
| dc.identifier.isbn | 978-1-72814-355-2 |
| dc.identifier.uri | http://hdl.handle.net/2117/345553 |
| dc.description.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 % |
| dc.description.sponsorship | This work has been done under COST CA15104 IRACONEU project. It was supported in part by the Spanish ministryof science through the project RTI2018-099880-B-C32, with ERFD funds, and the Grant FPI-UPC provided by the UPC. |
| dc.format.extent | 5 p. |
| dc.language.iso | eng |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| dc.subject | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| dc.subject.lcsh | Machine learning |
| dc.subject.lcsh | Software-defined networking (Computer network technology) |
| dc.subject.other | machine learning |
| dc.subject.other | Cloud Radio Access Network |
| dc.title | Machine-learning based traffic forecasting for resource management in C-RAN |
| dc.type | Conference report |
| dc.subject.lemac | Aprenentatge automàtic |
| dc.subject.lemac | Xarxes definides per programari (Tecnologia de xarxes d'ordinadors) |
| dc.contributor.group | Universitat Politècnica de Catalunya. WiComTec - Grup de recerca en Tecnologies i Comunicacions Sense Fils |
| dc.description.peerreviewed | Peer Reviewed |
| dc.relation.publisherversion | https://ieeexplore.ieee.org/document/9200958 |
| dc.rights.access | Restricted access - publisher's policy |
| local.identifier.drac | 28703507 |
| dc.description.version | Postprint (published version) |
| dc.date.lift | 10000-01-01 |
| local.citation.author | Guerra, R.; Ruiz, S.; Garcia-Lozano, M.; Olmos, J. |
| local.citation.contributor | European Conference on Networks and Communications |
| local.citation.publicationName | 2020 European Conference on Networks and Communications (EuCNC) - EuCNC 2020: European Conference on Networks and Communications, Dubrovnik, Croatia, June 15-18 |
| local.citation.startingPage | 1 |
| local.citation.endingPage | 5 |