Self‐Managed 5G Networks 1

Autor: Josep Mangues-Bafalluy, Corrado Puligheddu, Jorge Martin-Perez, Andrea Sgambelluri, Koen A.E. De Schepper, Engin Zeydan, Carla-Fabiana Chiasserini, Luca Valcarenghi, Sokratis Barmpounakis, Panagiotis Kontopoulos, Francesco Paolucci, Xi Li, Nikolaos Koursioumpas, Carlos J. Bernardos, Andres Garcia-Saavedra, Ricardo Martinez, Carlos Guimaraes, Kiril Antevski, Lina Magoula, Jorge Baranda, Danny De Vleeschauwer, Claudio EttoreCasetti, Chrysa Papagianni

Publikováno v: Communication Networks and Service Management in the Era of Artificial Intelligence and Machine Learning

Meeting 5G high bandwidth rates, ultra‐low latencies, and high reliabilities requires of network infrastructures that automatically increase/decrease the resources based on their customers' demand.An autonomous and dynamic management of a 5G networ

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b1825d8c1a77bde979b6d3cd52c8a5b4
https://doi.org/10.1002/9781119675525.ch4

Network Traffic Anomaly Prediction for Beyond 5G Networks

Autor: Nikolaos Koursioumpas, Lina Magoula, Sokratis Barmpounakis, Ioannis Stavrakakis

Publikováno v: 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b91125816287d4d77764a6c1627ef126
https://doi.org/10.1109/pimrc54779.2022.9977469

LSTM-based QoS prediction for 5G-enabled Connected and Automated Mobility applications

Autor: Ramin Khalili, Lina Magoula, Jose Mauricio Perdomo, Ramya Panthangi Manjunath, Sokratis Barmpounakis, Nikolaos Koursioumpas

Publikováno v: 5GWF

Determining whether the network can provide the required resources -and as a result the required Quality of Service (QoS)- for Connected and Automated Mobility (CAM) applications is crucial, as human safety is involved. In order to identify potential

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::cb6b8af6390acf25ad2f6404b15d46ec
https://doi.org/10.1109/5gwf52925.2021.00083

5Growth data-driven AI-based scaling

Autor: Sokratis Barmpounakis, Jorge Martin-Perez, Lina Magoula, Josep Mangues-Bafalluy, Xi Li, Danny De Vleeschauwer, Andres Garcia-Saavedra, Jorge Baranda, Carla-Fabiana Chiasserini, Marco Malinverno, Koteswararao Kondepu, Corrado Puligheddu, Luca Valcarenzhi, Chrysa Papagianni

Publikováno v: 2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit): Porto, Portugal, 8-11 June 2021, 383-388
STARTPAGE=383;ENDPAGE=388;TITLE=2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
EuCNC/6G Summit
e-Archivo. Repositorio Institucional de la Universidad Carlos III de Madrid
instname

Proceedings of: Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 8-11 June 2021, Porto, Portugal. This paper presents a data-driven approach leveraging AI/ML models to automate the service scaling operation and,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb907b3290f8554c59c3bdcf2e8627fa
https://dare.uva.nl/personal/pure/en/publications/5growth-datadriven-aibased-scaling(c739aadf-3e1b-4829-a4ea-42e7af6b9c5f).html