RT Conference Proceedings
T1 ML-based network management framework for XR services.
A1 Peñaherrera-Pulla, Oswaldo Sebastián
A1 Baena, Carlos
A1 Barco-Moreno, Raquel
A1 Fortes-Rodríguez, Sergio
K1 Redes de ordenadores
K1 Telecomunicaciones
AB This work presents a novel framework designed for the management of XR (Extended Reality) services for B5G/6G network paradigms. These networks will enable its near-future deployment to change the concept of the XR experiences known at this moment. Our proposed framework powered by ML (Machine Learning) consists of the measurement and estimation of metrics based on network-accessible information, and a proof of concept of network optimization. The latter is based on the use of KQI (Key Quality Indicators) to tune the performance of XR services. This in conjunction with ML approaches, can offer additional levels of intelligence to networks. To validate this, a 360-video service has been selected as a use case to provide a proof of concept of the performance, utility, and novelty of this work.
YR 2023
FD 2023
LK https://hdl.handle.net/10630/27350
UL https://hdl.handle.net/10630/27350
LA eng
NO This work has been partially funded by: Ministerio de Asuntos Económicos y Transformación Digital and European Union - NextGenerationEU within the framework “Recuperación, Transformación y Resiliencia y el Mecanismo de Recuperación y Resiliencia” under the project MAORI, and Universidad de Málaga through the “II Plan Propio de Investigación, Transferencia y Divulgación Científica”. This work has been also supported by Junta de Andalucía through Secretaría General de Universidades, Investigación y Tecnología with predoctoral grant (Ref. PREDOC_01712) as well as by Ministerio de Ciencia y Tecnología through grant FPU19/04468.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 27 feb 2026