RT Journal Article
T1 AutomAdapt: Zero Touch Configuration of 5G QoS Flows Extended for Time-Sensitive Networking.
A1 Luque Schempp, Francisco
A1 Panizo-Jaime, Laura
A1 Gallardo-Melgarejo, María del Mar
A1 Merino-Gómez, Pedro
K1 Sistemas de comunicaciones inalámbricos
K1 Aprendizaje automático (Inteligencia artificial)
K1 Sistemas autoorganizativos
AB The aim of IEEE Time-Sensitive Networking (TSN) standards is to grant deterministic communication in traditional Ethernet networks for Industry 4.0. Insofar as the use cases in the Factory need some mobility, the extension of the TSN capabilities over the fifth-generation (5G) cellular network is the next step. Some challenges in TSN over 5G, such as TSN translators time synchronization functionality, are well defined in the standards, even if they have not yet been addressed in the market. However other challenges, such as the dynamic configuration of the entire network (or part of the it) based on quality requirements of the current TSN traffic pattern, are defined at a very high level and delegated to vendors for implementation. This paper addresses this challenge, using an Automata Learning approach to monitor and reconfigure the end-to-end 5G QoS flow to keep the quality of a TSN session within the required values. Additionally, algorithms are provided to build the automata from network data and predict potential deviations of the requirements to meet the expected quality. Moreover, this work presents a functional TSN over a 5G testbed where the algorithms have been tested, demonstrating that the proposed solution achieves an improvement of around 40% compared to the usual operation of the network.
PB IEEE
YR 2023
FD 2023
LK https://hdl.handle.net/10630/31085
UL https://hdl.handle.net/10630/31085
LA eng
NO F. Luque-Schempp, L. Panizo, M. -D. -M. Gallardo and P. Merino, "AutomAdapt: Zero Touch Configuration of 5G QoS Flows Extended for Time-Sensitive Networking," in IEEE Access, vol. 11, pp. 82960-82977, 2023, doi: 10.1109/ACCESS.2023.3302264
NO This work was supported in part by the EVOLVED5G Project (European Union Horizon 2020) under Grant 101016608; in part by the5G+TACTILE Project (NEXTGENERATION.UE, Spanish UNICO 5G I+D) under Grant TSI-063000-2021-11; and in part by the RFOGProject (Spanish Government) under Grant RTI2018-099777-B-I00.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026