@misc{10481/80955,
year = {2022},
month = {9},
url = {https://hdl.handle.net/10481/80955},
abstract = {Time-Sensitive Networking (TSN) is expected to become a key layer 2 technology for 5G and Beyond (5GB) transport networks (TN) as it allows for services with stringent and deterministic quality-of-service constraints and their coexistence with non-performance-sensitive traffic. Autonomous solutions for configuring TSN-based TNs are essential to ensure the deterministic QoS requisites of the 5GB streams while facilitating the zero-touch management of the network and reducing the operational costs. However, due to the configuration flexibility offered by TSN networks, using exact optimization methods to develop such solutions usually results in algorithms with high computational complexity. In this work, we propose and evaluate an initial design of a Reinforcement Learning (RL)-based solution for the long-term configuration of asynchronous TSN-based 5GB TNs. We successfully validated the proper operation of the proposal for an industrial private 5G scenario.},
organization = {H2020 research and innovation project 5G-CLARITY (Grant No. 871428)},
organization = {TRUE5G (PID2019-
108713RB-C53)},
organization = {6G-CHRONOS (TSI-063000-2021-28)},
keywords = {DRL},
keywords = {Time-Sensitive Networking (TSN)},
keywords = {Asynchronous Traffic Shape},
keywords = {5G},
keywords = {Transport Networks},
title = {Leveraging DRL for Traffic Prioritization in 5G and Beyond TSN-based Transport Networks},
doi = {10.5281/zenodo.7060473},
author = {Prados Garzón, Jonathan and Chinchilla Romero, Lorena and Muñoz Luengo, Pablo and Ameigeiras Gutiérrez, Pablo José and Ramos Muñoz, Juan José},
}