| dc.contributor.author | Prados Garzón, Jonathan | |
| dc.contributor.author | Taleb, Tarik | |
| dc.date.accessioned | 2026-02-10T07:53:38Z | |
| dc.date.available | 2026-02-10T07:53:38Z | |
| dc.date.issued | 2021-03-08 | |
| dc.identifier.citation | J. Prados-Garzon and T. Taleb, "Asynchronous Time-Sensitive Networking for 5G Backhauling," in IEEE Network, vol. 35, no. 2, pp. 144-151, March/April 2021, doi: 10.1109/MNET.011.2000402 | es_ES |
| dc.identifier.issn | 1558-156X | |
| dc.identifier.issn | 0890-8044 | |
| dc.identifier.uri | https://hdl.handle.net/10481/110781 | |
| dc.description | This work is partially supported by the European Union’s
Horizon 2020 research and innovation programme under the
CHARITY project with grant agreement No. 101016509. It is
also partially supported by the Academy of Finland Project
CSN - under Grant Agreement 311654 and the 6Genesis
project under Grant No. 318927. | es_ES |
| dc.description.abstract | Fifth Generation (5G) phase 2 rollouts are around
the corner to make mobile ultra-reliable and low-latency services
a reality. However, to realize that scenario, besides the new 5G
built-in Ultra-Reliable Low-Latency Communication (URLLC)
capabilities, it is required to provide a substrate network with
deterministic Quality-of-Service support for interconnecting the
different 5G network functions and services. Time-Sensitive
Networking (TSN) appears as an appealing network technology
to meet the 5G connectivity needs in many scenarios involving
critical services and their coexistence with Mobile Broadband
traffic. In this article, we delve into the adoption of asynchronous
TSN for 5G backhauling and some of the relevant related
aspects. We start motivating TSN and introducing its mainstays.
Then, we provide a comprehensive overview of the architecture
and operation of the IEEE 802.1Qcr Asynchronous Traffic
Shaper (ATS), the building block of asynchronous TSN. Next,
a management framework based on ETSI Zero-touch network
and Service Management (ZSM) and Abstraction and Control
of Traffic Engineered Networks (ACTN) reference models is
presented for enabling the TSN transport network slicing and
its interworking with Fifth Generation (5G) for backhauling.
After, we cover the flow allocation problem in asynchronous
TSNs and the importance of Machine Learning techniques for
assisting it. Last, we present a simulation-based proof-of-concept
(PoC) to assess the capacity of ATS-based forwarding planes for
accommodating 5G data flows. | es_ES |
| dc.description.sponsorship | European Union’s Horizon 2020, Nº 101016509 | es_ES |
| dc.description.sponsorship | Academy of Finland Project CSN, grant 311654 | es_ES |
| dc.description.sponsorship | Genesis project, grant 318927 | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | IEEE | es_ES |
| dc.rights | Atribución-NoComercial-CompartirIgual 4.0 Internacional | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
| dc.subject | Backhaul Networks | es_ES |
| dc.subject | Transport Networks | es_ES |
| dc.subject | Time-Sensitive Networking (TSN) | es_ES |
| dc.title | Asynchronous Time-Sensitive Networking for 5G Backhauling | es_ES |
| dc.type | journal article | es_ES |
| dc.rights.accessRights | open access | es_ES |
| dc.identifier.doi | 10.1109/MNET.011.2000402 | |
| dc.type.hasVersion | SMUR | es_ES |