TY - GEN
T1 - 5GTQ
T2 - 98th IEEE Vehicular Technology Conference, VTC 2023-Fall
AU - Debnath, Rubi
AU - Akinci, Mustafa Selman
AU - Ajith, Devika
AU - Steinhorst, Sebastian
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The integration of Time-Sensitive Networking (TSN) and 5G communication is crucial for achieving ultra-reliable and low-latency communication (URLLC) in various domains. This includes applications like industrial automation involving mobile and collaborative robots, as well as Industrial IoT (IIoT). However, an exhaustive performance analysis of mixed 5G-TSN networks that support the unique features of TSN and 5G is lacking. Furthermore, a deeper understanding of TSN traffic scheduling in the 5G system (5GS) is necessary. To address this gap, we propose a novel 5G-TSN Quality-of-Service (QoS) aware simulation framework that incorporates priority-based scheduling in the 5GS. Our framework integrates the latest 5G and TSN simulation libraries using OMNeT++ and provides the first-ever results of the performance analysis of a 5G-TSN converged network. We implement the 5G-TSN bridge translation mechanism and introduce a QoS mapping algorithm for our framework. Through a detailed performance evaluation, we assess the impact of 5G QoS-aware scheduling methods on the overall network performance. Our open-source framework utilizes the latest Simu5G and INET4.4 libraries and simulates two different 5G-TSN scenarios to provide comprehensive insights. The results of our study demonstrate that the performance of the 5G-TSN network is significantly influenced by the scheduling in the 5G network, as a substantial portion of the overall delay originates from the 5GS. Notably, our findings reveal that the 5G-TSN network can achieve latency values within 3ms for TSN traffic, emphasizing the need for a joint scheduling mechanism to meet URLLC requirements.
AB - The integration of Time-Sensitive Networking (TSN) and 5G communication is crucial for achieving ultra-reliable and low-latency communication (URLLC) in various domains. This includes applications like industrial automation involving mobile and collaborative robots, as well as Industrial IoT (IIoT). However, an exhaustive performance analysis of mixed 5G-TSN networks that support the unique features of TSN and 5G is lacking. Furthermore, a deeper understanding of TSN traffic scheduling in the 5G system (5GS) is necessary. To address this gap, we propose a novel 5G-TSN Quality-of-Service (QoS) aware simulation framework that incorporates priority-based scheduling in the 5GS. Our framework integrates the latest 5G and TSN simulation libraries using OMNeT++ and provides the first-ever results of the performance analysis of a 5G-TSN converged network. We implement the 5G-TSN bridge translation mechanism and introduce a QoS mapping algorithm for our framework. Through a detailed performance evaluation, we assess the impact of 5G QoS-aware scheduling methods on the overall network performance. Our open-source framework utilizes the latest Simu5G and INET4.4 libraries and simulates two different 5G-TSN scenarios to provide comprehensive insights. The results of our study demonstrate that the performance of the 5G-TSN network is significantly influenced by the scheduling in the 5G network, as a substantial portion of the overall delay originates from the 5GS. Notably, our findings reveal that the 5G-TSN network can achieve latency values within 3ms for TSN traffic, emphasizing the need for a joint scheduling mechanism to meet URLLC requirements.
KW - 5G
KW - 5QI
KW - Quality-of-Service
KW - Time-Sensitive Networking
KW - simulation
KW - wired-wireless communication
UR - http://www.scopus.com/inward/record.url?scp=85181174848&partnerID=8YFLogxK
U2 - 10.1109/VTC2023-Fall60731.2023.10333533
DO - 10.1109/VTC2023-Fall60731.2023.10333533
M3 - Conference contribution
AN - SCOPUS:85181174848
T3 - IEEE Vehicular Technology Conference
BT - 2023 IEEE 98th Vehicular Technology Conference, VTC 2023-Fall - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 October 2023 through 13 October 2023
ER -