Comparative Performance Evaluation of 5G-TSN Applications in Indoor Factory Environments

Dirk Pesch; Kouros Zanbouri; Md. Noor-A-Rahim

REVIEW 4 major objections 5 minor 21 references

Comparative Performance Evaluation of 5G-TSN Applications in Indoor Factory Environments

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper claims that a private 5G network integrated as a time-sensitive networking (TSN) bridge can carry a factory robot's high-priority control traffic with stable end-to-end latency across four standard indoor factory channel…

desk verdict Useful 5G-TSN simulation study in 3GPP indoor factory channels, but an invalid FR1 numerology configuration undermines the headline latency numbers. read the letter →

arxiv 2501.12792 v2 pith:6JW5EE6Z submitted 2025-01-22 cs.NI

Kouros Zanbouri , Md. Noor-A-Rahim , Dirk Pesch This is my paper · ORCID

classification cs.NI

keywords 5GTSNWirelessIndustrialNetworksIndoorFactoryURLLCEnd-to-endlatencyHARQ

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to show that wireless 5G can be fused with time-sensitive networking (TSN), the Ethernet mechanism that gives industrial traffic bounded latency, and still meet the timing demands of a factory floor. It simulates a private single-cell 5G network acting as a TSN bridge and carrying the traffic of a mobile industrial robot across four indoor factory channel profiles defined by the TR 38.901 report. The key result is that the highest-priority Network Control traffic keeps stable end-to-end latency across every profile and test case, while video and best-effort traffic show wider delay variation. The authors conclude that 5G-TSN can effectively handle latency-critical applications in indoor factories, at least in a controlled single-device setting.

What carries the argument

The load-bearing object is the indoor factory channel model of the TR 38.901 report, implemented in the simulation with four profiles that vary clutter density and base-station height: sparse/low, dense/low, sparse/high, and dense/high. The model feeds radio propagation effects through path loss and line-of-sight probability into SINR, then into block-error probability and HARQ retransmissions, which is how the channel ultimately shapes end-to-end latency. On top of that radio core, the network classifies traffic into three TSN priority levels — Network Control, Video, and Best Effort — mapped to 5G quality-of-service bearers, so the simulation jointly measures the effects of radio environment, distance, and traffic priority.

What would settle it

Repeat the same traffic mix in a real or more standard simulation setting with a common subcarrier spacing (for example 30 kHz at 3.5 GHz) and several UEs sharing the cell; if the high-priority control stream's end-to-end latency or jitter exceeds the bounds that TSN guarantees on wired Ethernet, the claim that 5G-TSN can address latency-sensitive factory scenarios would fail.

Watch

Extended reading notes

Core claim

The central discovery is that, in the simulated 5G-TSN network, periodic Network Control traffic — packets of 50 to 500 bytes arriving every 50 ms to 1 s — keeps a stable end-to-end latency under all four indoor factory profiles (InF-SL, InF-DL, InF-SH, InF-DH), whereas Video and Best Effort flows show much wider delay spread. The simulations also show that InF-DL produces the lowest downlink SINR and InF-SH the highest, and that Hybrid Automatic Repeat reQuest (HARQ) error rates grow with distance in every profile, with InF-SL and InF-SH degrading most sharply and InF-DL and InF-DH tolerating longer links. The paper reads these results as evidence that 5G-TSN can address latency-sensitive scenarios in indoor factory environments.

Load-bearing premise

The entire verdict rests on the simulation's radio settings matching real factory deployments: the configuration uses a single UE and single base station on flat ground, with numerology index 4 paired to a 5.9 GHz carrier, and if those choices are not representative of standard New Radio installations, the measured delays do not generalize.

Editorial extensions

If this is right

A single-cell private 5G network configured as a TSN bridge can keep periodic high-priority control traffic stable while video and best-effort flows share the same radio link.
Factory radio planning should treat the dense-clutter, low-base-station profile as the hardest case and the sparse-clutter, high-base-station profile as the best for short-range, high-rate links.
Distance is a first-order reliability factor: HARQ error rates rise in every profile as the terminal moves from 85 m to 255 m, so longer links should be assigned to the more latency-tolerant profiles.
The work provides a baseline for later 5G-TSN factory studies, since it is the first to combine the standard indoor factory profiles with a 5G-TSN network model.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

I infer that the reported stability of control traffic reflects both TSN prioritization and the light load of 50- to 500-byte packets; the paper tests only one UE, so real multi-robot factories should be expected to see more HARQ retransmissions.
The configuration pairs numerology index 4 with a 5.9 GHz carrier, which is not a standard New Radio deployment; switching to a common subcarrier spacing could shift the absolute latency numbers even if the ranking across profiles remains.
The pattern that InF-SH has high SINR but worse long-range latency suggests high base stations are best deployed as small cells rather than as wide-coverage cells, an extension the paper does not draw itself.
If the results hold in multi-UE tests, they imply that safety-critical control loops can move onto the wireless 5G-TSN segment, leaving wired TSN for only the most stringent interlocks.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Kouros and colleagues have put together a useful simulation study of 5G-TSN in an indoor factory setting. The genuinely new bit is the implementation of the 3GPP TR 38.901 indoor factory (InF) path-loss and LOS probability models inside Simu5G, and then using that to compare four InF profiles (SL, DL, SH, DH) for an AGV use case with three traffic classes. That is a reasonable and timely thing to do; the results showing that the dense-clutter profiles behave differently from sparse ones in terms of SINR, HARQ error rate, and end-to-end delay are plausible and instructive. The paper is clearly written and the authors are upfront that this is a controlled-environment study.

The soft spots are real, and one of them is load-bearing. Table I sets carrier frequency to 5.9 GHz (FR1) and numerology index to 4. In 3GPP NR, numerology index 4 means 240 kHz subcarrier spacing, which is not defined for FR1; FR1 supports 15, 30, and 60 kHz (indices 0-2). The stress-test note is correct: this invalid combination shortens the OFDM symbol duration and slot length, so the physical-layer transmission time, HARQ RTT, and processing delays are all compressed. Since the headline metric is end-to-end latency, the reported delays in Figs. 4 and 6 are likely unrealistically optimistic relative to any deployable FR1 system. This is not a matter of interpretation; the paper explicitly cites 3GPP TR 38.901/TS 38.211, and the configuration contradicts those specs. The authors need to rerun with a valid numerology (probably index 2 at 5.9 GHz, or justify a different band) and check whether the qualitative conclusions survive.

There are also secondary issues: no confidence intervals, seed counts, or run counts are reported for the box plots; there is no quantitative latency target to judge whether the values actually meet factory requirements (e.g., the IIC or 3GPP URLLC bounds); and no simulation artifacts are shared. The 'first to consider a wireless factory environment' claim is a bit strong given that the authors' own reference [14] already simulated InF environments, though that was channel characterization rather than full 5G-TSN.

On balance, this is a serious attempt at an important problem, and the InF implementation is a contribution in itself. The numerology error is fixable, but it affects the central quantitative claim, so the paper needs a major revision and a rerun before the results can be trusted. I would send it to peer review with the expectation of heavy revision, but I would not cite the current numerical results in my own work.

Referee Report

4 major / 5 minor

Summary. The paper simulates a 5G-TSN network supporting an automated guided vehicle in an indoor factory, using OMNeT++/Simu5G/5GTQ and implementing the 3GPP TR 38.901 indoor factory (InF) channel profiles. It evaluates SINR, HARQ error rate, and end-to-end delay for three traffic classes (network control, video, best effort) across four InF profiles and three distance ranges, and concludes that 5G-TSN can address latency-sensitive scenarios in indoor factory environments.

Significance. If the quantitative results are valid, the paper would provide a useful early simulation baseline for 5G-TSN deployment in factory environments, and the InF-profile implementation in Simu5G is a concrete contribution. The three-class traffic model and the comparison across InF-SL, InF-DL, InF-SH, and InF-DH are sensible. However, the main evidence rests on a radio configuration that does not correspond to a valid 3GPP NR deployment, so the significance of the reported latency numbers cannot be assessed until the simulations are corrected and rerun.

major comments (4)

[§IV, Table I] Numerology index 4 at a 5.9 GHz carrier is not a valid 3GPP NR configuration. Numerology index 4 corresponds to 240 kHz subcarrier spacing, which is defined for FR2, not for FR1 (410 MHz–7.125 GHz), where the supported spacings are 15, 30, and 60 kHz (and at most 120 kHz in specific cases). This invalid setting shortens the OFDM symbol and slot duration, which directly reduces the physical-layer transmission time, HARQ round-trip time, and processing latency. Because the paper's headline metric is end-to-end delay, the reported values in Figs. 4 and 6 likely rely on an unsupported physical-layer configuration and are therefore not representative of any deployable 5G system in a factory. The paper should either use a valid FR1 numerology and rerun the simulations, or explicitly justify this configuration against the cited 3GPP specifications.
[§IV, Table I and Fig. 3] The physical environment is set to 'FlatGround' while the paper claims to evaluate the indoor factory profiles InF-SL, InF-DL, InF-SH, and InF-DH. FlatGround is not a 3GPP TR 38.901 InF environment, and with a single gNB and a single UE it does not capture the clutter, blockage, and shadowing statistics that distinguish the four InF profiles. The manuscript should explain how the FlatGround setting is combined with the InF path-loss models and demonstrate that the RF environment does not dominate the profile-specific differences shown in Fig. 3.
[§IV, Figs. 4–6 and Table II] No confidence intervals, number of simulation runs, random seed values, or simulation duration are reported. The paper's claims about latency stability and the differences between traffic classes and profiles are based on box plots, but the reader cannot determine whether those distributions come from enough independent replications to support the conclusions. This is load-bearing for the central claim and needs to be addressed, for example by reporting the number of replications and adding standard error or confidence-interval information.
[§III-B and §V] The traffic classes NC, Video, and BE are described qualitatively, but the paper does not map them to concrete 3GPP or TSN latency, reliability, or cycle-time requirements for industrial control. Without such a mapping, the statement that 5G-TSN 'can effectively handle latency-critical applications in indoor factories' remains qualitative, and the abstract's claim that the findings 'demonstrate' the ability to address latency-sensitive scenarios is not quantitatively supported. The paper should state which latency budgets and reliability targets the simulated traffic is intended to meet.

minor comments (5)

[§IV, Fig. 3] There is a typo in the text: 'the the highest SINR values' should be 'the highest SINR values'.
[§III-A] Equations (1)–(8) are not numbered consistently in the text, and Eq. (6) is said to cover 'Eqs. 6, 7, 8' while the displayed equations do not have visible equation numbers. Please renumber and format the equations for clarity.
[§III-A and Table I] The InF-HH profile is described in Section III-A but is not included in Table I or in the simulation results; please clarify whether it was excluded and why.
[§IV, Fig. 6] Figure 6 shows only InF-SL and InF-SH, while the surrounding text discusses InF-DL and InF-DH in comparison; either add those profiles to the figure or adjust the text to match the presented data.
[§IV] The framework is listed as 'iNet 4.5.2' in the text; the official name is INET Framework. Please use the correct spelling.

Circularity Check

0 steps flagged · score 1.0 of 10

Simulation study with no fitted-to-result parameters; two self-citations are peripheral, so no meaningful circularity.

full rationale

The paper's central claim is an empirical simulation result, not a derivation. End-to-end latency and HARQ values (Figs. 4-6) are generated by running OMNeT++/Simu5G with the 3GPP TR 38.901 InF path-loss formulas (Eqs. 1-8) and the 5GTQ TSN framework; no parameter is fitted to the reported latency outcomes, and no 'prediction' is obtained by inverting the simulation inputs. The InF channel model is quoted from an external standard, not from the authors' prior work. The only self-citations are [11] (a survey of wireless TSN) and [14] (a prior InF channel characterization/validation), cited for background and for the InF model respectively; the equations actually used in Section III.A are from TR 38.901, so neither citation is load-bearing for the quantitative results. The 'first to consider wireless factory environments' claim in Section I is a literature assertion, not an input to the simulation. The questionable numerology index 4 at 5.9 GHz and FlatGround environment in Table I are correctness/validity risks, not circularity: they affect whether the simulated configuration is representative, but the reported numbers are not forced by definition or by a fitted target. Overall, the derivation chain is self-contained: standard channel model plus standard simulator plus stated traffic scenarios yield the reported metrics.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities. Its results rest on standard 3GPP channel models and existing simulator frameworks; the main unverified inputs are the simulator configuration and the representativeness of the scenario.

free parameters (1)

Numerology index = 4
Hand-chosen in Table I for a 5.9 GHz carrier; not justified by 3GPP NR band definitions and directly affects slot length and hence end-to-end delay.

assumptions (4)

domain assumption 3GPP TR 38.901 InF path loss and LOS probability models accurately describe indoor factory radio propagation
Used as the basis for all channel-related results in Section III-A; no validation against factory measurements is provided.
domain assumption The 5GTQ framework and Simu5G correctly model the 5G-TSN bridge, TSN scheduling, and 5G NR protocol stack
The paper relies on these simulators for end-to-end delay and HARQ results in Section IV without independent validation.
domain assumption Single UE, single gNB, flat-ground physical environment, and no inter-cell interference is representative enough for the conclusions
Section IV, Table I sets one UE and one gNB; the conclusion generalizes to indoor factory deployments despite the simplicity.
ad hoc to paper Numerology index 4 is a valid NR configuration at 5.9 GHz
Table I lists numerology index 4 with carrier frequency 5.9 GHz, but 3GPP NR numerology 4 applies to bands above 52.6 GHz; no justification is given.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Comparative Performance Evaluation of 5G-TSN Applications in Indoor Factory Environments." pith.science (2026). https://pith.science/paper/6JW5EE6Z

@misc{pith2026250112792,
  author       = {Pith},
  title        = {Pith review of: Comparative Performance Evaluation of 5G-TSN Applications in Indoor Factory Environments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6JW5EE6Z}},
  note         = {Machine review of arXiv:2501.12792}
}

read the original abstract

While Time-Sensitive Networking (TSN) enhances the determinism, real-time capabilities, and reliability of Ethernet, future industrial networks will not only use wired but increasingly wireless communications. Wireless networks enable mobility, have lower costs, and are easier to deploy. However, for many industrial applications, wired connections remain the preferred choice, particularly those requiring strict latency bounds and ultra-reliable data flows, such as for controlling machinery or managing power electronics. The emergence of 5G, with its Ultra-Reliable Low-Latency Communication (URLLC) promises to enable high data rates, ultra-low latency, and minimal jitter, presenting a new opportunity for wireless industrial networks. However, as 5G networks include wired links from the base station towards the core network, a combination of 5G with time-sensitive networking is needed to guarantee stringent QoS requirements. In this paper, we evaluate 5G-TSN performance for different indoor factory applications and environments through simulations. Our findings demonstrate that 5G-TSN can address latency-sensitive scenarios in indoor factory environments.

Figures

Figures reproduced from arXiv: 2501.12792 by the authors.

**Figure 1.** 5G-TSN Indoor Factory Environment While TSN excels at managing latency and jitter in wired environments through bandwidth reservation and scheduling, its application to wireless networks requires careful consideration of the dynamic nature of wireless channels, including fluctuating data rates, error rates, and packet loss [11]. The most promising approach for incorporating TSN capabilities into 5G networks, as out… view at source ↗

**Figure 2.** 5G-Based MiR architecture in 5G-TSN network [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 4.** End-to-End delay with varying data rate [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

Figures from the paper (1 more)

**Figure 6.** Figure 6: End-to-End delay with different profiles and distances [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

21 extracted references · 19 canonical work pages

[14]

5g wireless channel characterization in indoor factory environments: Simulation and validation,

A. Vijayan, M. Kuhn, J. John, M. Noor-A-Rahim, D. Pesch, B. OCon- nor, K. Crean, and E. Armstrong, “5g wireless channel characterization in indoor factory environments: Simulation and validation,” in Pro- ceedings of the 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023, pp. 1–6

work page 2023
[1]

Iec/ieee 60802 - time-sensitive networking profile for industrial automation,

IEEE, “Iec/ieee 60802 - time-sensitive networking profile for industrial automation,” https://1.ieee802.org/tsn/iec-ieee-60802/, accessed: 2024- 07-19

work page 2024
[2]

Performance of integrated 3gpp 5g and ieee tsn networks,

P. M. Rost and T. Kolding, “Performance of integrated 3gpp 5g and ieee tsn networks,” IEEE Communications Standards Magazine , vol. 6, no. 2, pp. 51–56, 2022

work page 2022
[3]

5g radio network design for ultra-reliable low-latency communication,

J. Sachs, G. Wikstrom, T. Dudda, R. Baldemair, and K. Kittichokechai, “5g radio network design for ultra-reliable low-latency communication,” IEEE Network , vol. 32, no. 2, pp. 24–31, 2018

work page 2018
[4]

Distributing deterministic, accurate time for tightly coordinated network and software applications: Ieee 802.1as, the tsn profile of ptp,

K. B. Stanton, “Distributing deterministic, accurate time for tightly coordinated network and software applications: Ieee 802.1as, the tsn profile of ptp,” IEEE Communications Standards Magazine , vol. 2, no. 2, pp. 34–40, 2018

work page 2018
[5]

A period-aware routing method for ieee 802.1qbv tsn networks,

K. Huang, J. Wu, X. Jiang, D. Xiong, K. Huang, H. Yao, W. Xu, Y . Peng, and Z. Liu, “A period-aware routing method for ieee 802.1qbv tsn networks,” Electronics, vol. 10, no. 1, 2021. [Online]. Available: https://www.mdpi.com/2079-9292/10/1/58

work page 2021
[6]

Performance assessment of the ieee 802.1qch in an automotive scenario,

L. Leonardi, L. L. Bello, and G. Patti, “Performance assessment of the ieee 802.1qch in an automotive scenario,” in Proceedings of 2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) , 2020, pp. 1–6

work page 2020
[7]

Ieee 802.1 avb and its application in carrier- grade ethernet [standards topics],

G. M. Garner, F. Feng, K. D. Hollander, H. Jeong, B. Kim, B.-J. Lee, T.-C. Jung, and J. Joung, “Ieee 802.1 avb and its application in carrier- grade ethernet [standards topics],” IEEE Communications Magazine , vol. 45, no. 12, pp. 126–134, 2007

work page 2007

Show all 21 references

[8]

Scheduling real-time communication in ieee 802.1qbv time sensitive networks,

S. S. Craciunas, R. S. Oliver, M. Chmel ´ık, and W. Steiner, “Scheduling real-time communication in ieee 802.1qbv time sensitive networks,” in Proceedings of the 24th International Conference on Real-Time Networks and Systems , ser. RTNS ’16. New York, NY , USA: Association fo...

2016
[9]

Design optimization of frame preemption in real-time switched ethernet,

T. Park, S. Samii, and K. G. Shin, “Design optimization of frame preemption in real-time switched ethernet,” in Proceedings of the 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE) , 2019, pp. 420–425

2019
[10]

Enhanced mobile broadband (embb): A review,

D. M. Abdullah and S. Y . Ameen, “Enhanced mobile broadband (embb): A review,” Journal of Information Technology and Informatics , vol. 1, no. 1, pp. 13–19, 2021

2021
[11]

A comprehensive survey of wireless time-sensitive networking (TSN): Architecture, technologies, applications, and open issues,

K. Zanbouri, M. Noor-A-Rahim, J. John, C. J. Sreenan, H. V . Poor, and D. Pesch, “A comprehensive survey of wireless time-sensitive networking (TSN): Architecture, technologies, applications, and open issues,” IEEE Communications Surveys & Tutorials , pp. 1–1, 2025

2025
[12]

Work in progress: A centralized configuration model for tsn-5g networks,

Z. Satka, I. lvarez, M. Ashjaei, and S. Mubeen, “Work in progress: A centralized configuration model for tsn-5g networks,” in Proceedings of the 2022 IEEE 27th International Conference on Emerging Technolo- gies and Factory Automation (ETF A) , 2022, pp. 1–4

2022
[13]

5g; study on channel model for frequencies from 0.5 to 100 ghz (3gpp tr 38.901 version 18.0.0 release 18),

3rd Generation Partnership Project, “5g; study on channel model for frequencies from 0.5 to 100 ghz (3gpp tr 38.901 version 18.0.0 release 18),” 3rd Generation Partnership Project (3GPP), Technical Report ETSI TR 138 901 V18.0.0, 2024, retrieved from https://www.etsi.org/stand...

2024
[15]

How amazon deploys robots in its operations facilities to benefit employees and customers,

Amazon, “How amazon deploys robots in its operations facilities to benefit employees and customers,” 2024, accessed on 2024-08-22. [Online]. Available: https://www.aboutamazon.com/news/operations/ how-amazon-deploys-robots-in-its-operations-facilities

2024
[16]

A review of recent advances in automated guided vehicle technologies: Integration challenges and research areas for 5g-based smart manufacturing applications,

E. A. Oyekanlu, A. C. Smith, W. P. Thomas, G. Mulroy, D. Hitesh, M. Ramsey, D. J. Kuhn, J. D. Mcghinnis, S. C. Buonavita, N. A. Looper, M. Ng, A. Ngoma, W. Liu, P. G. Mcbride, M. G. Shultz, C. Cerasi, and D. Sun, “A review of recent advances in automated guided vehicle technol...

2020
[17]

Products - robots,

M. I. Robots, “Products - robots,” n.d. [Online]. Available: https://mobile-industrial-robots.com/products/robots
[18]

An overview of the omnet++ simulation environment,

A. Varga and R. Hornig, “An overview of the omnet++ simulation environment,” in Proceedings of the 1st International ICST Conference on Simulation Tools and Techniques for Communications, Networks and Systems. ICST, 5 2010

2010
[19]

M ´esz´aros, A

L. M ´esz´aros, A. Varga, and M. Kirsche, INET Framework . Cham: Springer International Publishing, 2019, pp. 55–106. [Online]. Available: https://doi.org/10.1007/978-3-030-12842-5 2

2019 doi
[20]

Simu5g: Simulator for 5g new radio networks,

Simu5G, “Simu5g: Simulator for 5g new radio networks,” accessed on 2024-08-23. [Online]. Available: https://simu5g.org/

2024
[21]

5gtq: Qos-aware 5g-tsn simulation framework,

R. Debnath, M. S. Akinci, D. Ajith, and S. Steinhorst, “5gtq: Qos-aware 5g-tsn simulation framework,” in Proceedings of the 2023 IEEE 98th V ehicular Technology Conference (VTC2023-Fall), 2023, pp. 1–7

2023

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [14]

5g wireless channel characterization in indoor factory environments: Simulation and validation,

A. Vijayan, M. Kuhn, J. John, M. Noor-A-Rahim, D. Pesch, B. OCon- nor, K. Crean, and E. Armstrong, “5g wireless channel characterization in indoor factory environments: Simulation and validation,” in Pro- ceedings of the 2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023, pp. 1–6

work page 2023

[2] [1]

Iec/ieee 60802 - time-sensitive networking profile for industrial automation,

IEEE, “Iec/ieee 60802 - time-sensitive networking profile for industrial automation,” https://1.ieee802.org/tsn/iec-ieee-60802/, accessed: 2024- 07-19

work page 2024

[3] [2]

Performance of integrated 3gpp 5g and ieee tsn networks,

P. M. Rost and T. Kolding, “Performance of integrated 3gpp 5g and ieee tsn networks,” IEEE Communications Standards Magazine , vol. 6, no. 2, pp. 51–56, 2022

work page 2022

[4] [3]

5g radio network design for ultra-reliable low-latency communication,

J. Sachs, G. Wikstrom, T. Dudda, R. Baldemair, and K. Kittichokechai, “5g radio network design for ultra-reliable low-latency communication,” IEEE Network , vol. 32, no. 2, pp. 24–31, 2018

work page 2018

[5] [4]

Distributing deterministic, accurate time for tightly coordinated network and software applications: Ieee 802.1as, the tsn profile of ptp,

K. B. Stanton, “Distributing deterministic, accurate time for tightly coordinated network and software applications: Ieee 802.1as, the tsn profile of ptp,” IEEE Communications Standards Magazine , vol. 2, no. 2, pp. 34–40, 2018

work page 2018

[6] [5]

A period-aware routing method for ieee 802.1qbv tsn networks,

K. Huang, J. Wu, X. Jiang, D. Xiong, K. Huang, H. Yao, W. Xu, Y . Peng, and Z. Liu, “A period-aware routing method for ieee 802.1qbv tsn networks,” Electronics, vol. 10, no. 1, 2021. [Online]. Available: https://www.mdpi.com/2079-9292/10/1/58

work page 2021

[7] [6]

Performance assessment of the ieee 802.1qch in an automotive scenario,

L. Leonardi, L. L. Bello, and G. Patti, “Performance assessment of the ieee 802.1qch in an automotive scenario,” in Proceedings of 2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE) , 2020, pp. 1–6

work page 2020

[8] [7]

Ieee 802.1 avb and its application in carrier- grade ethernet [standards topics],

G. M. Garner, F. Feng, K. D. Hollander, H. Jeong, B. Kim, B.-J. Lee, T.-C. Jung, and J. Joung, “Ieee 802.1 avb and its application in carrier- grade ethernet [standards topics],” IEEE Communications Magazine , vol. 45, no. 12, pp. 126–134, 2007

work page 2007