A Sensitivity Analysis on the Potential of 5G Channel Quality Prediction

Sabari Nathan Anbalagan; Remco Litjens; Kallol  Das; Alessandro Chiumento; Paul J.M. Havinga; Hans Leo van den Berg

doi:10.1109/VTC2021-Spring51267.2021.9448944

A Sensitivity Analysis on the Potential of 5G Channel Quality Prediction

Sabari Nathan Anbalagan, Remco Litjens, Kallol Das, Alessandro Chiumento, Paul J.M. Havinga, Hans Leo van den Berg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

1 Citation (Scopus)

85 Downloads (Pure)

Abstract

With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10th user throughput percentile by up to 125% for eMBB use case and decreases the 90th resource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.

Original language	English
Title of host publication	2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)
Place of Publication	Piscataway, NJ
Publisher	IEEE
ISBN (Electronic)	978-1-7281-8964-2
ISBN (Print)	978-1-7281-8965-9
DOIs	https://doi.org/10.1109/VTC2021-Spring51267.2021.9448944
Publication status	Published - 15 Jun 2021
Event	IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Online Event Duration: 25 Apr 2021 → 28 Apr 2021 Conference number: 93 https://events.vtsociety.org/vtc2021-spring/

Publication series

Name	IEEE Vehicular Technology Conference, VTC
Publisher	IEEE
Volume	2021
ISSN (Print)	1550-2252
ISSN (Electronic)	2577-2465

Conference

Conference	IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring
Abbreviated title	VTC2021-Spring
City	Online Event
Period	25/04/21 → 28/04/21
Internet address	https://events.vtsociety.org/vtc2021-spring/

Keywords

5G
Factories of the Future
Industrial IoT
Networking

Access to Document

10.1109/VTC2021-Spring51267.2021.9448944

Anbalagan2021sensitivityAccepted author version, 2.98 MB

Cite this

Anbalagan, S. N., Litjens, R., Das, K., Chiumento, A., Havinga, P. J. M., & van den Berg, H. L. (2021). A Sensitivity Analysis on the Potential of 5G Channel Quality Prediction. In 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) [9448944] (IEEE Vehicular Technology Conference, VTC; Vol. 2021). IEEE. https://doi.org/10.1109/VTC2021-Spring51267.2021.9448944

@inproceedings{0783d9722f7a423ea620f67e86d28839,

title = "A Sensitivity Analysis on the Potential of 5G Channel Quality Prediction",

abstract = "With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10th user throughput percentile by up to 125% for eMBB use case and decreases the 90th resource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.",

keywords = "5G, Factories of the Future, Industrial IoT, Networking",

author = "Anbalagan, {Sabari Nathan} and Remco Litjens and Kallol Das and Alessandro Chiumento and Havinga, {Paul J.M.} and {van den Berg}, {Hans Leo}",

note = "Funding Information: The authors would like to thank Maria Raftopoulou of TU Delft for generating the utilized BLER curves with The Vienna 5G Link Level Simulator [15] and Daan Middelkoop of the University of Twente for his analysis (even if excluded from the final version of the paper). This publication is funded partly by the Dutch Research Council (NWO) within the research programme Smart Industries under the project SUPREME (project No. 15467) and by the EU H2020 Research and Innovation Programme under the Clear5G project (grant agreement No. 761745).” Publisher Copyright: {\textcopyright} 2021 IEEE.; IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring, VTC2021-Spring ; Conference date: 25-04-2021 Through 28-04-2021",

year = "2021",

month = jun,

day = "15",

doi = "10.1109/VTC2021-Spring51267.2021.9448944",

language = "English",

isbn = "978-1-7281-8965-9",

series = "IEEE Vehicular Technology Conference, VTC",

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url = "https://events.vtsociety.org/vtc2021-spring/",

}

Anbalagan, SN, Litjens, R, Das, K, Chiumento, A , Havinga, PJM & van den Berg, HL 2021, A Sensitivity Analysis on the Potential of 5G Channel Quality Prediction. in 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)., 9448944, IEEE Vehicular Technology Conference, VTC, vol. 2021, IEEE, Piscataway, NJ, IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring, Online Event, 25/04/21. https://doi.org/10.1109/VTC2021-Spring51267.2021.9448944

A Sensitivity Analysis on the Potential of 5G Channel Quality Prediction. / Anbalagan, Sabari Nathan; Litjens, Remco; Das, Kallol et al.

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring). Piscataway, NJ : IEEE, 2021. 9448944 (IEEE Vehicular Technology Conference, VTC; Vol. 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Chiumento, Alessandro

AU - Havinga, Paul J.M.

AU - van den Berg, Hans Leo

N1 - Conference code: 93

PY - 2021/6/15

Y1 - 2021/6/15

N2 - With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10th user throughput percentile by up to 125% for eMBB use case and decreases the 90th resource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.

AB - With increasing network complexity, intelligent mechanisms to efficiently achieve the required quality of service of wireless-enabled applications are being developed, especially for industrial environments due to the onset of the fourth industrial revolution. In this paper, the potential benefits of wireless channel quality prediction for two of the three major use cases supported by 5G viz. enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communication (URLLC) are quantified in an industrial indoor environment through simulations. Our analysis shows that the ability to perform perfect prediction improves the 10th user throughput percentile by up to 125% for eMBB use case and decreases the 90th resource utilization percentile by up to 37% for URLLC use case. Furthermore, the maximum tolerable prediction inaccuracy is found to be up to 5 dB and 0.35 dB for eMBB and URLLC use cases, respectively.

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Y2 - 25 April 2021 through 28 April 2021

ER -

A Sensitivity Analysis on the Potential of 5G Channel Quality Prediction

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