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

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

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)

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 languageEnglish
Title of host publication2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)
Subtitle of host publicationProceedings
Place of PublicationPiscataway
PublisherIEEE
Number of pages5
ISBN (Electronic)978-1-7281-8964-2
ISBN (Print)978-1-7281-8965-9
DOIs
Publication statusPublished - 2021
Event93rd IEEE Vehicular Technology Conference, VTC 2021-Spring - Virtual, Online
Duration: 25 Apr 202128 Apr 2021

Conference

Conference93rd IEEE Vehicular Technology Conference, VTC 2021-Spring
CityVirtual, Online
Period25/04/2128/04/21

Keywords

  • 5G
  • Factories of the Future
  • Industrial IoT
  • Networking

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