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

Conference paper (2021)

Authors

Sabari Nathan Anbalagan University of Twente
R. Litjens TNO, Network Architectures and Services - EEMCS
Alessandro Chiumento University of Twente
P. Havinga University of Twente
Hans van den Berg TNO, University of Twente
Research Group
Network Architectures and Services (EEMCS) (TU Delft)
Networking 5G Factories of the Future Industrial IoT
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Quantum & Computer Engineering

Research Group

Network Architectures and Services

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.