@inproceedings{8b21929c5fbe4fe6bab4c83296d8a8da,
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 Paul Havinga and {van den Berg}, Hans",
year = "2021",
doi = "10.1109/VTC2021-Spring51267.2021.9448944",
language = "English",
isbn = "978-1-7281-8965-9",
series = "IEEE Vehicular Technology Conference VTC",
publisher = "IEEE",
booktitle = "2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)",
address = "United States",
note = "93rd IEEE Vehicular Technology Conference, VTC 2021-Spring ; Conference date: 25-04-2021 Through 28-04-2021",
}