A Sensitivity Analysis on the Potential of 5G Channel Quality Prediction
| Autor: | Sabari Nathan Anbalagan, Hans van den Berg, Kallol Das, R. Litjens, Alessandro Chiumento, Paul J.M. Havinga |
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| Přispěvatelé: | Pervasive Systems, Digital Society Institute, Design and Analysis of Communication Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Percentile
Network complexity Computer science business.industry Quality of service 020206 networking & telecommunications 020302 automobile design & engineering Throughput 02 engineering and technology Factories of the Future Reliability engineering Networking 0203 mechanical engineering Industrial IoT 0202 electrical engineering electronic engineering information engineering Wireless Latency (engineering) business Wireless sensor network 5G Communication channel |
| Zdroj: | VTC Spring 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) |
| ISSN: | 1550-2252 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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