Enhancing 5G Core with Multi-Access Edge Computing

This research employs NYCU-developed open source 5G core network \boldsymbol{free5GC} and Intel open source edge computing platform OpenNESS to build a 5G private network. An online multi-person chorus application is then deployed on the edge platform to (1) achieve High Reliability and Low Latency...

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Main Authors: Lee, Ho-Cheng, Lin, Fuchun Joseph, Chen, Jyh-Cheng, Chen, Chien, Wang, Patrick
Format: Conference Proceeding
Language:English
Subjects:
Core Network
Edge Computing
Low Latency
NFV
Traffic Influence
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Lin, Fuchun Joseph
Chen, Jyh-Cheng
Chen, Chien
Wang, Patrick
description This research employs NYCU-developed open source 5G core network \boldsymbol{free5GC} and Intel open source edge computing platform OpenNESS to build a 5G private network. An online multi-person chorus application is then deployed on the edge platform to (1) achieve High Reliability and Low Latency Communication (URLLC) requirements, and (2) improve the backhaul bandwidth occupancy rate from the edge to the core network. In addition, this research implements the traffic influence function proposed in the 3GPP 5G standards, which can dynamically change traffic rules of the 5G core during execution, directing specific traffic to the edge applications in order to improve the performance of private networks. Finally, to verify the effectiveness of this schema, this research uses the example application deployed to compare the performance of the system equipped with edge computing with that without edge platform. Our analysis is done with both a physical RAN and the UERANSIM simulator.
doi_str_mv 10.1109/WOCC58016.2023.10139748
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subjects Core Network
Edge Computing
Low Latency
NFV
Traffic Influence
title Enhancing 5G Core with Multi-Access Edge Computing
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