@inproceedings{e8af374f12ec4acb96d451fd66488163,
title = "Enhancing 5G Core with Multi-Access Edge Computing",
abstract = "This research employs NYCU-developed open source 5G core network 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.",
keywords = "5G, Core Network, Edge Computing, Low Latency, NFV, Traffic Influence",
author = "Lee, {Ho Cheng} and Lin, {Fuchun Joseph} and Chen, {Jyh Cheng} and Chien Chen and Patrick Wang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 32nd Wireless and Optical Communications Conference, WOCC 2023 ; Conference date: 05-05-2023 Through 06-05-2023",
year = "2023",
doi = "10.1109/WOCC58016.2023.10139748",
language = "English",
series = "32nd Wireless and Optical Communications Conference, WOCC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "32nd Wireless and Optical Communications Conference, WOCC 2023",
address = "美國",
}