Edge Computing Network UPF Deployment for 5G Latency
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Solution Overview
Problem
The 5G network architecture faces challenges in meeting the ultra-low latency and high data capacity requirements for services like autonomous driving, as existing edge computing deployment scenarios struggle to optimize end-to-end latency and Quality of Service (QoS) across diverse geographical locations.
Innovation Solution
The implementation of edge computing networks involves deploying User Plane Functions (UPF) and local Data Networks (DN) in strategic locations, facilitated by Operations Support Systems (OSS) that manage communication between 3GPP and non-3GPP network elements, using network slicing technology to ensure QoS targets are met, and dynamic re-routing based on RAN condition data to maintain service reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If edge computing networks are deployed to reduce end-to-end latency, then service reliability improves, but network complexity increases
Solution Approach 1:
The patent segments the network into multiple edge computing sites distributed across different geographical locations. Each site operates as an independent network slice with its own UPF and local DN, allowing latency-sensitive services to be processed locally while non-sensitive services use centralized infrastructure, thus reducing overall network complexity while improving service reliability.
Solution Approach 2:
The patent implements dynamic service routing that can switch between centralized and distributed edge computing modes based on service requirements. The system dynamically selects appropriate edge sites and configures network slices in real-time, enabling adaptive complexity management while maintaining high service reliability through multiple deployment options.
2Loss of time
If UPF and local DN are deployed in strategic locations, then end-to-end latency is reduced, but deployment cost increases
Solution Approach 1:
The patent designs edge computing sites with multi-functional UPF and local DN configurations that can serve multiple services simultaneously. A single edge site deployment can support various latency-sensitive applications (autonomous driving, remote surgery, industrial control) through network slicing, thereby reducing per-service deployment costs while achieving ultra-low latency for multiple services.
Solution Approach 2:
The patent implements selective deployment where edge computing resources are strategically placed only in locations and configurations necessary for specific service requirements. Not all services require edge deployment - the system applies local quality optimization only where latency is critical, reducing overall deployment costs while achieving ultra-low latency where needed.
3Reliability
If network slicing technology is used to ensure QoS targets, then service quality improves, but system complexity increases
Solution Approach 1:
The patent implements self-service network slicing where the system automatically configures and manages network slices based on service requirements without manual intervention. The OSS automatically provisions UPF, configures local DN, and establishes network slices when services are deployed, reducing operational complexity while maintaining high QoS through automated resource allocation and management.
Data Source
AI summary
Techniques discussed herein can facilitate edge computing in connection with a variety of deployment scenarios. Various embodiments can facilitate one or more of: deploying UPF(s) (User Plane Function(s)) to support edge computing; removing UPF(s) not needed for edge computing; deploying local DN(s) (Data Network(s)); E2E (Edge-to-Edge) OSS (Operations Support System) deployment scenarios; and providing RAN (Radio Access Network) condition data to support various applications (e.g., autonomous driving).