RIC-Based Dynamic AMF Selection for 5G Signaling and Mobility
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Solution Overview
Problem
Traditional methods of core network function selection in disaggregated 5G RANs are ineffective due to increasing network complexity, varying IoT use cases, and the need for dynamic reconfiguration, leading to inefficiencies such as inordinate signaling load, degraded quality of service, and operator overhead.
Innovation Solution
Implementing a RAN intelligent controller (RIC)-based dynamic AMF selection and auto-discovery mechanism that considers system attributes and end-user service benefits, enabling robust and optimal AMF selection through AI/ML algorithms, and providing a global view of network resources for intelligent signaling and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional static methods are used for core network function selection in disaggregated 5G RANs, then implementation simplicity is maintained, but network efficiency deteriorates due to inordinate signaling load and degraded quality of service
Solution Approach 1:
The patent introduces a RAN Intelligent Controller (RIC) as an intermediary component that mediates between the RAN elements and the core network. The RIC collects information from RAN elements, processes it using AI/ML algorithms, and makes intelligent decisions about AMF selection. This intermediary resolves the contradiction by adding complexity only where needed (in the RIC) while keeping the overall system efficient, as the RIC centralizes the intelligence required to reduce signaling load and improve QoS without requiring complex changes throughout the entire network.
Solution Approach 2:
The patent dynamically changes the parameters used for AMF selection based on real-time network conditions. Instead of using static selection criteria, the RIC employs AI/ML algorithms that adapt selection parameters such as AMF pool identification, tracking area code, and routing decisions based on current network state, traffic patterns, and service requirements. This dynamic parameter adjustment improves network efficiency while the automation of this process through AI/ML prevents the complexity from becoming unmanageable.
2Adaptability or versatility
If dynamic reconfiguration is implemented to accommodate varying IoT use cases and network complexity, then adaptability improves, but operator overhead increases
Solution Approach 1:
The patent implements self-service through automated AMF selection and discovery mechanisms driven by AI/ML algorithms in the RIC. The system automatically adapts to varying IoT use cases and network conditions without requiring manual operator intervention. The RIC autonomously processes network information, makes selection decisions, and reconfigures routing as needed. This self-service capability provides high adaptability to different scenarios while significantly reducing operator overhead, as the system manages its own complexity automatically.
Solution Approach 2:
The patent performs preliminary actions by pre-configuring the RIC with AI/ML models and selection policies that enable automatic adaptation to various IoT use cases. The RIC is prepared in advance with the intelligence needed to handle different network scenarios, device types, and service requirements. When network conditions change or new devices connect, the pre-configured systems automatically apply appropriate selection strategies without requiring operators to manually reconfigure anything, thus maintaining adaptability while minimizing operational burden.
3Reliability
If static node level configurations are used for AMF selection, then configuration simplicity is maintained, but service quality deteriorates due to inability to handle mobility triggers and dynamic conditions
Solution Approach 1:
The patent implements feedback mechanisms where the RIC continuously collects information from RAN elements about network conditions, device mobility, and service requirements. This feedback loop enables the AI/ML algorithms to dynamically adjust AMF selection decisions in real-time. The RIC monitors the performance and outcomes of selection decisions, using this feedback to refine future selections. This feedback-driven approach ensures high service assurance and reliability by adapting to mobility triggers and dynamic conditions, while the centralized feedback processing in the RIC manages the complexity rather than distributing it throughout the network.
4Productivity
If AI/ML algorithms are deployed for intelligent AMF selection, then resource utilization optimization is achieved, but implementation complexity increases
Solution Approach 1:
The patent employs a universal RIC platform that can deploy and manage AI/ML algorithms for multiple functions including AMF selection, load balancing, and mobility management. The RIC serves as a multi-functional intelligence hub that handles various network optimization tasks through configurable AI/ML models. This universal approach improves resource utilization efficiency by applying intelligent algorithms across multiple network functions, while the standardized RIC platform reduces deployment complexity compared to implementing separate AI/ML solutions for each function. The RIC provides a unified interface and management layer that simplifies the deployment process despite the sophistication of the underlying algorithms.
Data Source
AI summary
A near real time RAN intelligent controller (near-RT RIC) dynamically selects an AMF for a UE by receiving over an E2 interface with a CU an AMF selection request in connection with a UE; generating, in response to the AMF selection request, an AMF selection; and sending to the CU over the E2 interface the AMF selection to cause the CU to initiate NGAP signaling with an AMF identified in the AMF selection.