AI/ML Parameter Provisioning With Threshold Validation in 5G Networks
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Solution Overview
Problem
The existing 5G mobile communication systems lack mechanisms to set and enforce thresholds for AI/ML-related parameter provisioning, leading to potential suboptimal service provision due to unverified or insufficient parameter values.
Innovation Solution
A method and apparatus for AI/ML-related external parameter provision in a mobile communication system, involving a unified data manager (UDM), network function (NF), AI/ML application function (AF), network exposure function (NEF), and unified data repository (UDR), which includes determining whether to update the UDR with parameter values based on evaluation metrics and thresholds, and transmitting notifications of updated parameter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If parameter values are accepted without threshold verification, then the system operates with simpler procedures, but the service quality deteriorates due to suboptimal parameter values
Solution Approach 1:
The system performs preliminary threshold verification of parameter values before accepting them into the network. The UDM/UDR checks whether received parameter values meet predefined thresholds associated with each parameter type, ensuring that only validated parameters are stored and used for AI/ML operations, thereby preventing suboptimal service provision from the outset
Solution Approach 2:
The system implements feedback mechanisms where the UDM notifies the NF about accepted or rejected parameter values, and the NF can subscribe to notifications about parameter value changes. This feedback loop ensures that the system maintains awareness of parameter quality and can respond appropriately to parameter provisioning events
2Reliability
If threshold verification is implemented for parameter values, then the service quality improves, but the system complexity increases
Solution Approach 1:
The UDM/UDR system performs self-service by automatically verifying parameter values against predefined thresholds without requiring external validation. The system maintains its own threshold configurations and autonomously determines whether to accept or reject received parameter values, reducing the need for complex external verification mechanisms
Solution Approach 2:
The UDM and UDR components perform multiple functions: they not only store parameter values but also verify them against thresholds, manage threshold configurations, and provide notification services. This multi-functionality reduces the need for separate dedicated verification systems, thereby managing complexity while maintaining reliability
Data Source
AI summary
The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. For Artificial Intelligence/Machine Learning (AI/ML)-related external parameter provision in a mobile communication system comprising a unified data manager (UDM), a network function (NF), an AI/ML application function (AF), a network exposure function (NEF), a unified data repository (UDR), and one or more user equipment (UE), a method includes receiving, at the UDM from the NF, a subscribe request including a request for a parameter, receiving, at the UDM from the AI/ML AF via the NEF, a parameter provision request including a parameter value for the parameter and an evaluation metric associated with the parameter value, determining, by the UDM, whether to update the UDR with the parameter value based on a threshold associated with the parameter and the evaluation metric, and if it is determined to update the UDR, updating, by the UDR, the UDR with the parameter value, and transmitting, by the UDM to the NF, a notification of the updated parameter value.