AI/ML Model Renewal for Network Context Adaptation
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Solution Overview
Problem
Machine learning models used in user equipment (UE) and network functions (NF) become unsuitable when the UE moves away from the network where the models were trained, leading to performance degradation due to changes in network context, with no existing mechanisms to detect or address this mismatch.
Innovation Solution
Implement mechanisms to detect mismatches between pre-trained AI/ML models and current network contexts, allowing for model renewal by retraining the models with new data from the current context, using AI/ML monitoring models to profile datasets and evaluate performance, and updating models when performance thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a pre-trained AI/ML model is deployed in a UE, then the model can provide accurate predictions and actions for the network context where it was trained, but the model performance degrades when the UE moves to a different network with different measurement data characteristics
Solution Approach 1:
The patent implements dynamic model adaptation by enabling UEs to detect when network context changes occur and automatically request updated AI/ML models from the network. The system transitions from a static pre-trained model to a dynamic system where models can be renewed based on detected context changes, ensuring continued accuracy across different network environments
Solution Approach 2:
The patent establishes a feedback mechanism where the UE monitors network measurement data characteristics, compares them against the training data profile, and triggers model renewal requests when mismatches are detected. This closed-loop feedback ensures the model remains adapted to current network conditions
2Reliability
If the UE continuously monitors network context changes to detect model mismatch, then the model can be renewed timely to maintain performance, but the complexity of the system increases due to additional monitoring and detection mechanisms
Solution Approach 1:
The patent implements self-service by enabling the UE to autonomously monitor its own network context, detect mismatches between current and training data characteristics, and automatically request model renewals without requiring complex external monitoring infrastructure. The UE serves itself in detecting when model renewal is needed
Solution Approach 2:
The patent introduces an intermediary mechanism where the UE acts as a mediator between the network and the AI/ML model lifecycle. The UE monitors context changes and communicates renewal needs to the network, simplifying the overall system architecture by distributing the monitoring function to the edge device
Data Source
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AI summary
There are provided measures for machine learning model renewal. Such measures exemplarily comprise receiving a first machine learning model message including a first machine learning inference model, obtaining network related input data, feeding said first machine learning inference model with said network related input data, receiving, upon unsuitability of said first machine learning inference model for an experienced network condition, a second machine learning model message including a second machine learning inference model, and replacing said first machine learning inference model with said second machine learning inference model.