Pre-deployment Validation for AI/ML Two-Sided Models
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Solution Overview
Problem
Ensuring interoperability and testability of AI/ML-enabled features with two-sided models deployed at both user equipment (UE) and network (NW) is challenging due to differences in model sources, input data, versions, update cycles, and lack of coordination mechanisms, which complicates end-to-end validation and performance evaluation.
Innovation Solution
Implementing a pre-deployment validation sequence and dedicated test mode that involves exchanging messages between the device under test (DUT) and the test equipment (TE) to request and validate AI/ML models from repositories, ensuring interoperability and testability by determining model compatibility and performance based on key performance indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If two-sided AI/ML models are deployed at both UE and network with different model sources and versions, then model functionality and versatility are improved, but interoperability and testability become difficult to ensure
Solution Approach 1:
The patent implements a pre-deployment validation sequence that executes before actual deployment to verify model compatibility. This preliminary action checks model versions, input/output formats, and performance characteristics to ensure interoperability between UE and network models, preventing compatibility issues from arising in the deployed system.
Solution Approach 2:
The validation mechanism establishes feedback loops where model performance metrics and compatibility results are communicated back to the deployment system. This feedback enables automatic rejection of incompatible model pairs and triggers re-validation when model updates occur, maintaining reliability despite version changes.
2Reliability
If comprehensive model validation is performed before deployment, then model compatibility and performance are ensured, but validation time and complexity increase
Solution Approach 1:
The validation process is divided into separate stages: model registration, compatibility checking, performance validation, and approval. Each stage focuses on specific aspects of model quality, allowing parallel processing of different validation tasks and reducing overall validation time through structured segmentation of the validation workflow.
3Adaptability or versatility
If multiple AI/ML models are managed with different update cycles, then model adaptability is improved, but coordination and synchronization become complex
Solution Approach 1:
The patent introduces a model repository and validation server as intermediary components between UE and network models. These intermediaries centralize model storage, version management, and compatibility verification, simplifying the coordination of multiple models with different update cycles by providing a single point of control and validation.
Data Source
AI summary
An apparatus configured to: enter a test mode; receive, from a test equipment, an indication of at least one first model used at a first side of a two-sided model; request, from a model repository, at least one second model for use at a second side of the two-sided model, wherein the at least one second model is selected based, at least partially, on the at least one first model; and receive, from the model repository, the at least one second model. An apparatus configured to: receive, from a test equipment, a model validation request; provide a data set to at least one model to obtain an inference validation response, wherein the at least one model is used at a first side of a two-sided model; transmit, to the test equipment, the inference validation response; and receive, from the test equipment, a validation sequence result.