AI/ML Air Interface Dataset Identification for Context Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Challenges arise in aligning UE and network node configurations and radio characteristics during AI/ML air interface use cases, as certain configurations are not explicitly indicated or exposed, leading to difficulties in matching datasets with the correct context for model training and activation.
Innovation Solution
Techniques for identifying datasets with dataset identifiers that associate UE and network node configurations and radio characteristics without explicit indication, enabling coordinated lifecycle management actions for AI/ML air interface use cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If configurations are not explicitly indicated or exposed, then security and system simplicity are improved, but dataset matching accuracy and context alignment deteriorate
Solution Approach 1:
The patent introduces dataset identifiers as intermediary elements that indirectly represent configuration contexts without exposing the actual configurations. These identifiers act as mediators between the hidden configuration state and the dataset selection process, enabling accurate matching while maintaining configuration privacy and system simplicity.
Solution Approach 2:
The patent creates simplified copies of configuration contexts in the form of dataset identifiers. These identifiers capture the essential contextual information needed for dataset matching without replicating the full configuration details, thus enabling accurate matching while minimizing information exposure and maintaining system efficiency.
2Productivity
If dataset identification techniques are implemented, then model training efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements preliminary assignment of dataset identifiers to configurations during data collection sessions. This pre-establishment of identifier-configuration mappings enables rapid dataset retrieval and matching during model training without requiring complex real-time analysis, thus improving training efficiency while keeping the additional system complexity manageable through structured pre-processing.
3Speed
If dataset identifiers are assigned and associated with configurations, then context matching speed is improved, but information management complexity increases
Solution Approach 1:
The patent segments the configuration information into discrete, identifiable units by assigning unique dataset identifiers to specific configuration contexts. This segmentation enables rapid identification and matching of relevant datasets by working with compact identifier references rather than analyzing full configuration details, thus improving matching speed while managing information complexity through modular organization.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may collect a dataset during a data collection session associated with an artificial intelligence or machine learning (AI/ML) air interface use case. The UE may receive, from a network node, a dataset identifier assigned to the data collection session associated with the AI/ML air interface use case. The UE may associate the dataset identifier with the dataset and with one or more configurations associated with the UE during the data collection session. Numerous other aspects are described.