name: AI/ML
full_form: Artificial Intelligence/Machine Learning
context: Finalization of representative sub-use cases: The following are selected as representative sub-use cases: - Direct AI/ML positioning: - AI/ML model output: UE location - e.g., fingerprinting based on channel observation as the input of AI/ML model - AI/ML assisted positioning: - AI/ML model output: new measurement and/or enhancement of existing measurement - e.g., LOS/NLOS identification, timing and/or angle of measurement, likelihood of measurement More specifically, the following Cases are considered for the study: - Case 1: UE-based positioning with UE-side model, direct AI/ML or AI/ML assisted positioning - Case 2a: UE-assisted/LMF-based positioning with UE-side model, AI/ML assisted positioning - Case 2b: UE-assisted/LMF-based positioning with LMF-side model, direct AI/ML positioning - Case 3a: NG-RAN node assisted positioning with gNB-side model, AI/ML assisted positioning - Case 3b: NG-RAN node assisted positioning with LMF-side model, direct AI/ML positioning One-sided model
description: Artificial Intelligence/Machine Learning (AI/ML) refers to the standards governing telecommunications operations that utilize machine learning and artificial intelligence techniques for various purposes such as positioning accuracy enhancements in 5G networks.
explanation: The context explicitly mentions AI/ML as 'standards governing telecommunications operations', indicating its role as a set of rules and guidelines defining how AI/ML is implemented within the telecommunications industry.
section_title: 5.3Positioning accuracy enhancements
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