UE Positioning Measurement Selection Using ML Channel Analysis
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Solution Overview
Problem
The existing 5G positioning protocols require UEs to measure and report back timing and angular measurements for all detectable beams of TRPs, which is impractical due to the large number of measurements needed, especially at higher carrier frequencies, leading to excessive power consumption and complexity.
Innovation Solution
A UE-centric method using machine learning classifiers to determine when and which subset of positioning measurements are necessary by analyzing channel metrics and environmental changes, minimizing the need for repeated measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the UE measures and reports timing and angular measurements for all detectable beams of all TRPs as mandated by the standard, then positioning accuracy is improved, but power consumption and device complexity increase excessively
Solution Approach 1:
The patent applies partial action by having the UE measure and report positioning metrics for only a selected subset of TRPs rather than all detectable TRPs. The UE determines this subset based on criteria such as signal strength thresholds, movement detection, or network configuration, thereby performing fewer measurements while still achieving adequate positioning accuracy for the scenario.
Solution Approach 2:
The patent segments the set of all detectable TRPs into multiple groups or subsets. The UE measures positioning metrics for TRPs in different subsets at different times or based on different criteria, rather than measuring all TRPs simultaneously. This segmentation reduces the measurement burden while maintaining comprehensive positioning coverage.
2Measurement precision
If the UE measures and reports timing and angular measurements for all detectable beams of all TRPs, then positioning accuracy is improved, but device complexity increases excessively
Solution Approach 1:
The patent applies partial action by having the UE measure and report positioning metrics for only a selected subset of TRPs rather than all detectable TRPs. The UE determines this subset based on criteria such as signal strength thresholds, movement detection, or network configuration, thereby performing fewer measurements while still achieving adequate positioning accuracy for the scenario.
Solution Approach 2:
The patent segments the set of all detectable TRPs into multiple groups or subsets. The UE measures positioning metrics for TRPs in different subsets at different times or based on different criteria, rather than measuring all TRPs simultaneously. This segmentation reduces the measurement burden while maintaining comprehensive positioning coverage.
3Speed
If the LMF requests positioning measurements at each positioning session, then positioning responsiveness is improved, but the number of measurements and overhead increase impractically
Solution Approach 1:
The patent applies preliminary action by having the UE proactively determine and report positioning metrics for a subset of TRPs before the LMF requests them, or by maintaining measurement state information that can be quickly reported when needed. The UE uses local decision-making based on channel conditions and movement detection to prepare measurement data in advance, reducing the need for repeated full measurements at each LMF request.
Solution Approach 2:
The patent implements feedback mechanisms where the UE monitors channel conditions, movement state, and previous measurement results to dynamically adjust which TRPs require measurement and reporting. This feedback loop allows the UE to maintain positioning responsiveness by reporting only when necessary, reducing the overall number of measurements while maintaining readiness for quick positioning updates.
Data Source
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AI summary
The embodiments relate to an apparatus and a method, where the apparatus comprises means for listening and receiving a positioning measurement request from a location server at a first point of time; means for determining whether a set of positioning metrics need to be measured, wherein the determining is based on an output from a machine learning classifier to which channel metrics between two points of time is given as input; whether the output indicates a new measurement, means for performing the measurement of positioning metrics from all transmission points; or if not, means for determining which subset of transmission points needs to be measured and means for performing the measurement of such subset of transmission points; and means for reporting the measured positioning metrics to the location server.