ML Positioning Model Failure Signaling in Wireless Networks
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately monitoring and reporting the performance of machine learning positioning models, particularly in coordinating life cycle management and ensuring reliable positioning services.
Innovation Solution
A method and system for monitoring the performance of a machine learning positioning model in a wireless communication network, where a first network entity (e.g., a RAN node) continuously assesses the model's performance based on predefined parameters and transmits a failure indication to a second network entity (e.g., a location server) when a predetermined number of failures occur within a specified time frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning positioning models are deployed for accurate positioning, then positioning accuracy is improved, but system reliability deteriorates due to model failures and performance degradation
Solution Approach 1:
The patent implements a feedback mechanism where the first network entity continuously monitors the performance of the machine learning positioning model and reports status information (including failure indications) to the second network entity. This feedback loop enables the system to detect model failures and trigger appropriate actions (such as model reconfiguration or fallback to traditional positioning methods), thereby maintaining system reliability while using ML models for positioning.
Solution Approach 2:
The patent establishes a failure monitoring and reporting mechanism that proactively identifies model performance degradation before complete system failure occurs. By setting up failure criteria and continuous monitoring, the system prepares compensatory measures (alternative positioning methods) in advance, cushioning against the potential harm of model failures and ensuring continuous reliable positioning service.
2Reliability
If continuous monitoring of machine learning model performance is implemented, then system reliability is improved, but device complexity increases
Solution Approach 1:
The patent divides the monitoring function into distinct components: the first network entity performs local performance monitoring and status determination, while the second network entity receives reports and makes high-level decisions. This segmentation distributes complexity across multiple entities, with each handling specific tasks, thereby reducing the complexity burden on any single component while achieving comprehensive monitoring coverage.
Solution Approach 2:
The first network entity autonomously monitors the performance of the machine learning positioning model and determines whether failure criteria are met, without requiring constant external intervention. This self-service monitoring approach simplifies the overall system architecture by enabling local entities to manage their own performance assessment and reporting.
3Measurement precision
If failure monitoring with maximum failure thresholds is implemented, then positioning accuracy is maintained through quick failure detection, but loss of time occurs due to failure reporting and timer management
Solution Approach 1:
The patent pre-configures failure criteria, maximum failure thresholds, and timer parameters before deploying the machine learning positioning model. By establishing these thresholds and monitoring rules in advance, the system can quickly determine when failures occur without requiring complex real-time analysis, thereby minimizing the time loss associated with failure detection and reporting while maintaining positioning accuracy.
Data Source
AI summary
Disclosed are techniques for communication. In an aspect, a first network entity monitors performance of a machine learning positioning model based on one or more parameters, wherein the machine learning positioning model is used for positioning a user equipment (UE), and transmits, to a second network entity, a failure indication for the machine learning positioning model based on a number of failures of the machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer.