CSI Feedback ML Lifecycle Monitoring for Reliable Status Reporting
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Solution Overview
Problem
Determining the operational status of machine learning (ML) functionality or models for channel state information (CSI) feedback enhancement in wireless communication systems is complex and challenging, necessitating improved life cycle management (LCM) procedures to ensure reliable performance.
Innovation Solution
Implementing an ML functionality-based or model-identifier (ID)-based LCM procedure, where a UE transmits capability signaling for CSI feedback enhancement, performs monitoring, and reports status changes to the network, allowing for configuration adjustments such as de-activation or fallback to improve system reliability and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ML functionality or model is deployed for CSI feedback enhancement, then system performance is improved, but complexity of determining operational status increases
Solution Approach 1:
The patent segments the LCM procedure into distinct components: capability signaling, monitoring configuration, status detection, and reporting. Each component handles a specific aspect of ML functionality management, reducing overall procedural complexity while maintaining reliability.
Solution Approach 2:
The patent implements preliminary action by having the UE transmit capability signaling before actual ML functionality deployment. The network entity configures monitoring parameters in advance, and establishes fallback mechanisms beforehand, allowing for smoother operation and easier status determination.
2Reliability
If ML model monitoring is implemented to detect failure status, then system reliability is improved, but measurement and detection difficulty increases
Solution Approach 1:
The patent implements feedback mechanisms where the UE continuously monitors ML functionality performance and reports status changes to the network entity. The network entity provides configuration feedback and can trigger reconfiguration based on reported status, creating a closed-loop system that simplifies detection while maintaining reliability.
Solution Approach 2:
The UE performs self-monitoring of ML functionality status using configured parameters and autonomously determines when status changes occur. This self-service approach reduces the burden on the network entity and simplifies the overall detection process while ensuring reliable status tracking.
3Reliability
If capability signaling and monitoring configuration are added, then ML functionality reliability is improved, but communication overhead increases
Solution Approach 1:
The patent makes the capability signaling and monitoring configuration universal by designing them to work across different ML functionality types and deployment scenarios. This multi-functionality allows the same signaling framework to handle various ML models and monitoring requirements, reducing the need for additional specialized signaling and thereby controlling overhead.
Data Source
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AI summary
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for machine Learning for CSI feedback enhancement in wireless communication. A UE (102) receives (206), from a network entity (104), a first control signaling indicating an LCM configuration for at least one of an ML functionality or an ML model associated with a CSI report. The LCM configuration indicates an LCM metric. The UE (102) receives (210), from the network entity (104), a second control signaling indicating at least one CSI reference signal (CSI-RS) resource for monitoring the LCM metric. The UE (102) transmits (214), to the network entity (104), an indication of a status change of the at least one of the ML functionality or the ML model associated with the CSI report. The indication is based on the at least one CSI-RS resource for monitoring the LCM metric.