UE Beam Prediction Monitoring With Reference Signal Feedback
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Solution Overview
Problem
Existing communication networks face challenges in effectively monitoring and managing beam prediction due to dependencies on UE measurements, misalignment in UE-NW configurations, and unstable channel conditions, leading to unreliable performance monitoring and prediction failures.
Innovation Solution
Implementing a user equipment (UE) with an inference module for beam prediction using machine learning models, coupled with performance monitoring and failure detection modules to detect and respond to prediction failures, allowing for adaptive adjustments and reporting to the network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If performance monitoring is implemented using UE measurements, then beam prediction can be monitored, but the monitoring becomes unreliable due to configuration misalignment and unstable channel conditions
Solution Approach 1:
The patent implements a feedback mechanism where the UE monitors performance of beam prediction using reference signals and reports performance metrics or failure indications back to the network entity. This closed-loop feedback enables the network to detect when performance monitoring fails and take corrective actions, thereby improving reliability while maintaining manageable complexity through structured reporting protocols.
Solution Approach 2:
The patent introduces an intermediary performance monitoring mechanism that uses reference signals as a mediator between the beam prediction process and the evaluation system. By monitoring reference signal quality and using it as an intermediate indicator, the system can assess beam prediction performance indirectly, reducing the complexity of direct measurement while improving reliability through a stable reference point.
2Measurement precision
If beam prediction is performed continuously, then prediction accuracy can be maintained, but system resources are consumed and failures may occur without detection
Solution Approach 1:
The patent implements periodic performance monitoring where the UE evaluates beam prediction at scheduled intervals using reference signals rather than continuously. This periodic approach maintains prediction accuracy by regularly checking performance while reducing energy consumption by allowing the system to operate in a lower-power state between monitoring intervals. The network entity receives periodic reports and can trigger reconfiguration only when needed.
3Ease of operation
If performance monitoring uses reference signals, then monitoring can be implemented, but failures to detect or measure signals lead to monitoring failures
Solution Approach 1:
The patent implements beforehand cushioning by establishing multiple reference signal resources and alternative monitoring paths in advance. When the primary reference signal fails to be detected or measured, the system has pre-configured alternatives ready, preventing monitoring failure. This approach maintains ease of operation through pre-planned procedures while improving reliability by having backup mechanisms in place before failures occur.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting monitoring parameters such as reference signal power thresholds, measurement intervals, and resource allocations based on detected conditions. When signal detection becomes unreliable, the system changes parameters like increasing power thresholds or switching to alternative reference signals, thereby maintaining monitoring reliability while preserving ease of operation through automated parameter adaptation.
Data Source
AI summary
A user equipment comprising: an inference module configured to predict at least one reference signal resource using a first set of input reference signal resources as an input of the inference module; a performance monitoring module configured for performance monitoring of the inference module, wherein the performance monitoring uses a second set of reference signals as input to the performance monitoring module; a performance monitoring failure detection module configured to detect failure of the performance monitoring module.