Inference Error Feedback for ML Beam Management
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately training machine learning models for predictive beam management, particularly in accounting for outlier conditions or corner cases, leading to unacceptably high differences between predicted and actual values.
Innovation Solution
A method where a user equipment (UE) is configured to perform machine learning-based inferences for characteristics of resources, communication beams, or channels, and compares these inferences with actual measurements, identifying and reporting errors to the network entity, which can then update the machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used for predictive beam management, then prediction speed and automation are improved, but accuracy under outlier conditions deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the UE compares machine learning-based predicted measurements with actual measurements and reports inference errors to the network entity. The network entity uses these error reports to update and refine the machine learning model, creating a continuous improvement loop that maintains prediction speed while progressively improving accuracy under outlier conditions.
Solution Approach 2:
The system performs preliminary machine learning-based predictions to identify potential outlier conditions before they significantly degrade performance. By detecting inference errors early through comparison with actual measurements and reporting them to the network entity for model updates, the system proactively addresses accuracy issues before they accumulate.
2Loss of time
If machine learning models are trained with limited data, then training time and complexity are reduced, but model accuracy deteriorates
Solution Approach 1:
The feedback mechanism enables continuous model refinement using real-world inference error data collected from UE measurements. This allows the model to progressively improve accuracy by learning from actual deployment conditions and outlier cases, effectively performing ongoing training without requiring extensive initial training data or prolonged training periods.
Solution Approach 2:
The system enables self-service learning where the machine learning model automatically improves itself through the feedback loop. The network entity updates the model based on inference error reports from the UE, allowing the model to adapt and improve accuracy autonomously without requiring manual retraining with additional datasets.
Data Source
AI summary
Methods, systems, and devices for wireless communications are described. A user equipment (UE) may receive control signaling indicating a configuration for the UE to perform a machine learning-based inference (e.g., based on a machine learning model) for predicting a characteristic of at least one resource, at least one communication beam, or at least one communication channel. The characteristic may be associated with a spatial domain, a time domain, a frequency domain, or any combination thereof. In accordance with the configuration, the UE may perform the machine learning-based inference for the characteristic. The UE may also perform a measurement of the characteristic (e.g., an actual measurement). The UE may also transmit, in accordance with a triggering condition, an indication of a difference between the machine learning-based inference and the measurement of the characteristic.