Machine Learning Error Reporting in Wireless Communication
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Solution Overview
Problem
Current wireless communication systems face challenges in efficiently reporting errors associated with machine learning-based models used in wireless communication, leading to biased updates and increased overhead in data transmission.
Innovation Solution
A method where user equipment (UE) detects error events in machine learning-based models and transmits error reports to base stations, including updated parameters and scalar quantities to normalize data, reducing reporting overhead and eliminating bias in model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If error reports are transmitted frequently to ensure accurate model updates, then model accuracy is improved, but data transmission overhead increases
Solution Approach 1:
The patent applies partial action by transmitting only a subset of error reports rather than all errors. The UE selectively reports errors based on criteria such as error severity, frequency, or impact on model performance, thereby reducing transmission overhead while maintaining sufficient accuracy for meaningful model updates.
Solution Approach 2:
The patent changes parameters related to error reporting thresholds and filtering criteria. By adjusting these parameters, the system can optimize the balance between reporting enough errors to maintain accuracy and limiting reports to reduce overhead. The base station can configure reporting thresholds that adapt to current operational conditions.
2Reliability
If all error data is reported to ensure unbiased model updates, then update fairness is improved, but reporting overhead increases
Solution Approach 1:
The patent extracts only the essential error information needed for unbiased updates, filtering out redundant or less significant error data. This extraction process maintains the fairness and reliability of model updates by ensuring that only pertinent error information influences the update process, while reducing the overall reporting overhead.
Solution Approach 2:
The patent implements feedback mechanisms where the base station receives error reports, processes them to ensure unbiased updates, and sends back updated model parameters. This feedback loop allows the system to maintain update fairness while managing reporting overhead through intelligent error selection and prioritization.
Data Source
AI summary
Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment may apply a machine learning-based model to one or more functions for wireless communication, determine an error event associated with the machine learning-based model based at least in part on applying the machine learning-based model, and transmit, to a base station, an error report based at least in part on determining the error event associated with the machine learning-based model. Numerous other aspects are provided.