User Equipment Prediction Error Feedback for ML Updates
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Solution Overview
Problem
In wireless communication networks, particularly in 5G NR systems, the burden on base stations to process large amounts of sampled data from user equipment (UE) for updating machine learning algorithms is significant, as current methods require feeding back all signal measurements, including both correct and incorrect predictions, which is inefficient and resource-intensive.
Innovation Solution
User equipment (UE) is configured to selectively feed back only sampled data corresponding to prediction errors to the base station, reducing the amount of data transmitted and processed, while still providing insightful data for improving machine learning algorithms, by distinguishing between correct and incorrect predictions and prioritizing the transmission of prediction error data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all signal measurements are fed back to the base station for updating machine learning algorithms, then the machine learning algorithm can be updated with comprehensive data, but the resource burden on the base station increases significantly and data transmission efficiency decreases
Solution Approach 1:
The patent extracts only the necessary subset of data (signal measurements corresponding to prediction errors) from the complete dataset and transmits only this extracted portion to the base station. This selective extraction reduces the data volume significantly while maintaining the quality and relevance of training data for improving machine learning algorithm accuracy.
Solution Approach 2:
The patent applies local quality by differentiating between different types of signal measurements based on their predictive accuracy. Instead of treating all measurements uniformly, the system identifies and prioritizes measurements that correspond to prediction errors (local quality characteristic) over correct predictions, as these error cases provide more valuable feedback for algorithm improvement.
2Loss of information
If all signal measurements including correct predictions are transmitted, then complete feedback is provided for algorithm training, but data transmission resources are wasted
Solution Approach 1:
The system extracts only the subset of signal measurements that correspond to prediction errors from the complete measurement set. By taking out only the error cases rather than transmitting all measurements, the patent reduces transmission energy consumption while preserving the essential information needed for effective algorithm training.
Solution Approach 2:
The patent discards correct predictions from the transmission process (as they provide less valuable feedback) while recovering and prioritizing the transmission of prediction error data. This selective discarding and recovering approach optimizes the balance between information completeness and energy efficiency.
3Quantity of substance
If user equipment processes and transmits all sampled data, then comprehensive feedback is provided, but the complexity of data handling and transmission increases
Solution Approach 1:
The patent segments the signal measurements into distinct categories: those corresponding to prediction errors and those corresponding to correct predictions. This segmentation enables the UE to process and differentiate data types, transmitting only the error segment to the base station, thereby reducing overall data volume and processing complexity.
Solution Approach 2:
The UE applies local quality by identifying and prioritizing specific local characteristics within the data (prediction errors versus correct predictions). This quality differentiation simplifies the data handling process at the UE by focusing computational resources on identifying and preparing only the error cases for transmission, rather than processing all data uniformly.
Data Source
AI summary
Wireless communications systems and methods related to user equipment reporting for updating of machine learning algorithms are provided. A user equipment (UE) applies a machine learning-based network to a set of received signal measurements. The UE determines whether an output of the machine learning-based network fails to satisfy one or more criteria. The UE communicates, with a base station (BS), a report when the output of the machine learning-based network fails to satisfy the one or more criteria. The BS communicates, with one or more UEs, a first configuration for a machine learning-based network. The BS receives, from a first UE of the one or more UEs, a report associated with a prediction error in the machine learning-based network. The BS communicates, with the first UE, a second configuration for the machine learning-based network based on the received report.