AI Training Data Screening to Reduce Air Interface Waste
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Solution Overview
Problem
The existing method of periodically or continuously exchanging training data between AI model training and data collection network elements leads to a waste of air interface resources and can result in the transmission of invalid data, causing pollution in the training dataset and affecting the accuracy and adaptability of AI models.
Innovation Solution
A method where a first network element collects candidate training data and determines its validity based on received information from a second network element, sending only valid data to the second network element, thereby reducing the transmission of invalid data and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training data is exchanged periodically or continuously between network elements, then the AI model training is maintained, but air interface resources are wasted
Solution Approach 1:
The first network element performs preliminary validity determination on candidate training data before transmission to the second network element. By filtering out invalid data in advance, the system avoids wasting air interface resources transmitting unnecessary data while ensuring only valid training data reaches the training network element.
Solution Approach 2:
The patent extracts and removes invalid candidate training data from the transmission stream. The first network element identifies and separates invalid data from valid data, transmitting only the valid portion to the second network element, thereby reducing air interface resource consumption.
2Ease of operation
If all collected training data is transmitted without validity screening, then data transmission is simplified, but invalid data pollutes the training dataset
Solution Approach 1:
The first network element performs preliminary validity screening on candidate training data before transmission. This preliminary action ensures that only valid data is transmitted to the second network element, preventing data pollution while maintaining a relatively simple transmission process.
Solution Approach 2:
The patent extracts and removes invalid data from the candidate training data set before transmission. By separating invalid data and excluding it from transmission, the system maintains transmission simplicity while ensuring training dataset quality.
3Reliability
If validity determination is performed on collected training data, then data quality is improved, but processing time and complexity increase
Solution Approach 1:
The first network element performs self-service validity determination using locally stored first information (validity criteria). This self-service approach eliminates the need for complex validation processes or external verification, improving data quality while minimizing processing time and complexity.
Solution Approach 2:
The patent uses parameter-based validity determination where the first network element compares candidate training data against predefined parameters (first information) stored locally. This parameter-based approach enables efficient quality filtering without significant time or complexity overhead.
Data Source
AI summary
A first network element (a training data collection network element) receives first information from a second network element (an AI model training network element), the first information being for determining validity of candidate training data collected by the first network element. The first network element collects candidate training data of an AI model, and determines the validity of the candidate training data based on the first information. When the candidate training data is valid, the first network element sends valid candidate training data to the second network element, but does not send invalid candidate training data. When there is no valid candidate training data, the first network element indicates, to the second network element, that the training data collected this time is invalid, and does not send the candidate training data collected this time to the second network element. Waste of air interface resources can be reduced.