Dynamic Labeling for ML Models in Changing Radio Environments
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Solution Overview
Problem
Machine learning models trained in a first radio environment often fail to perform optimally in a second radio environment due to variations in geographic and temporal changes, leading to inaccurate predictions and inefficient network operations.
Innovation Solution
A method for dynamically adapting the labeling scheme of machine learning models using unsupervised learning to optimize performance in changing radio environments, allowing for automated updates without manual re-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a machine learning model is trained in a first radio environment, then it achieves good performance in that environment, but it fails to perform optimally in a second radio environment due to geographic and temporal variations
Solution Approach 1:
The patent implements dynamic labeling schemes that automatically adapt to changing radio environments. The system continuously monitors environmental characteristics and adjusts labeling criteria in real-time, transforming the static labeling approach into a dynamic one that maintains model performance across varying conditions without requiring complete retraining
Solution Approach 2:
The patent changes the parameters of the labeling scheme based on environmental characteristics. By modifying labeling thresholds and criteria according to detected environmental features, the system adapts the model's behavior to match current radio conditions, resolving the contradiction between maintaining reliable performance and adapting to new environments
2Ease of manufacture
If conventional static labeling schemes are used, then the labeling process is simple and consistent, but the labeling becomes inappropriate when the distribution of feature values changes in a new radio environment
Solution Approach 1:
The system performs self-service by automatically detecting environmental characteristics and adjusting its own labeling scheme without external intervention. The machine learning model autonomously adapts its labeling criteria based on observed data distributions, maintaining both simplicity and accuracy through self-adjustment rather than complex manual configuration
3Measurement precision
If manual re-training and re-labeling is performed for each new radio environment, then model accuracy is maintained, but the time and resources required increase significantly
Solution Approach 1:
The system prepares for environmental changes by implementing continuous environmental monitoring and incremental labeling adjustments. Rather than waiting for performance degradation to trigger complete retraining, the system proactively adapts labeling schemes in small steps as environmental drift is detected, maintaining accuracy while minimizing time loss
Data Source
AI summary
Methods and nodes in a communications network for using a machine learning model that has been trained using training data from a first radio environment, in a second radio environment, wherein the machine learning model takes a first set of features as input and outputs a label based on a first labelling scheme, the first labelling scheme having been determined for the first radio environment. A method comprises obtaining (402) a dataset in the second radio environment comprising a plurality of data points comprising values of the first set of features, labelling (404) each data point in the dataset using the first labelling scheme, and determining (406) from the labelled dataset whether the first labelling scheme is suitable for use in the second radio environment. Responsive to determining that the first labelling scheme is unsuitable for use in the second radio environment, the method further comprises determining (408) a second labelling scheme for the machine learning model, using an unsupervised labelling method.