RF Calibration AI Model for Unmeasured Frequency Points
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Solution Overview
Problem
Existing RF calibration methods for wireless communication devices are time-consuming and resource-intensive due to the need for extensive calibration at multiple frequency points and gain settings, which increases production time and costs.
Innovation Solution
Employing an artificial intelligence (AI) model to predict RF measurements at unmeasured frequency points based on a reduced set of calibration measurements, using a subset of frequency points as input to train the model and compare predicted measurements with actual measurements to determine prediction errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional RF calibration methods are used with measurements at multiple frequency points and gain settings, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The system performs preliminary calibration measurements at all frequency points and gain settings during manufacturing, then uses machine learning models to learn the relationships between different measurement conditions. This pre-collected data enables later predictions without requiring full calibration sequences during operation, significantly reducing calibration time while maintaining precision.
Solution Approach 2:
The system creates virtual copies of calibration measurements through machine learning predictions. Instead of physically performing measurements at all frequency points and gain settings, the ML model generates predicted measurement values that replicate the效果 of actual measurements, reducing the need for extensive physical calibration while maintaining measurement accuracy.
2Measurement precision
If traditional RF calibration methods are used with measurements at multiple frequency points and gain settings, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system performs preliminary calibration measurements at all frequency points and gain settings during manufacturing, then uses machine learning models to learn the relationships between different measurement conditions. This pre-collected data enables later predictions without requiring full calibration sequences during operation, significantly reducing calibration time while maintaining precision.
Solution Approach 2:
The system creates virtual copies of calibration measurements through machine learning predictions. Instead of physically performing measurements at all frequency points and gain settings, the ML model generates predicted measurement values that replicate the效果 of actual measurements, reducing the need for extensive physical calibration while maintaining measurement accuracy.
3Loss of time
If the number of calibration measurements is reduced using AI predictions, then loss of time decreases and productivity improves, but measurement precision may deteriorate
Solution Approach 1:
The system implements feedback mechanisms where predicted measurements are compared against actual measurements, and the differences (errors) are used to refine and update the machine learning models. This continuous feedback loop ensures that predictions remain accurate even as the system operates with reduced measurement sets, maintaining precision while benefiting from time savings.
Solution Approach 2:
The system dynamically adjusts the calibration strategy based on operating conditions. The ML models are trained to handle different frequency bands, gain settings, and device variations, adapting predictions to specific conditions rather than using fixed reduction rules. This dynamic approach maintains accuracy across diverse scenarios while minimizing calibration requirements.
Data Source
AI summary
In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may be a computing device. The computing device receives calibration data that includes RF measurements at multiple frequency points for a band and calibration index (CID) combination. The computing device selects a subset of the frequency points as input frequency points. The computing device trains an artificial intelligence (AI) model using the calibration data. During the training, the computing device inputs RF measurements at the input frequency points to the AI model. The computing device generates predicted RF measurements for the remaining frequency points of the plurality of frequency points using the AI model. The computing device compares the predicted RF measurements to actual RF measurements in the calibration data to determine prediction errors.