Crest Factor Reduction Parameter Prediction for New RF Use Cases
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Legacy crest factor reduction (CFR) optimization techniques are inefficient for online optimization and cannot predict optimal parameter values for new or un-predetermined use cases, leading to performance degradation and failure to meet spectral emission mask limits.
Innovation Solution
A machine learning model is trained to predict optimal CFR processing characteristics based on use case attributes, allowing for precise configuration and prediction of parameter values for various use cases, including new ones, thereby avoiding iterative optimization procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If legacy CFR optimization techniques are used, then CFR processing can be performed, but the optimization is inefficient and cannot predict optimal parameter values for new use cases
Solution Approach 1:
The system performs preliminary optimization offline to generate training data, then uses a trained machine learning model to predict optimal parameters in real-time. This preliminary action allows the system to handle new use cases efficiently without performing iterative optimization during online operation.
Solution Approach 2:
The system creates a copy of the optimization knowledge by training a machine learning model on offline-generated training data. The model copies the optimal parameter patterns learned from training data and applies them to new use cases, eliminating the need for repeated iterative optimization.
2Measurement precision
If iterative optimization procedures are used for each use case, then optimal parameters can be determined, but the process is time-consuming and fails to meet performance criteria
Solution Approach 1:
Optimal parameters are determined in advance through offline optimization and stored as training data. The machine learning model then retrieves and applies these pre-determined parameters instantly during online operation, eliminating time-consuming iterative procedures while maintaining optimization accuracy.
Solution Approach 2:
The patent replaces the mechanical iterative optimization process with a machine learning-based prediction system. The ML model directly outputs optimal parameters based on input features, substituting the step-by-step iterative search with a single prediction operation.
3Productivity
If CFR parameters are not optimized, then processing is faster, but performance degrades and spectral emission mask limits are not met
Solution Approach 1:
The system copies optimized parameter settings from training data into the ML model, enabling instant retrieval of compliance-assuring parameters during online operation. This copying mechanism ensures spectral emission mask compliance without sacrificing processing speed.
Solution Approach 2:
The machine learning model autonomously selects optimal CFR parameters based on input use case features, without requiring external optimization procedures. This self-service capability ensures both speed and compliance by directly outputting pre-learned optimal parameters.
Data Source
AI summary
Disclosed is a method comprising selecting one or more crest factor reduction processing characteristics to be optimized for a crest factor reduction technique; collecting use case attribute data comprising a set of values of one or more use case attributes associated with the crest factor reduction technique; determining, based on the use case attribute data, a set of optimized configuration parameter values for the one or more crest factor reduction processing characteristics; generating a set of labelled training data based on the use case attribute data and the set of optimized configuration parameter values; and training, based on the set of labelled training data, a machine learning model for predicting configuration parameter values for the one or more crest factor reduction processing characteristics.