RF Power Amplifier Pre-Distortion With Spectrum-Guided Training
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Solution Overview
Problem
Existing pre-distortion systems for radio frequency amplifiers in multi-channel wireless transmitters face instability in coefficient training due to varying signal conditions, particularly when multiple channels with different wireless standards are used, leading to suboptimal performance.
Innovation Solution
A method to determine pre-distorter coefficients by selectively updating input samples based on a figure of merit calculated from the frequency and amplitude spectrum, ensuring comprehensive frequency coverage and optimal amplifier characteristics, even in dynamic signal conditions like frequency hopping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-distorter coefficients are trained using all input samples in multi-channel wireless transmitter, then more data is available for training, but coefficient training becomes unstable due to varying signal conditions and frequency spectrum coverage
Solution Approach 1:
The patent applies local quality by selecting input samples based on their frequency spectrum characteristics. Instead of treating all samples uniformly, the system identifies and selects samples that provide good coverage of the frequency spectrum, particularly focusing on regions where the amplifier operates with significant power. This selective approach ensures that coefficient training is performed using locally optimal samples that represent the actual operating conditions of the amplifier.
Solution Approach 2:
The patent implements feedback by using the frequency spectrum of previously selected input samples as a criterion for selecting new samples. The system continuously monitors the frequency content of selected samples and uses this information to guide subsequent sample selections, ensuring comprehensive frequency coverage. This feedback mechanism stabilizes coefficient training by maintaining a representative sample set that reflects the amplifier's operating spectrum.
2Manufacturing precision
If pre-distorter coefficients are determined for entire frequency spectrum, then distortion correction is improved across all channels, but training complexity and computational load increase
Solution Approach 1:
The patent extracts only the necessary input samples that provide meaningful information for coefficient training. Instead of processing all available input samples across the entire frequency spectrum, the system identifies and extracts samples that have significant power content and represent the actual operating conditions. This extraction approach reduces training complexity while maintaining distortion correction accuracy for the relevant frequency regions.
Solution Approach 2:
The patent segments the frequency spectrum into relevant and irrelevant regions based on amplifier power distribution. By dividing the entire frequency spectrum into segments and selectively training on samples from the relevant segments (where the amplifier operates with significant power), the system reduces training complexity while maintaining accurate distortion correction where it matters most.
3Quantity of substance
If all input samples are used for coefficient determination, then more training data is available, but frequency spectrum coverage becomes uneven and suboptimal
Solution Approach 1:
The patent changes the selection parameter from simply using all available samples to selecting samples based on their frequency spectrum characteristics. By introducing frequency spectrum coverage as a selection criterion, the system transforms the sample selection process from a quantity-based approach to a quality-based approach, ensuring that the selected samples provide uniform and comprehensive frequency spectrum coverage.
Solution Approach 2:
The patent applies local quality by evaluating each input sample's frequency spectrum characteristics and selecting samples that provide good local coverage of the frequency spectrum. This ensures that regions where the amplifier operates with significant power are well-represented in the training set, improving the precision of coefficient determination for these critical frequency regions.
Data Source
AI summary
Coefficients of a pre-distorter are determined for distortion correction of a signal amplified by a radio frequency amplifier by sampling the signal at the pre-distorter input to generate a plurality of input samples and sampling the signal at the amplifier output to generate a plurality of output samples, each of the output samples corresponding to one of the input samples, calculating a figure of merit for one or more of the input samples on the basis of the one or more input samples, selectively updating a previously selected set of input samples with the one or more input samples on the basis of the calculated figure of merit, whereby to generate an updated selected set and determining the coefficients of the pre-distorter on the basis of the input samples in the updated selected set and the corresponding output samples.