RF Power Amplifier Pre-Distortion With Spectrum-Based Sample Selection
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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 handling multiple channels with different wireless standards, 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
1Quantity of substance
If pre-distorter coefficients are trained using all available input samples in multi-channel systems, then more data is available for training, but the training becomes unstable due to varying signal conditions and frequency spectrum gaps
Solution Approach 1:
The patent changes the parameter of sample selection by introducing a figure of merit based on frequency spectrum occupancy. Instead of using all samples uniformly, the system evaluates each sample's frequency spectrum and selectively includes samples that meet occupancy thresholds, transforming the training approach from quantity-based to quality-based sample selection
Solution Approach 2:
The system performs self-evaluation by calculating the figure of merit for each sample set and automatically determining which samples to include in training. The pre-distorter trainer autonomously adjusts the selected set of input samples based on spectrum occupancy analysis, enabling adaptive training without external intervention
2Productivity
If the pre-distorter is trained on samples with incomplete frequency spectrum coverage, then training is simpler and faster, but the pre-distorter performance degrades across the entire frequency band
Solution Approach 1:
The patent implements feedback by continuously monitoring the frequency spectrum occupancy and using it to adjust the selected set of training samples. The figure of merit calculation provides feedback on spectrum coverage quality, enabling the system to iteratively improve sample selection until occupancy thresholds are met, ensuring comprehensive frequency band coverage for accurate pre-distorter training
3Adaptability or versatility
If multiple channels with different wireless standards are transmitted simultaneously, then system versatility increases, but coefficient training stability decreases due to varying signal conditions
Solution Approach 1:
The patent applies local quality by evaluating and selecting training samples based on their individual frequency spectrum characteristics. Instead of treating all samples uniformly across multiple channels, the system assesses each sample's spectrum occupancy locally and selectively includes samples that contribute to comprehensive frequency coverage, ensuring stable training despite varying signal conditions across different wireless standards
Data Source
AI summary
Coefficients of a pre-distorter are determined for correction of distortion of a signal amplified by a radio frequency amplifier by sampling the signal at the input to the pre-distorter to generate a plurality of input samples and sampling the signal at the output of the amplifier to generate a plurality of output samples, each of the plurality of output samples corresponding to one of the plurality of input samples, calculating a figure of merit for one or more of said plurality of input samples on the basis of the one or more of said plurality of input samples, selectively updating a previously selected set of input samples with said one or more of said plurality of 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.