Adaptive Predistortion Amplifier Selective Sampling
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Solution Overview
Problem
Existing predistortion methods for power amplifiers face challenges in reliability due to outlier measurements, which corrupt the process of finding optimal tuning parameters for adaptive predistortion, leading to inefficient linearization and increased distortion.
Innovation Solution
An adaptive predistortion linearized amplifier system that employs selective sampling and an adaptation circuit to reduce the number of input and output samples using characteristics like signal magnitude and phase, improving the reliability of predistortion coefficient updates and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If differential log measurements are used to measure gain, then gain information is obtained, but outlier measurements with large errors occur that corrupt the search process for optimal tuning parameters
Solution Approach 1:
The patent extracts only the useful measurements from the differential log detector and discards outlier measurements with large errors. The adaptive algorithm processes only selected measurements that fall within acceptable error bounds, separating useful data from harmful outliers to improve reliability while maintaining measurement precision.
Solution Approach 2:
The patent changes the parameter selection criterion by introducing a threshold-based selection mechanism. Instead of using all differential log measurements, the system selectively processes measurements based on their error characteristics, changing from universal processing to conditional processing based on measurement quality parameters.
2Adaptability or versatility
If all input and output samples are processed for adaptive predistortion, then comprehensive tuning is achieved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts and processes only a subset of samples that provide the most valuable information for adaptive predistortion. By selecting representative samples rather than processing all samples, the system achieves comprehensive tuning with reduced computational complexity and processing time.
Solution Approach 2:
The patent applies partial action by processing only the necessary portion of samples required for effective adaptive predistortion. Instead of exhaustive processing of all samples, the system uses a selective subset that provides sufficient information for accurate tuning, reducing computational burden while maintaining adaptability.
3Reliability
If predistortion is tuned to reduce output distortion, then linearization is improved, but the system requires reliable measurement and estimation processes that are currently compromised by outlier measurements
Solution Approach 1:
The patent applies preliminary anti-action by preemptively filtering out outlier measurements before they can corrupt the predistortion tuning process. The selective measurement processing prevents harmful data from affecting the adaptation algorithm, ensuring reliable tuning and effective distortion reduction.
Solution Approach 2:
The patent implements feedback through the adaptive algorithm that continuously monitors measurement quality and adjusts processing accordingly. The system uses feedback from measurement error analysis to determine which samples to process, creating a closed-loop system that improves reliability while maintaining distortion reduction effectiveness.
Data Source
AI summary
An adaptive predistortion linearized amplifier system employing selective sampling is disclosed. Two selective sampling methods of identifying valid measurements using information from the input signal are disclosed. The selective sampling process improves the convergence properties of tuning algorithms based on minimizing the gain error variance. It also reduces the number of data samples processed by the estimator used in the tuning process.