DPD Feedback Compression by Sample Combining for Real-Time Training
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Solution Overview
Problem
Digital predistortion systems face challenges in reducing model coefficient estimation complexity and sample buffering needs due to high sampling rates and correlated feedback samples, which increase computational complexity and power consumption.
Innovation Solution
A feedback compression technique that under-samples feedback samples and combines consecutive samples into compressed samples based on predetermined parameters, such as integration period and under-sampling rate, to reduce the number of samples and buffering requirements, while maintaining statistical information and enabling robust model coefficient estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high sampling rates are used in digital predistortion systems, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential information from high-rate feedback samples by using under-sampling combined with integration. Instead of processing all high-rate samples, the system extracts representative samples at lower rates while maintaining the necessary statistical information for accurate model coefficient estimation through the integration process.
Solution Approach 2:
The patent changes the sampling rate parameter from high to low (under-sampling) while compensating for the information loss through integration. This parameter transformation allows the system to operate at lower computational complexity while maintaining measurement precision through the mathematical integration of the under-sampled samples.
2Measurement precision
If high sampling rates are used in digital predistortion systems, then measurement precision is improved, but power consumption increases
Solution Approach 1:
The patent extracts only the essential information from high-rate feedback samples by using under-sampling combined with integration. Instead of processing all high-rate samples, the system extracts representative samples at lower rates while maintaining the necessary statistical information for accurate model coefficient estimation through the integration process.
Solution Approach 2:
The patent changes the sampling rate parameter from high to low (under-sampling) while compensating for the information loss through integration. This parameter transformation allows the system to operate at lower computational complexity while maintaining measurement precision through the mathematical integration of the under-sampled samples.
3Measurement precision
If consecutive feedback samples are processed, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges consecutive under-sampled feedback samples through integration to form integrated samples. This combining process reduces the number of samples that need to be buffered and processed individually, while the integration maintains the cumulative information from the consecutive samples, effectively reducing buffering needs without sacrificing measurement precision.
4Measurement precision
If high chip data-rates are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential information from high-rate feedback samples by using under-sampling combined with integration. Instead of processing all high-rate samples, the system extracts representative samples at lower rates while maintaining the necessary statistical information for accurate model coefficient estimation through the integration process.
Solution Approach 2:
The patent changes the sampling rate parameter from high to low (under-sampling) while compensating for the information loss through integration. This parameter transformation allows the system to operate at lower computational complexity while maintaining measurement precision through the mathematical integration of the under-sampled samples.
Data Source
AI summary
Example embodiments provide a compression technique of feedback samples for digital predistortion. A system (100) may comprise a feedback receiver (116) configured to receive feedback signal of a power amplifier (112) output and determine a set of under-sampled samples based on the feedback signal; a compressing circuitry (130) configured to: obtain the under-sampled samples; and compress the under-sampled samples, wherein two or more consecutive under-sampled samples are combined into one or more single samples based on one or more predetermined parameters; and a model coefficient training circuitry (102) configured to receive the compressed under-sampled samples and determine model coefficients for digital predistortion based on the compressed under-sampled samples. A system and a method are disclosed.