Power Amplifier Predistortion via Block-Diagonal ADC Sampling
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Solution Overview
Problem
Existing digital predistortion (DPD) systems face challenges in meeting the increasing performance requirements of wireless communication applications due to the nonlinearity of power amplifiers, which causes spectral growth and distortions, leading to increased bit error rates and complex data processing demands.
Innovation Solution
A model identification system that uses an analog-to-digital converter (ADC) with a block diagonal sampling matrix to reduce the data rate and computational complexity by generating a digital signal with a lower sampling rate, allowing for efficient predistortion of power amplifiers in communication systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high sampling rate is used in the ADC to capture power amplifier output signals accurately, then measurement precision is improved, but data rate and computational complexity increase
Solution Approach 1:
The sampling matrix is segmented into a block diagonal structure with multiple diagonal blocks, where each block corresponds to a subset of signal components. This segmentation allows the system to process different signal components separately at reduced sampling rates, thereby reducing overall computational complexity while maintaining measurement precision for critical signal components.
Solution Approach 2:
The system changes the sampling rate parameter dynamically by using a block diagonal sampling matrix with different sampling rates for different signal components. This allows high sampling rates to be applied only where necessary for measurement precision, while lower sampling rates are used for other components, thus reducing overall data rate and computational complexity.
2Measurement precision
If a high sampling rate is used in the ADC to capture power amplifier output signals accurately, then measurement precision is improved, but data transmission rate increases
Solution Approach 1:
The sampling matrix is segmented into a block diagonal structure with multiple diagonal blocks, where each block corresponds to a subset of signal components. This segmentation allows the system to process different signal components separately at reduced sampling rates, thereby reducing overall computational complexity while maintaining measurement precision for critical signal components.
Solution Approach 2:
The system changes the sampling rate parameter dynamically by using a block diagonal sampling matrix with different sampling rates for different signal components. This allows high sampling rates to be applied only where necessary for measurement precision, while lower sampling rates are used for other components, thus reducing overall data rate and computational complexity.
3Reliability
If complex predistortion models are used to compensate for power amplifier nonlinearity, then performance is improved, but device complexity increases
Solution Approach 1:
The predistortion model is segmented into multiple components corresponding to different diagonal blocks of the sampling matrix. Each component processes a specific subset of signal characteristics, allowing the complex predistortion task to be divided into manageable parts that can be implemented with reduced overall system complexity while maintaining comprehensive performance.
Data Source
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AI summary
A model identification system [FIG. 8A, 800] includes an analog to digital converter (ADC) [FIG. 8A, 210]. The ADC includes a conversion circuit [FIG. 8A, 212] configured to receive a first analog signal [FIG. 8A, 208] and generate a first digital signal [FIG. 8A, 214] including samples having a first rate by sampling the first analog signal at the first rate. The ADC further includes a first digital signal processing (DSP) circuit [FIG. 8A, 802] configured to generate a second digital signal [FIG. 8A, 804] including samples having a second rate less than the first rate based on the first digital signal and a first sampling matrix [FIG. 8B, 854A; FIG. 9, Dbikones]. The first sampling matrix is a block diagonal matrix including a plurality of diagonal blocks, each diagonal block is a row vector including a plurality of elements.