Power Amplifier Predistortion with Indirect-Direct Pre-Inverse Learning
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Solution Overview
Problem
Existing methods for estimating pre-inverses in nonlinear systems, such as power amplifiers, often fail to accurately linearize the system due to limitations in indirect learning techniques that lack theoretical completeness and do not directly address the pre-inverse, leading to suboptimal performance.
Innovation Solution
The proposed solution involves an apparatus and method that includes an amplifier circuit with an indirect learning circuit that iteratively adjusts predistortion data during an indirect learning mode until convergence, followed by a direct learning circuit that further refines the predistortion data using a power amplifier model, enabling accurate pre-inverse estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If indirect learning techniques are used to estimate pre-inverses, then the system can be implemented with existing methods, but the accuracy of linearization is insufficient
Solution Approach 1:
The learning process is divided into two distinct modes: indirect learning mode for initial pre-inverse estimation and direct learning mode for refinement. This segmentation allows each mode to specialize in specific tasks, with indirect learning providing a starting point and direct learning improving accuracy, thereby resolving the contradiction between implementability and precision.
Solution Approach 2:
The indirect learning mode performs preliminary estimation of the pre-inverse before the direct learning mode begins. This preliminary action provides an initial solution that the direct learning mode can then refine, enabling the system to achieve high accuracy while maintaining theoretical completeness.
2Device complexity
If only indirect learning mode is used, then the implementation is simpler, but the pre-inverse estimation does not converge to optimal accuracy
Solution Approach 1:
The learning circuit is segmented into two operational modes with distinct functions. The indirect learning mode handles initial estimation with simpler computations, while the direct learning mode refines the solution with more sophisticated algorithms. This segmentation balances complexity and accuracy by assigning appropriate computational tasks to each mode.
Solution Approach 2:
The system transitions continuously from indirect learning to direct learning, maintaining the useful action of pre-inverse estimation throughout. The direct learning mode continues and refines the work initiated by indirect learning, ensuring that the estimation process achieves optimal accuracy without unnecessary interruptions or restarts.
3Reliability
If direct learning mode is used from the beginning, then the theoretical framework is more complete, but the convergence speed is slower
Solution Approach 1:
The indirect learning mode performs preliminary estimation that brings the system close to the optimal solution before direct learning begins. This preliminary action reduces the distance to convergence, allowing the theoretically complete direct learning mode to converge faster than if it started from scratch.
Solution Approach 2:
Different learning modes are applied at different stages of the estimation process. Indirect learning with its faster convergence is used initially when far from the optimal solution, while direct learning with its theoretical completeness is used later when closer to convergence. This local optimization of algorithm selection resolves the speed-completeness contradiction.
Data Source
AI summary
An apparatus is provided to determine pre-distortion for a nonlinear system. The apparatus comprises a datapath and a power amplifier. The datapath employs predistortion data to generally linearized the power amplifier. To generate this predistortion data, an indirect learning circuit and a direct learning circuit can be employed. The indirect learning circuit is generally coupled to the amplifier circuit so that it can iteratively adjust predistortion data during an indirect learning mode until convergence is reached. The direct learning circuit is generally coupled to the amplifier circuit and the indirect learning circuit and that receives the input signal so that the predistortion data can be copied to the direct learning circuit from the indirect learning after convergence is reached and so that the direct learning circuit can adjust the predistortion data during a direct learning mode.