Power Amplifier Linearization Using Stable Coefficient Estimation
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Solution Overview
Problem
Existing linearization techniques for power amplifiers in wireless communication systems face challenges in achieving efficient convergence, performance, and stability, particularly with time-variant input signals, leading to signal distortions and inefficiencies.
Innovation Solution
A method using fixed-point arithmetic with dither and reference coefficients to stabilize coefficient updates in adaptation algorithms, enhancing convergence and robustness against signal variations, particularly for power amplifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing linearization techniques are used for power amplifiers, then the system can operate with nonlinear components, but signal distortions occur and convergence is inefficient
Solution Approach 1:
The patent transforms the nonlinear power amplifier characteristics into a linear model by changing the representation parameters through polynomial approximation. The nonlinear transfer function is expressed as a polynomial with coefficients that are optimized to minimize distortion, effectively transforming the problem from dealing with nonlinear parameters to optimizing polynomial coefficients.
Solution Approach 2:
The patent replaces the direct nonlinear mechanical/electrical system behavior with a mathematical polynomial model. Instead of directly controlling the nonlinear power amplifier, the system uses polynomial coefficients to model and compensate for nonlinearities, substituting the physical nonlinear system with a mathematical representation that can be more effectively controlled.
2Adaptability or versatility
If adaptation algorithms are used to linearize power amplifiers, then the system can adapt to varying conditions, but convergence stability is poor with time-variant input signals
Solution Approach 1:
The patent implements a dynamic adaptation mechanism where polynomial coefficients are continuously updated based on incoming signals. The system transitions from static linearization parameters to dynamic coefficients that adapt in real-time to varying input conditions, allowing the system to maintain performance across different operating conditions while achieving stable convergence.
3Manufacturing precision
If linearization is applied to improve signal quality, then signal distortions are reduced, but computational complexity increases
Solution Approach 1:
The patent applies polynomial approximation of limited degree (partial action) rather than attempting to model all nonlinearities. By using a polynomial of sufficient but not excessive degree, the system achieves acceptable linearization performance while keeping the computational complexity manageable, avoiding the need for complex high-order models.
Data Source
AI summary
Disclosed is a method comprising selecting a mathematical model associated with a nonlinear component, determining an error signal associated with the nonlinear component, wherein the error signal indicates a difference between a first signal and a second signal, estimating one or more parameters that minimize the error signal based on the mathematical model, and estimating and/or linearizing the nonlinear component based on the estimated one or more parameters.