Nonlinear Circuit Model Network for Power Amplifier Memory Effects
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Solution Overview
Problem
Conventional methods for linearizing nonlinear circuitry, such as power amplifiers, fail to effectively model and compensate for dynamic or memory effects, which lead to spectral regrowth and intermodulation distortion, increasing bit error rates and co-channel interference, especially in radio frequency wideband applications.
Innovation Solution
A model network comprising static nonlinear elements and linear filters is used, with transfer functions calculated from two-tone measurements, employing memory polynomials and Volterra kernels to describe nonlinear behavior, allowing for the identification and compensation of memory effects without requiring costly adaptive algorithms or complex measurement systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If power amplifiers are driven in the nonlinear region to increase efficiency, then energy efficiency is improved, but spectral regrowth and intermodulation distortion increase causing higher bit error rates and co-channel interference
Solution Approach 1:
A predistorter is placed before the power amplifier to pre-compensate for nonlinearities by applying the inverse of the expected distortion. The predistorter modifies the input signal such that when it passes through the nonlinear power amplifier, the overall system response becomes linear, thereby preventing spectral regrowth and intermodulation distortion before they occur.
Solution Approach 2:
An adaptive feedback mechanism is employed where the system continuously measures the actual output of the power amplifier and adjusts the predistorter coefficients to minimize distortion. This closed-loop approach allows the system to adapt to changing conditions and maintain optimal linearization performance while operating in the nonlinear region.
2Device complexity
If conventional static AM/AM and AM/PM conversion models are used to model nonlinear circuitry, then model simplicity is maintained, but dynamic or memory effects cannot be modeled
Solution Approach 1:
The model transitions from static to dynamic by incorporating time-varying coefficients that adapt to changing signal conditions. The predistorter coefficients are updated continuously based on recent signal statistics, allowing the model to capture memory effects and dynamic behavior while maintaining a relatively simple structural framework.
Solution Approach 2:
The model uses adjustable parameters (coefficients) that change based on signal characteristics and operating conditions. By varying these parameters adaptively, the model can accurately represent both static nonlinearities and dynamic memory effects without requiring a fundamentally complex structure, thus balancing simplicity and accuracy.
3Reliability
If adaptive algorithms like LMS or RLS are used to model dynamic effects, then modeling accuracy is improved, but computational efforts and system requirements increase significantly
Solution Approach 1:
Instead of implementing full adaptive algorithms that process all signal details, the system applies a simplified approach that captures the essential memory effects with reduced computational effort. The predistorter uses a limited set of adjustable coefficients updated based on key signal characteristics, providing sufficient accuracy for dynamic effects without the heavy computational burden of complete adaptive algorithms.
Solution Approach 2:
The solution extracts only the most critical components needed to model memory effects, rather than implementing complete adaptive algorithms. By focusing on the dominant dynamic characteristics and representing them with simplified mathematical models and limited coefficients, the system achieves acceptable modeling accuracy with significantly reduced computational complexity and simpler hardware requirements.
Data Source
AI summary
A model network of a nonlinear circuitry includes one or more static nonlinear elements and a plurality of linear filters with transfer functions. A method for determining the model network includes performing an input amplitude-to-output amplitude measurement of the nonlinear circuitry and performing an input amplitude-to-output phase measurement of the nonlinear circuitry. The transfer functions are calculated on the basis of results of the input amplitude-to-output amplitude measurement and input amplitude-to-output phase measurement.