Digital Predistortion Architecture for PA Memory Effects
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Solution Overview
Problem
Existing wireless technologies face challenges in achieving efficient power amplifier linearity and linear system response due to increased nonlinearity and memory effects, which are not adequately captured by conventional models, leading to issues with ACLR and DPD requirements.
Innovation Solution
A multistage, multi-rate digital predistortion system using a series of compensators with parallel processing circuit blocks, each implementing transformation functions as a set of neurons, and employing machine learning to adaptively configure models for improved linearization of power amplifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional models are used for power amplifier linearization, then device complexity is reduced, but manufacturing precision and reliability deteriorate due to inadequate capture of nonlinearity and memory effects
Solution Approach 1:
The digital predistortion system is divided into multiple processing circuit blocks, each implementing a specific transformation function (e.g., memory polynomial, Volterra series). This segmentation allows each block to handle specific nonlinear and memory effects independently, improving overall linearity precision while managing complexity through modular architecture
Solution Approach 2:
The patent transitions from conventional scalar models to complex-valued neural network models that operate in the complex signal domain. This dimensional change enables the system to capture both amplitude and phase nonlinearities simultaneously, significantly improving linearity precision for modern wireless modulations
2Reliability
If more complex transformation functions are implemented to capture memory effects, then reliability improves, but device complexity increases
Solution Approach 1:
The system employs adaptive neural network models that dynamically adjust their parameters based on operating conditions such as power amplifier back-off level and signal characteristics. This dynamic adaptation maintains high reliability across varying operational states without requiring a separate complex model for each condition
Solution Approach 2:
The digital predistortion system incorporates feedback mechanisms where the actual power amplifier output is measured and used to update the neural network model parameters. This closed-loop feedback ensures the model continuously adapts to capture memory effects accurately, improving reliability while the feedback-driven learning reduces the need for overly complex predetermined models
Data Source
AI summary
An adaptable, generalized digital predistortion system that can increase the accuracy of the distortion applied to a power amplifier input signal is disclosed. Further, the digital predistortion system can be implemented using a reduced circuit area by reducing a number of coefficients used by the models applied to generate the distortion compensation signal. Further, the system can implement an improved digital predistortion adaptation engine that can improve one or more of the following metrics of a complete radio frequency (RF) analog front-end radio signal chain: error vector magnitude (EVM), adjacent channel leakage ratio (ACLR), spectrum emission mask (SEM), and power consumption of the circuits and systems. An example of circuits and systems is a radio frequency power amplifier (PA).