Digital Pre-Distortion Alignment for Nonlinear Amplifier Linearity
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Solution Overview
Problem
Existing digital pre-distortion methods for linearizing non-linear amplifiers face limitations due to assumptions about delay and phase alignment, which can lead to misalignment and instability if other linear distortion products are present.
Innovation Solution
An adaptive pre-distorter system that uses a linear finite impulse response (FIR) filter to align input and feedback signals by minimizing a cost function, allowing for enhanced alignment and updating of pre-distortion parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional digital pre-distortion methods are used with simple delay and phase alignment assumptions, then the system complexity is low, but linearity and stability deteriorate when other linear distortion products are present
Solution Approach 1:
The alignment process is segmented into two distinct stages: first applying delay and complex gain alignment to correct basic timing and phase issues, then applying IIR filter alignment to address remaining linear distortion products. This segmentation allows each alignment stage to focus on specific types of distortions, improving overall linearity while managing system complexity through modular processing
Solution Approach 2:
The delay and complex gain alignment is performed as a preliminary action before the IIR filter alignment. By pre-correcting the basic delay and phase misalignments, the subsequent IIR filter alignment can focus specifically on correcting linear distortion products, thereby improving linearity without significantly increasing the overall system complexity
2Reliability
If conventional digital pre-distortion methods are used with simple delay and phase alignment assumptions, then the device complexity is low, but stability deteriorates when other linear distortion products are present
Solution Approach 1:
The alignment process is segmented into two distinct stages: first applying delay and complex gain alignment to correct basic timing and phase issues, then applying IIR filter alignment to address remaining linear distortion products. This segmentation allows each alignment stage to focus on specific types of distortions, improving overall linearity while managing system complexity through modular processing
Solution Approach 2:
The delay and complex gain alignment is performed as a preliminary action before the IIR filter alignment. By pre-correcting the basic delay and phase misalignments, the subsequent IIR filter alignment can focus specifically on correcting linear distortion products, thereby improving linearity without significantly increasing the overall system complexity
3Manufacturing precision
If IIR filter alignment is applied to correct linear distortion products, then linearity is improved, but computational complexity increases
Solution Approach 1:
The delay and complex gain alignment is performed as a preliminary action before the IIR filter alignment. By pre-correcting the basic delay and phase misalignments, the subsequent IIR filter alignment can focus specifically on correcting linear distortion products, thereby improving linearity without significantly increasing the overall system complexity
Solution Approach 2:
The IIR filter alignment parameters are adapted dynamically based on the specific characteristics of the linear distortion products present in the system. Rather than using fixed alignment parameters, the system adjusts the IIR filter coefficients to match the actual distortion characteristics, improving linearity correction effectiveness while managing computational complexity through adaptive rather than exhaustive processing
Data Source
AI summary
A non-linear power amplifier generates an amplified output signal based on a pre-distorted signal generated by a digital pre-distorter (DPD) based on an input signal. A feedback path generates a feedback signal based on the amplified output signal. The feedback signal is aligned with the input signal, or vice versa, and the aligned signals are used to adaptively update the DPD processing. In particular, a linear FIR filter is estimated to minimize a cost function based on the input and feedback signals. Depending on how the filter is generated, the filter is applied to the input signal or to the feedback signal to generate the aligned input and feedback signals.