Orthogonal Basis Functions for Power Amplifier Predistortion
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Solution Overview
Problem
Radio-frequency power amplifiers for communications face a trade-off between linearity and efficiency, with non-linear responses at saturation points leading to distortion and out-of-band emissions, which existing digital predistortion techniques struggle to fully address, especially when considering memory effects and varying input signal distributions.
Innovation Solution
A predistortion system using an orthogonal basis function set is developed, where model coefficients for a two-dimensional lattice prediction model are computed from input signal samples to generate predistorted signals, customizing the basis function set to the input signal distribution and improving the conditioning of matrices for weighting coefficient evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If power amplifiers are operated at or near the saturation point to improve efficiency, then energy efficiency is improved, but linearity deteriorates causing non-linear response and distortion
Solution Approach 1:
The predistorter applies preliminary distortion to the input signal before it reaches the power amplifier. By pre-distorting the signal in the opposite direction of the expected amplifier distortion, the system compensates for non-linearities before they occur, enabling the amplifier to operate efficiently at saturation while maintaining overall linearity
Solution Approach 2:
The system dynamically adjusts predistortion parameters based on the input signal distribution and amplifier operating conditions. By changing the predistortion characteristics to match the actual signal conditions, the system maintains optimal compensation across varying operating points, addressing both efficiency and linearity requirements
2Manufacturing precision
If traditional digital predistortion techniques are used to compensate for distortion, then linearity is improved, but performance deteriorates when memory effects and varying input signal distributions are not adequately addressed
Solution Approach 1:
The predistorter transitions from static to dynamic operation by continuously adapting its characteristics based on real-time signal conditions. The system monitors input signal distribution and amplifier performance, then adjusts predistortion parameters dynamically to maintain optimal compensation under varying conditions including memory effects and changing signal statistics
Solution Approach 2:
The system employs feedback mechanisms to monitor the actual output signal and compare it with the desired output. This feedback information is used to continuously refine and update the predistortion parameters, ensuring accurate compensation even when signal conditions change or memory effects are present
3Device complexity
If non-orthogonal basis functions are used in predistortion modeling, then model construction is simpler, but numerical stability deteriorates leading to poor conditioning of matrices for weighting coefficient evaluation
Solution Approach 1:
The system transforms the basis functions from non-orthogonal to orthogonal form through parameter transformation. By applying orthogonalization to the basis function set, the system improves the conditioning of the coefficient evaluation matrices, leading to more stable and accurate weight calculations while maintaining model effectiveness
Data Source
AI summary
A predistorter applies a distortion function to an input signal to predistort the input signal. The output of the distortion function is modeled as the sum of the output signals from the orthogonal basis functions weighted by corresponding weighting coefficients. Techniques are described for orthogonalizing the basis function output signals depending on the distribution of the input signal.