Hybrid DPD Actuator for RF Power Amplifier Linearity
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Solution Overview
Problem
Radio frequency (RF) power amplifiers face challenges in achieving linearity due to their nonlinear behavior, especially at high power levels, which results in distortion and reduced efficiency, and existing digital predistortion (DPD) methods struggle with accuracy and efficiency, particularly with increasing sampling rates and complex signal dynamics.
Innovation Solution
A hybrid basis-function-based and neural network-based DPD system that combines a basis-function-based actuator and a neural network-based actuator to predistort input signals for RF power amplifiers, using Volterra series and General Memory Polynomial models, with a combiner to generate a predistorted signal, allowing for improved linearity and efficiency by modeling complex nonlinear characteristics and long-term memory effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional DPD methods are used, then implementation is simpler, but linearity and distortion reduction are insufficient
Solution Approach 1:
The patent combines two different DPD approaches (basis-function-based and neural network-based actuators) into a single hybrid system. The basis-function actuator handles short-term memory effects while the neural network actuator handles long-term memory effects, merging their capabilities to achieve superior linearity and distortion reduction compared to conventional single-approach DPD methods.
2Measurement precision
If higher sampling rates are used, then signal accuracy is improved, but processing complexity and resource requirements increase
Solution Approach 1:
The patent segments the DPD processing into two parallel paths operating at different sampling rates. The basis-function actuator processes at the full signal sampling rate to capture short-term dynamics, while the neural network actuator processes at a reduced sampling rate to handle long-term memory effects. This segmentation maintains signal accuracy where needed while reducing overall processing complexity.
3Power
If PA operates at high power levels, then output power is improved, but linearity deteriorates due to nonlinear behavior
Solution Approach 1:
The patent applies preliminary anti-action by using the hybrid DPD system to pre-distort the input signal in a way that anticipates and counteracts the nonlinear distortion that will be introduced by the high-power PA. The basis-function actuator compensates for immediate nonlinear effects while the neural network actuator compensates for slower varying nonlinear characteristics, enabling the PA to operate at high power levels while maintaining linearity.
Data Source
AI summary
Systems, devices, and methods related to hybrid basis function, neural network-based digital predistortion (DPD) are provided. An example apparatus for a radio frequency (RF) transceiver includes a digital predistortion (DPD) actuator to receive an input signal associated with a nonlinear component of the RF transceiver and output a predistorted signal. The DPD actuator includes a basis-function-based actuator to perform a first DPD operation using a set of basis functions associated with a first nonlinear characteristic of the nonlinear component. The DPD actuator further includes a neural network-based actuator to perform a second DPD operation using a first neural network associated with a second nonlinear characteristic of the nonlinear component. The predistorted signal is based on a first output signal of the basis-function-based actuator and a second output signal of the neural network-based actuator.