Digital Predistortion Weighting for Dynamic GaN Power Amplifiers
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Solution Overview
Problem
Existing digital predistortion systems face challenges in accurately compensating for the dynamic characteristics of Gallium Nitride (GaN) power amplifiers used in transceivers, particularly in time-division duplexing (TDD) operations, leading to inefficiencies and increased complexity due to factors like variable bandwidth and power fluctuations.
Innovation Solution
A dynamic digital predistortion system employing a primary model and one or more auxiliary models, adjusted by a weighting function, to enhance accuracy and reduce complexity, using models like Volterra-series or neural networks to adapt to power amplifier states and signal characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single static predistortion model is used, then the circuit complexity is low, but the predistortion accuracy deteriorates under dynamic power amplifier conditions
Solution Approach 1:
The patent divides the predistortion system into multiple static models (first predistortion model, second predistortion model, etc.), each optimized for specific operating conditions. These segmented models are selectively applied based on real-time amplifier state, achieving high accuracy across dynamic conditions without requiring a single complex adaptive model.
Solution Approach 2:
The system dynamically switches between different predistortion models based on real-time amplifier operating conditions (power level, temperature, bandwidth). This dynamic model selection mechanism allows the system to maintain high predistortion accuracy under varying conditions while keeping each individual model relatively simple.
2Adaptability or versatility
If multiple predistortion models are used to cover different operating conditions, then the predistortion accuracy improves, but the device complexity increases
Solution Approach 1:
The patent segments the operating conditions into distinct regions (e.g., different power levels, temperature ranges, bandwidth conditions) and assigns specific predistortion models to each segment. This segmentation enables the system to adapt to diverse dynamic conditions while maintaining manageable complexity through structured model organization.
Solution Approach 2:
The patent creates a universal predistortion framework that can handle multiple operating conditions through a standardized model selection and switching mechanism. This multi-functional architecture allows the same system structure to serve various predistortion needs across different amplifier states, reducing overall system complexity.
3Measurement precision
If complex adaptive models are used to track power amplifier state changes, then the predistortion accuracy improves, but the power consumption increases
Solution Approach 1:
The patent pre-characterizes the power amplifier's dynamic behavior during the calibration phase, storing lookup tables and model parameters that capture temperature drift, power variations, and bandwidth effects. During operation, the system simply retrieves and applies these pre-computed corrections rather than performing complex real-time calculations, significantly reducing power consumption while maintaining high tracking accuracy.
Solution Approach 2:
The system uses lightweight dynamic model selection based on simple threshold comparisons of operating conditions rather than computationally intensive adaptive algorithms. This dynamic approach achieves accurate tracking of amplifier state changes with minimal processing power, reducing energy consumption compared to continuous complex adaptation.
4Reliability
If the predistortion system is continuously updated to track amplifier state, then the predistortion accuracy is maintained, but the processing time increases
Solution Approach 1:
The patent performs comprehensive amplifier characterization and model generation during an initial calibration phase, creating lookup tables and storing model parameters that capture the amplifier's behavior across its entire operating range. During normal operation, the system rapidly retrieves pre-computed correction values based on current operating conditions, maintaining high compensation reliability with minimal processing time.
Solution Approach 2:
The system performs model updates and recalibration periodically or under specific trigger conditions rather than continuously, reducing processing overhead while maintaining compensation reliability. Between updates, the system efficiently applies stored model parameters, achieving reliable predistortion with reduced processing time requirements.
Data Source
AI summary
A device may include a first model implemented using a first processing circuit configured to implement a transformation function to generate an initial predistortion signal based on an input signal corresponding to an output of a power amplifier. The device may include a second model implemented using a second processing circuit configured to implement a modification function to modify the initial predistortion signal. A device may include a configurable multiplier configured to weight an output of the second model based at least in part on a weighting value to obtain a weighted output. A device may include a combiner configured to combine the initial predistortion signal that is output by the first model with the weighted output to generate a digital predistortion signal for the power amplifier in communication with the digital predistortion system.