Dynamic Digital Predistortion for GaN TDD 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 under Time Division Duplexing (TDD) operations, leading to inefficiencies and increased complexity due to hardware and software scalability issues.
Innovation Solution
A dynamic digital predistortion system utilizing a primary model combined with one or more auxiliary models, adjusted by a weighting function to capture transitions in power amplifier states, reducing complexity while enhancing accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single static predistortion model is used, then the system is simple to implement, but it cannot accurately compensate for dynamic characteristics of GaN power amplifiers under TDD operations
Solution Approach 1:
The patent implements dynamic predistortion by transitioning from a static model to a time-varying model that adapts to changing power amplifier states. The system uses time-dependent weighting functions to dynamically adjust the contribution of different predistortion models based on the current operational phase, enabling accurate compensation for dynamic characteristics while maintaining manageable complexity through structured model combination.
Solution Approach 2:
The patent segments the predistortion compensation into multiple components: a first predistortion model for baseline compensation and a second predistortion model for dynamic state compensation. By dividing the compensation task into separate models that are selectively combined using weighting functions, the system achieves high accuracy without requiring a single overly complex model, thus resolving the contradiction between precision and complexity.
2Measurement precision
If multiple predistortion models are combined to capture dynamic states, then predistortion accuracy improves, but hardware and software scalability becomes problematic
Solution Approach 1:
The patent creates a universal predistortion framework where the same architectural structure (multiple models with time-dependent weighting) can handle various operational scenarios including different TDD configurations, frequency bands, and power amplifier states. This multi-functional approach allows the system to maintain high accuracy across diverse conditions while preserving scalability, as the framework itself rather than specific model implementations determines the solution.
Solution Approach 2:
The patent achieves adaptability through parameter changes in the weighting functions that control model combination. By adjusting the time-dependent weighting parameters based on operational phase and other conditions, the system can accurately compensate for different dynamic states without requiring separate hardware or software configurations for each scenario, thus maintaining scalability while improving accuracy.
3Productivity
If dynamic adjustment to power amplifier states is implemented, then predistortion efficiency improves, but circuit area and power consumption increase
Solution Approach 1:
The patent implements dynamic adjustment through time-dependent weighting functions that automatically adapt the predistortion model combination based on the current power amplifier state and operational phase. This dynamic approach improves predistortion efficiency by ensuring the most appropriate model is active at each moment, while the mathematical nature of the weighting functions allows implementation in efficient digital signal processing hardware with minimal additional power consumption compared to static systems.
Data Source
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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.