Digital Doherty PA Auto-Tuning With Adaptive Pre-Distortion
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Solution Overview
Problem
Traditional analog Doherty power amplifiers suffer from inefficiencies and limited operational bandwidth due to fixed configurations and the need for manual tuning, which is not adaptable to varying input conditions and circuit states, making them cumbersome to optimize for optimal performance.
Innovation Solution
A digital Doherty power amplifier system with an autotuning controller that uses a data-driven optimization method to adaptively adjust the learning cost function and optimize parameters for both the power amplifier and digital pre-distortion system, allowing for flexible operation across different signal conditions and environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional analog DPA design with fixed configuration is used, then the structure is simple, but the adaptability to varying input conditions and circuit states deteriorates
Solution Approach 1:
The patent implements dynamic adaptability by introducing autotuning controllers that continuously adjust circuit parameters (phase alignment, power splitting ratio, gate bias) based on real-time operating conditions. This transforms the fixed analog DPA into a dynamically adaptable system that can respond to varying input signals, frequencies, and environmental changes while maintaining relatively simple hardware architecture.
Solution Approach 2:
The system changes operational parameters dynamically through digital control. The autotuning controller modifies phase alignment angles, power splitting ratios, and bias conditions based on measured performance metrics and desired operating points. This allows the same hardware structure to adapt to different modulation formats, power levels, and frequency bands without physical reconfiguration.
2Reliability
If manual tuning is performed to optimize DPA performance, then the performance can be optimized for fixed operating conditions, but the ease of operation deteriorates and the system cannot adapt to changing conditions
Solution Approach 1:
The patent implements self-service through autotuning controllers that automatically measure system performance, compare it against desired targets, and adjust control parameters without human intervention. The system monitors output signals, calculates performance metrics (linearity, efficiency), and autonomously modifies phase alignment, power splitting, and bias conditions to maintain optimal operation across changing conditions.
Solution Approach 2:
The system employs feedback mechanisms where the autotuning controller continuously monitors output signals from the DPA, evaluates performance metrics such as linearity and efficiency, and uses this information to adjust control parameters. This closed-loop feedback enables automatic adaptation to varying input conditions, eliminating the need for manual retuning when operating conditions change.
3Reliability
If the compensation circuit is made complex to achieve optimal performance, then the performance can be improved, but the device complexity increases making design cumbersome
Solution Approach 1:
The patent replaces complex analog compensation circuits with digital signal processing and software-based control. Instead of using intricate analog networks for phase and amplitude compensation, the system uses digital algorithms in the autotuning controller to calculate and apply corrective adjustments. This substitution reduces hardware complexity while maintaining or improving performance adaptability.
Solution Approach 2:
The autotuning controller serves multiple functions: it performs phase alignment adjustment, power splitting optimization, gate bias control, and performance monitoring all within a single integrated device. This multi-functionality eliminates the need for separate compensation circuits for each function, reducing overall device complexity while achieving comprehensive performance optimization.
Data Source
AI summary
An autotuning controller is provided for improving power efficiency and linearity of digital power amplifiers (DPAs). The controller includes an interface including input and output terminals connected to the DPAs, the interface being configured to acquire input signals and output signals, a digital pre-distortion (DPD)-DPA adaptive controller including a processor and a memory running and storing a DPD algorithm, an efficiency enhancement method and a learning cost function. The DPD adaptive controller is configured to perform steps of computing DPD coefficients to define a learning cost function based on a DPD model by use of a data-driven optimization method, wherein the leaning cost function includes both variables of a DDA performance and a DPD performance, updating the learning cost function based on the DPD performance, optimizing the updated learning cost function by solving the updated learning cost function with respect to the variables of the DDA performance, and providing optimal parameters for DPA and DPD via the interface.