Neural-Network Digital Predistortion for Adaptive Doherty Amplifiers
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional analog Doherty Power Amplifiers (DPAs) face limitations in energy efficiency and operational bandwidth due to their fixed configuration and inability to adapt to varying input signals and circuit states, requiring cumbersome manual tuning and lacking flexibility to optimize performance across different conditions.
Innovation Solution
A Digital Doherty Power Amplifier (DDPA) system with an auto-tuning controller that uses a neural network to adaptively optimize Digital Pre-Distortion (DPD) and Doherty amplifier coefficients, allowing for flexible operation across various signal conditions and environments, thereby enhancing efficiency and gain while maintaining linearity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional analog DPA design with fixed configuration is used, then manufacturing simplicity is maintained, but adaptability to varying input signals and circuit states deteriorates
Solution Approach 1:
The patent implements dynamic adaptability by introducing digital signal processing components that can adjust circuit parameters in real-time. The digital pre-distortion (DPD) system and auto-tuning controller dynamically modify control parameters based on varying input signals and circuit states, transforming the static analog DPA into a dynamic system that adapts to changing conditions without requiring complex manual reconfiguration.
Solution Approach 2:
The patent changes physical and operational parameters of the DPA system through digital control. The auto-tuning controller adjusts control parameters such as power splitting ratios, phase alignment, and bias conditions based on measured circuit states and input signal characteristics. This parameter adaptation enables the system to optimize performance across different operating conditions while maintaining a relatively simple underlying circuit architecture.
2Reliability
If manual tuning of control parameters is performed, then optimization for fixed operating conditions is achieved, but ease of operation deteriorates due to cumbersome tuning process
Solution Approach 1:
The patent implements self-service through the auto-tuning controller that automatically adjusts control parameters without requiring manual intervention. The system measures its own circuit states (power levels, phases, temperatures) and autonomously optimizes operating parameters to maintain peak performance. This self-tuning capability eliminates the cumbersome manual tuning process while ensuring reliable performance optimization across varying operating conditions.
Solution Approach 2:
The patent incorporates feedback mechanisms where the auto-tuning controller continuously monitors circuit states and input signal characteristics, then uses this information to adjust control parameters. The DPD system also employs feedback by comparing actual output signals with desired signals and adjusting pre-distortion parameters accordingly. This closed-loop feedback approach ensures automatic optimization without manual tuning while maintaining high reliability.
3Adaptability or versatility
If analog-based DPA design is used, then device complexity is reduced, but adaptability to different bandwidths and modulation formats deteriorates
Solution Approach 1:
The patent achieves universality by designing a digital signal processing framework that can handle multiple bandwidths and modulation formats through software-based control. The DPD system and auto-tuning controller are configured to adapt to various signal types and spectral requirements without requiring hardware modifications. This multi-functional digital layer sits atop the analog RF front-end, enabling a single device to serve multiple communication standards and bandwidth requirements.
4Loss of information
If fixed phase alignment and power splitting are used, then manufacturing precision is simplified, but loss of information increases due to circuit imbalance
Solution Approach 1:
The patent replaces fixed mechanical/analog phase alignment and power splitting mechanisms with digital signal processing techniques. Instead of relying on precise physical component matching, the system uses digital algorithms to compensate for phase and amplitude imbalances. The auto-tuning controller measures actual circuit characteristics and applies corrective digital signals to eliminate imbalance effects, substituting physical precision requirements with computational correction.
Data Source
AI summary
An auto-tuning controller for improving a performance of a power amplifier system is provided. The controller includes an interface including input terminals and output terminals, the interface being configured to acquire input signal conditions of power amplifiers (PAs), a training circuit including a processor and a memory running and storing a Digital Doherty amplifier (DDA) controller (module), a DPD controller (module) and a DDA-DPD neural network (NN). The training circuit is configured to perform sampling the input signal conditions, and selecting a DPD model from a set of polynomial models for the DPD controller and a set of DDA optimization variables for the DDA controller, using optimized DPD model and DDA coefficients, wherein the optimized DPD model and DDA coefficients are provided by performing an offline optimization for the DPD model and DDA coefficients based on a predetermined optimization method, collecting the optimized DPD coefficients and optimized DDA optimization variables, generating online-DDA optimal coefficients and DPD optimal coefficients using a trained DDA-DPD NN, and updating the generated optimal DDA and DPD coefficients via the output terminals of the interface.