Power Amplifier Modeling With Historical-Time Inputs for Distortion Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional power amplifier models struggle with poor generalization ability and prediction accuracy, especially when applied to complex distortion characteristics of modern power amplifiers, leading to unsatisfactory performance in practical scenarios.
Innovation Solution
The method involves multiple iterations of training an initial sub-model using labeled data and input data, including historical-time inputs, to generate multiple target sub-models, which are combined to form a final power amplifier model, optimizing the model structure and training process to enhance generalization and prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional power amplifier models are used, then the model structure is simple, but the generalization ability and prediction accuracy are poor
Solution Approach 1:
The patent divides the power amplifier model into multiple sub-models, each responsible for different aspects of the distortion characteristics. This segmentation allows each sub-model to focus on specific patterns, improving overall prediction accuracy while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent introduces historical-time input data as an additional dimension to the model, incorporating temporal information beyond just current input signals. This dimensional expansion enables the model to capture dynamic behavior and improve generalization ability by learning from past states
2Adaptability or versatility
If conventional power amplifier models are used, then the training process is simple, but the generalization ability is poor
Solution Approach 1:
The patent performs preliminary actions by pre-processing historical-time input data and organizing training datasets before actual model training. This preparation includes selecting relevant historical time points and structuring data in a format optimized for training, which streamlines the subsequent training process and improves generalization ability
Solution Approach 2:
The patent implements dynamic training by iteratively adjusting model parameters based on performance metrics and incorporating adaptive learning rates. The training process dynamically adapts to the complexity of the data, improving generalization ability while optimizing training time through intelligent resource allocation
3Reliability
If historical-time input data is incorporated, then the model captures dynamic behavior better, but the data processing complexity increases
Solution Approach 1:
The patent extracts only the most relevant historical-time input data features that contribute to dynamic behavior capture, rather than processing all possible historical data. This selective extraction reduces data processing complexity while maintaining model robustness by focusing on critical temporal patterns
Solution Approach 2:
The patent applies different processing strategies to different portions of historical-time data based on their importance. Critical recent historical data receives more sophisticated processing, while older less relevant data undergoes simpler processing, optimizing the balance between model robustness and processing complexity
Data Source
AI summary
A method and apparatus for acquiring a power amplifier model, a power amplifier model, a storage medium, and a program product are disclosed. The method may include: acquiring an initial sub-model, labeled data, and input data of a power amplifier; performing, according to the labeled data and the input data, iterative training on the initial sub-model until an iteration stop condition is reached, and after each iterative training is completed, obtaining one target sub-model; and obtaining a power amplifier model according to at least one of the target sub-models.