Power Amplifier Predistortion Using Operation Status Signals
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Solution Overview
Problem
Existing distortion compensation devices struggle to effectively compensate for distortion in transmission signals after power amplification, especially when the operation status of the power amplifier changes.
Innovation Solution
A distortion compensation device that includes processing circuitry to acquire an amplification target signal and an operation status signal, and uses a neural network learning model to estimate a predistortion signal for compensating distortion, even when the operation status of the power amplifier changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a learning model is trained to compensate distortion in transmission signals after power amplification, then distortion compensation accuracy is improved under fixed operation conditions, but the system fails to adapt when the operation status of the power amplifier changes
Solution Approach 1:
The patent implements dynamic adaptation by continuously monitoring operation status signals (temperature, frequency, bias voltage) and updating the learning model's weight coefficients in real-time. This transforms the static predistortion system into a dynamic one that automatically adjusts to changing power amplifier conditions, resolving the contradiction between achieving high compensation accuracy and adapting to operation status changes.
Solution Approach 2:
The system changes the parameters of the learning model (weight coefficients) based on detected operation status variations. By modifying these parameters in response to temperature, frequency, or bias voltage changes, the system maintains accurate distortion compensation across different operating conditions, simultaneously achieving both precision and adaptability.
2Productivity
If the learning model is trained with fixed operation status data, then training speed is improved, but the predistortion signal becomes inaccurate when operation status changes
Solution Approach 1:
The system performs preliminary training with fixed operation status data to establish initial weight coefficients quickly. This preliminary action provides a good starting point that can be rapidly refined through online adaptation when operation status changes occur, balancing training speed with ongoing accuracy.
Solution Approach 2:
The system implements feedback mechanisms where the actual distortion compensation performance is monitored and used to continuously adjust the learning model's weight coefficients. This feedback loop ensures that while initial training can be fast, the system continuously improves and maintains accuracy as operation status changes, resolving the contradiction between training speed and signal accuracy.
Data Source
AI summary
A distortion compensation device includes: an amplification target signal acquiring unit that acquires an amplification target signal to which power amplification by a power amplifier is performed; and an operation status signal acquiring unit that acquires an operation status signal indicating an operation status of the power amplifier. In addition, the distortion compensation device includes a distortion compensation unit that estimates a signal in which distortion is added to the amplification target signal acquired by the amplification target signal acquiring unit as a predistortion signal for compensating distortion generated in the amplification target signal after power amplification by the power amplifier on the basis of the amplification target signal acquired by the amplification target signal acquiring unit and the operation status signal acquired by the operation status signal acquiring unit, and outputs the predistortion signal to the power amplifier.