Direct Learning Predistortion Control for PA Divergence Stability
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Solution Overview
Problem
The Direct Learning Algorithm (DLA) for power amplifiers in wireless communication faces instability due to numerical error accumulation, leading to divergence and increased inter-modulation products, which violates 3GPP specifications and limits power savings.
Innovation Solution
An adaptive method for controlling power amplifiers using a pre-distorter with an adaptive direct learning algorithm, where parameters are adapted in steps based on error divergence detection, and error scaling factors are introduced to stabilize the system, preventing divergence by restoring stored states or adjusting error gradients and noise factors in the auto-correlation matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the power amplifier is operated at compressed regions to save power, then power consumption is reduced, but non-linearity increases causing increased inter-modulation products that violate 3GPP specifications
Solution Approach 1:
The patent applies Digital Pre-Distortion (DPD) to convert the harmful non-linearity of the power amplifier into a beneficial effect. By pre-distorting the input signal with an inverse characteristic of the amplifier's non-linearity, the system transforms the distortion that would normally violate specifications into a mechanism that enables efficient compressed-region operation while meeting 3GPP requirements
Solution Approach 2:
The pre-distorter applies preliminary correction to the input signal before it reaches the power amplifier. By pre-compensating for the expected non-linear distortion, the system prepares the signal in advance so that after amplification, the output meets linearity requirements despite operating in the compressed region
2Reliability
If continuous adaptation of the DPD algorithm is performed to track power amplifier changes, then tracking accuracy is improved, but numerical error accumulation causes instability and divergence
Solution Approach 1:
The patent implements a feedback mechanism that monitors the error between the actual and desired amplifier characteristics during adaptation. By using this error feedback to guide parameter updates in the pre-distorter, the system achieves continuous tracking of amplifier changes while maintaining stability through controlled adaptation steps
Solution Approach 2:
The adaptation process uses partial updates of the pre-distorter parameters rather than full continuous adaptation. By applying smaller, more frequent adjustments instead of large continuous changes, the system reduces numerical error accumulation while still tracking amplifier characteristics effectively
Data Source
AI summary
The present invention addresses method, apparatus and computer program product for controlling a Direct Learning Algorithm. Thereby, a power amplifier operating in a non-linear state is controlled. A signal to be amplified is input to a pre-distorter provided for compensating for non-linearity of the power amplifier. The pre-distorted output signal is forwarded from the pre-distorter to the power amplifier. Parameters of the pre-distorter are adapted in plural steps based on an error between a linearized signal output from the power amplifier and the signal to be amplified using an adaptive direct learning algorithm. It is detected whether the error diverges; and adapting of the parameters of the pre-distorter is stopped when it is determined that the error is diverging.