AI-Predicted Digital Predistortion for Fast PA Convergence
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Solution Overview
Problem
Existing digital predistortion (DPD) methods for power amplifiers (PAs) face challenges in adapting to dynamic changes in linearization characteristics, leading to prolonged convergence times, degraded throughput, increased computing complexity, and reduced PA efficiency.
Innovation Solution
A DPD method that predicts future traffic conditions using an AI module to determine PA-related parameters, such as DPD coefficients and biasing configurations, allowing for pre-emptive adaptation to minimize convergence time and maintain PA efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DPD passively follows and adapts to dynamic changes in linearization characteristic, then DPD can maintain linearity performance, but convergence time increases and throughput deteriorates
Solution Approach 1:
The patent applies preliminary action by predicting future traffic conditions and proactively adjusting DPD parameters before actual changes occur. The system uses an AI module to forecast traffic patterns and pre-adjusts predistortion coefficients, allowing DPD to maintain linearity performance without undergoing lengthy convergence processes when traffic conditions actually change.
2Reliability
If existing DPD algorithm optimization approaches are applied, then DPD performance can be improved, but computing complexity and power consumption increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting DPD algorithm parameters based on predicted traffic conditions. Instead of using fixed complex optimization algorithms, the system modifies key parameters such as predistortion coefficients and adaptation step sizes according to forecasted traffic patterns, achieving improved DPD performance with controlled computing complexity.
3Reliability
If DPD converges frequently to adapt to changing conditions, then linearity performance is maintained, but throughput deteriorates and implementation complexity increases
Solution Approach 1:
The patent applies preliminary action by using AI-based traffic prediction to anticipate when DPD convergence is actually needed. The system predicts future traffic conditions and proactively adjusts parameters only when significant changes are forecasted, avoiding frequent unnecessary convergence operations that would degrade throughput while still maintaining linearity performance when it matters.
4Use of energy by moving object
If PA biasing configuration is adjusted to improve efficiency, then PA efficiency increases, but DPD performance may be impacted
Solution Approach 1:
The patent applies preliminary action by predicting traffic conditions and proactively configuring PA biasing and DPD parameters together before operation. The AI module forecasts traffic patterns and simultaneously determines optimal PA voltage drain, voltage gate, and DPD coefficients, ensuring that efficiency improvements through biasing adjustments do not compromise DPD performance.
Data Source
AI summary
Embodiments of the present disclosure provide digital predistortion method and digital predistortion apparatus. The digital predistortion method for power amplifier, PA, comprises: in accordance with a predicted traffic condition associated with a future time, determining PA related parameters for the future time; and applying the determined PA related parameters when the future time comes.