Base Station PA Distortion Compensation for Uplink Signal Quality
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Solution Overview
Problem
Power amplifiers in radio transmitters introduce distortions to signals, leading to inefficiencies and reduced coverage due to the trade-off between power consumption and distortion, with existing digital predistortion techniques being computationally intensive and not feasible in all scenarios.
Innovation Solution
A system utilizing pre-trained machine learning models to select and apply power amplifier distortion models for compensating distortions in uplink data transmissions, allowing for reduced power backoff and increased efficiency without the need for complex digital predistortion units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If digital predistortion techniques are used to compensate power amplifier distortion, then signal quality is improved, but device complexity and computational requirements increase significantly
Solution Approach 1:
The patent uses a machine learning model to create a computational copy of the power amplifier's distortion characteristics. Instead of implementing complex digital predistortion circuitry, the system trains a neural network to replicate the PA's nonlinear behavior, then uses this model to generate compensation signals. This copying approach simplifies the hardware while maintaining compensation effectiveness.
Solution Approach 2:
The patent replaces the traditional mechanical/electronic digital predistortion system with a software-based machine learning approach. The complex analog/digital circuitry required for real-time predistortion is substituted with a trained neural network model that can be implemented in software or firmware, significantly reducing hardware complexity while maintaining or improving compensation performance.
2Area of stationary object
If power amplifier operates at higher power levels to improve coverage, then signal coverage is improved, but distortion increases and power consumption rises
Solution Approach 1:
The patent applies preliminary anti-action by using the machine learning model to predict and compensate for distortion before it degrades the signal. The system continuously monitors PA output and uses the trained model to generate pre-compensation signals that counteract the expected nonlinear distortion, allowing the PA to operate at higher power levels without producing harmful distortion in the transmitted signal.
3Manufacturing precision
If power backoff is increased to reduce distortion, then signal quality is maintained, but power consumption increases and coverage decreases
Solution Approach 1:
The patent changes the operational parameters of the power amplifier by using machine learning-based distortion compensation. Instead of reducing power levels (power backoff) to maintain signal quality, the system adjusts the compensation parameters through the trained ML model, allowing the PA to operate at optimal power levels with reduced backoff. This maintains signal quality while significantly reducing power consumption and improving coverage.
Data Source
AI summary
Disclosed is a method comprising selecting (401), by a base station, a power amplifier distortion model from a set of power amplifier distortion models, wherein the power amplifier distortion model comprises a pre-trained machine learning model configured to compensate power amplifier distortion. The method further comprises receiving (402), by the base station, one or more uplink data transmissions from a terminal device, and compensating (403), by the base station, at least a part of the power amplifier distortion from the one or more uplink data transmissions based at least partly on the power amplifier distortion model.