PA Nonlinearity Compensation Using Neural Distortion Compression
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Solution Overview
Problem
Current wireless communication technologies face challenges in addressing transmit (Tx) non-linearity, particularly in power amplifier (PA) operation, which affects the efficiency and accuracy of wireless signals.
Innovation Solution
The use of encoder and decoder neural networks to transform and recover transmit waveforms, respectively, is proposed to control PA operation and mitigate non-linearities. This involves calculating distortion error, compressing it with an auto-encoder, and transmitting it to the receiver to compensate for PA non-linearity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If power amplifier operates at high power to maximize efficiency, then power usage is improved, but non-linear distortion increases
Solution Approach 1:
The encoder neural network pre-distorts the transmit waveform before it enters the power amplifier, anticipating and compensating for the non-linearities that will occur during high-power amplification. This preliminary action allows the PA to operate efficiently in non-linear region while the pre-applied distortion correction ensures the final output remains linear.
Solution Approach 2:
The system employs feedback mechanisms where the receiver detects distortion errors in the received signal and communicates this information back to the transmitter. The encoder neural network uses this feedback to continuously adapt and refine the pre-distortion waveform, optimizing the balance between power amplifier efficiency and distortion reduction.
2Reliability
If traditional linear operation is used to maintain signal accuracy, then distortion is reduced, but power amplifier efficiency decreases
Solution Approach 1:
The patent replaces traditional mechanical/analog linearization circuits with a digital neural network-based pre-distortion system. The encoder neural network computationally transforms the input waveform to pre-compensate for PA non-linearities, substituting complex electronic linearization hardware with flexible digital signal processing that achieves both high efficiency and signal accuracy.
3Reliability
If neural network processing is added to transform waveforms, then PA operation is improved, but device complexity increases
Solution Approach 1:
The complex neural network processing is extracted and implemented as a separate encoder module that can be independently trained and optimized. By isolating the neural network functionality into a distinct component, the system manages complexity through modular design, allowing the neural network to be developed and updated without redesigning the entire transmitter system.
4Reliability
If distortion error compression is implemented to compensate for non-linearity, then communication reliability is improved, but processing complexity increases
Solution Approach 1:
The system transforms the distortion error from its original complex form into a compressed representation with reduced parameters. The encoder neural network processes the distortion error and outputs a compressed form that contains essential correction information in a more manageable parameter space, making further processing and transmission more efficient while maintaining correction effectiveness.
Data Source
AI summary
A transmitting device for wireless communication calculates distortion error based on a non-distorted digital transmit waveform and a non-linearity. The transmitting device compresses the distortion error with an encoder neural network of an auto-encoder. The transmitting device transmits, to a receiving device, the compressed distortion error to compensate for the non-linearity in a power amplifier (PA).