Neural Waveform Predistortion for PA Non-Linearity Compensation
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Solution Overview
Problem
Current wireless communication technologies, such as LTE and 5G, face challenges in addressing power amplifier non-linearity, which affects transmission efficiency and spectral power ratio, leading to reduced power usage and increased distortion.
Innovation Solution
The implementation of encoder and decoder neural networks to transform transmit waveforms, ensuring the power amplifier operates within a linear or near-saturation region, thereby mitigating non-linearity and optimizing power usage through waveform clipping and distortion error compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If power amplifier operates at high power to improve transmission efficiency, then power usage increases, but non-linearity 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-linear effects that will occur during amplification. This preliminary action allows the system to operate the power amplifier at high power levels while maintaining linear output characteristics.
Solution Approach 2:
The neural network acts as an intermediary between the digital signal and the power amplifier, transforming the signal in a way that compensates for the amplifier's non-linear characteristics. The decoder neural network at the receiver end further processes the signal to recover the original information, mediating the effects of non-linearity.
2Object-generated harmful factors
If traditional linear operation is used to reduce distortion, then power usage decreases, but transmission efficiency reduces
Solution Approach 1:
The system changes the parameters of the transmit waveform through neural network transformation, creating a pre-distorted signal that compensates for power amplifier non-linearity. This allows the power amplifier to operate in a more efficient region while maintaining acceptable distortion levels through intelligent signal processing.
Data Source
AI summary
A method of wireless communication by a transmitting device transforms a transmit waveform by an encoder neural network to control power amplifier (PA) operation with respect to non-linearities. The method also transmits the transformed transmit waveform across a propagation channel. A method of wireless communication by a receiving device receives a waveform transformed by an encoder neural network. The method also recovers, with a decoder neural network, the encoder input symbols from the received waveform. A transmitting device for wireless communication calculates distortion error based on a non-distorted digital transmit waveform and a distorted digital transmit waveform. The transmitting device also 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 power amplifier (PA) non-linearity.