Integrating Volterra series model and deep neural networks to equalize nonlinear power amplifiers

Patent metadata

Field	Value
Publication number	US-12273221-B2
Application number	US-202318395704-A
Country	US
Kind code	B2
Filing date	Dec 25, 2023
Priority date	Mar 15, 2019
Publication date	Apr 8, 2025
Grant date	Apr 8, 2025

Abstract

The nonlinearity of power amplifiers (PAs) has been a severe constraint in performance of modern wireless transceivers. This problem is even more challenging for the fifth generation (5G) cellular system since 5G signals have extremely high peak to average power ratio. Nonlinear equalizers that exploit both deep neural networks (DNNs) and Volterra series models are provided to mitigate PA nonlinear distortions. The DNN equalizer architecture consists of multiple convolutional layers. The input features are designed according to the Volterra series model of nonlinear PAs. This enables the DNN equalizer to effectively mitigate nonlinear PA distortions while avoiding over-fitting under limited training data. The non-linear equalizers demonstrate superior performance over conventional nonlinear equalization approaches.

First claim

The invention claimed is:

1. A method of compensating for non-linear distortion from a power amplifier having a non-linearity, comprising:
digitizing a non-linearly distorted signal from a radio frequency receiver;
transforming the digitized non-linearly distorted signal into a series expansion comprising at least third order terms with memory with at least one automated processor, each respective term representing an incremental non-linearity order and an associated delay;
processing the series expansion with a deep neural network, comprising at least one dropout layer, trained with respect to the non-linear distortion to produce an equalized output signal; and
extracting a set of information symbols from the equalized output signal with a demodulator.

2. The method according to claim 1 , wherein the neural network is trained with training data comprising a set of small amplitude training signals to estimate a channel response independent of the power amplifier and a set of large amplitude training signals associated with the power amplifier non-linearity.

3. The method according to claim 1 , wherein the non-linearly distorted signal comprises an orthogonal frequency division multiplexed signal.

4. The method according to claim 1 , wherein the series expansion of at least third order with memory comprises a Volterra series expansion.

5. The method according to claim 4 ,
wherein the terms of the Volterra series expansion are defined by:

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and
the neural network is a multi-layer feedforward neural network trained with respect to the channel non-linear distortion to produce:

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where:
y(n) represents the non-linearly distorted communication;
z(n) is the equalized output signal;
d is a memory depth parameter;
D is a total memory length;
k is a respective nonlinearity order;
P is a total nonlinearity order;
k is a nonlinear order;
b kd are nonlinear response parameters; and
r(n) is a channel response to the non-linearly distorted signal y(n).

6. The method according to claim 1 , wherein the series expansion of at least third order with memory comprises at least fifth order terms, and the neural network has at least two convolutional network layers.

7. The method according to claim 1 , wherein the neural network comprises at least three hidden layers, each hidden layer comprising at least 10 feature maps, and a fully connected layer subseque

Assignees

• Univ New York State Res Found

Inventors

• Li Xiaohua
• Thompson Robert

Classifications

• G06N3/09

  Supervised learning · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• H03F1/32

  Modifications of amplifiers to reduce non-linear distortion (by negative feedback H03F1/34) · CPC title

• G06N3/08

  Learning methods · CPC title

• G06N3/04

  Architecture, e.g. interconnection topology · CPC title