Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Power amplifier pre-distortion method of complex-valued pipeline recurrent neural network model

A technology of recurrent neural network and neural network model, applied in biological neural network models, neural learning methods, improving amplifiers to reduce nonlinear distortion, etc. effects, cumbersome training algorithms, etc.

Pending Publication Date: 2020-02-07
CHONGQING UNIV
View PDF0 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of the fact that the existing neural network model cannot well represent the memory effect of the power amplifier, and when the input signal is complex-valued, a cumbersome training algorithm will be introduced to cause lengthy training time, and it is proposed that the memory effect of the power amplifier must be represented , and can process complex-valued signals, and can quickly update the parameters of the model, a power amplifier pre-distortion method for a complex-valued pipeline recursive neural network model. The specific technical scheme is as follows:

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Power amplifier pre-distortion method of complex-valued pipeline recurrent neural network model
  • Power amplifier pre-distortion method of complex-valued pipeline recurrent neural network model
  • Power amplifier pre-distortion method of complex-valued pipeline recurrent neural network model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0072] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, so as to define the protection scope of the present invention more clearly.

[0073] Such as figure 1 and figure 2 Shown: a power amplifier predistortion method of a complex-valued pipeline recursive neural network model, wherein the predistortion system used includes RF PAs, a computer, a vector signal source VSG, and a signal analyzer VSA. The power amplifier used for implementation is a Class F power amplifier. In this embodiment, the complex-valued pipeline recursive neural network model is referred to as CPRNN, and the enhanced complex-valued real-time recursive learning algorithm is referred to as ACRTRL;

[0074] Including the following steps:

[0075] S1: Use MATLAB to generate an LTE dual-carrier signal, which ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a power amplifier pre-distortion method for a complex-valued pipeline recurrent neural network model. The power amplifier pre-distortion method comprises the steps: carrying out the modeling of a power amplifier behavior model through the complex-valued pipeline recurrent neural network model, solving a pre-distorter module, and carrying out the pre-distortion operation ofa power amplifier input signal, and specifically includes the steps: firstly, part of input and output signals of a power amplifier are used as test signals, and forward modeling is conducted on the power amplifier, and the model weight is optimized through an enhanced complex value real-time recursive learning algorithm, and the optimal model weight is obtained, and the nonlinear and memory capacity of the power amplifier represented by the model is checked; secondly, inversion is performed on the model so as to perform reverse modeling on the power amplifier to obtain a predistorter structure; and finally, the input signal of the power amplifier obtains a signal subjected to pre-distortion compensation through the predistorter structure, and sends the signal into the power amplifier, sothat the adjacent channel power ratio of the output signal of the power amplifier obtained at the moment can be remarkably improved.

Description

technical field [0001] The invention relates to the field of digital pre-distortion in digital signal processing, in particular to a power amplifier pre-distortion method for a complex-valued pipeline recursive neural network model. Background technique [0002] With the rapid development of wireless communication technology, especially when 4G and 5G mobile communications are widely popularized, the demand for spectrum resources in modern communication systems continues to increase. In order to meet this requirement, high-order modulation techniques are widely used in communication systems, but such modulation techniques will make it more difficult to design RF power amplifiers, which play a crucial role in RF front-end components, and will also cause spectrum regrowth . In order to meet the needs of modern wireless communication technology, a series of power amplifier linearization and efficiency enhancement technologies have been proposed, such as feedforward technology ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/367H03F1/02H03F1/32H03F3/195H03F3/213G06N3/04G06N3/08
CPCH03F1/3241H03F1/0288H03F3/195H03F3/213G06N3/08G06N3/045
Inventor 李明玉蔡振东靳一代志江徐常志
Owner CHONGQING UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com