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

LTE Uplink Interference Classification Method and System Based on Stacked Noise Reduction Autoencoder

A classification method and self-encoding technology, applied in the field of LTE uplink interference classification, can solve the problems of inaccurate interference classification accuracy, unsatisfactory model classification accuracy, trapped in local optimal values, etc., so as to overcome the lack of robustness and improve noise data. Sensitive, high-accuracy effects

Active Publication Date: 2022-05-17
SHANDONG JIANZHU UNIV
View PDF3 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

On the one hand, the traditional machine learning method needs to be affected by many factors in the process of feature extraction, which requires rich engineering experience and professional knowledge
On the other hand, the backpropagation algorithm based on gradient descent is easy to fall into a local optimum, which makes the classification accuracy of the model unsatisfactory.
[0005] The inventor found that the technical problem existing in the prior art is that the classification accuracy of LTE uplink interference is not accurate enough

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
  • LTE Uplink Interference Classification Method and System Based on Stacked Noise Reduction Autoencoder
  • LTE Uplink Interference Classification Method and System Based on Stacked Noise Reduction Autoencoder
  • LTE Uplink Interference Classification Method and System Based on Stacked Noise Reduction Autoencoder

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0045] This embodiment provides an LTE uplink interference classification method based on stack noise reduction self-encoding;

[0046] LTE uplink interference classification method based on stack noise reduction self-encoding, including:

[0047] S101: Obtain LTE base station uplink data to be classified;

[0048] S102: Preprocessing the LTE base station uplink data to be classified;

[0049] S103: Input the preprocessed uplink data of the LTE base station to be classified into the trained extreme learning machine based on stack noise reduction autoencoding to obtain the interference category of the uplink data of the LTE base station;

[0050] Among them, the extreme learning machine based on stack denoising self-encoding includes sequentially connected: input layer, hidden layer h1, hidden layer h2, hidden layer h3, classification layer and output layer.

[0051] Further, the LTE base station uplink data to be classified includes:

[0052] Base station identification num...

Embodiment 2

[0153] This embodiment provides an LTE uplink interference classification system based on stack noise reduction self-encoding;

[0154] LTE uplink interference classification system based on stack noise reduction self-encoding, including:

[0155] An acquisition module configured to: acquire LTE base station uplink data to be classified;

[0156] A preprocessing module, which is configured to: preprocess the LTE base station uplink data to be classified;

[0157] The classification module is configured to: input the preprocessed LTE base station uplink data to be classified into the trained extreme learning machine based on stack noise reduction self-encoding to obtain the interference category of the LTE base station uplink data;

[0158] Among them, the extreme learning machine based on stack denoising self-encoding includes sequentially connected: input layer, hidden layer h1, hidden layer h2, hidden layer h3, classification layer and output layer.

[0159] It should be n...

Embodiment 3

[0163] This embodiment also provides an electronic device, including: one or more processors, one or more memories, and one or more computer programs; wherein, the processor is connected to the memory, and the one or more computer programs are programmed Stored in the memory, when the electronic device is running, the processor executes one or more computer programs stored in the memory, so that the electronic device executes the method described in Embodiment 1 above.

[0164] It should be understood that in this embodiment, the processor can be a central processing unit CPU, and the processor can also be other general-purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate array FPGA or other programmable logic devices , discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, o...

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 an LTE uplink interference classification method and system based on stack noise reduction self-encoding, comprising: obtaining LTE base station uplink data to be classified; preprocessing the LTE base station uplink data to be classified; Base station uplink data is input into the trained extreme learning machine based on stack noise reduction self-encoding to obtain the interference category of LTE base station uplink data; among them, the extreme learning machine based on stack noise reduction self-encoding includes sequentially connected: input layer , hidden layer h1, hidden layer h2, hidden layer h3, classification layer and output layer. This model improves the efficiency of LTE network uplink interference analysis and has strong robustness.

Description

technical field [0001] The present invention relates to the technical field of LTE uplink interference classification, in particular to an LTE uplink interference classification method and system based on stack noise reduction self-encoding. Background technique [0002] The statements in this section merely mention the background technology related to the present invention and do not necessarily constitute the prior art. [0003] The scale of the current mobile communication network is constantly expanding, and the number of base stations is also increasing. 2G / 3G / 4G networks coexist, and 5G networks are also commercially available on a large scale. Problems such as improper frequency allocation or insufficient equipment separation between systems are becoming more and more prominent, making the LTE system uplink Interference is getting worse. However, the current interference investigation work mainly adopts the method of manual investigation for identification, and by co...

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 Patents(China)
IPC IPC(8): H04B17/345G06K9/62G06N3/04G06N3/08
CPCH04B17/345G06N3/08G06N3/048G06N3/045G06F18/2415Y02D30/70
Inventor 许鸿奎李鑫周俊杰张子枫卢江坤姜彤彤
Owner SHANDONG JIANZHU 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