LTE uplink interference classification method and system based on stack noise reduction self-coding

A classification method and self-encoding technology, which is applied in the field of LTE uplink interference classification, can solve the problems of falling into local optimal values, unsatisfactory model classification accuracy, and inaccurate interference classification accuracy, etc.

Active Publication Date: 2021-09-07
SHANDONG JIANZHU UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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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 de...

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  • LTE uplink interference classification method and system based on stack noise reduction self-coding
  • LTE uplink interference classification method and system based on stack noise reduction self-coding
  • LTE uplink interference classification method and system based on stack noise reduction self-coding

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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] A classification module 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 noted ...

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...

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Abstract

The invention discloses an LTE (Long Term Evolution) uplink interference classification method and system based on stack noise reduction self-coding. The method comprises the following steps: acquiring LTE base station uplink data to be classified; preprocessing the uplink data of the LTE base station to be classified; and inputting the preprocessed to-be-classified uplink data of the LTE base station into the trained extreme learning machine based on stack noise reduction self-coding to obtain an interference category of the uplink data of the LTE base station, wherein the extreme learning machine based on stack noise reduction self-coding comprises an input layer, a hidden layer h1, a hidden layer h2, a hidden layer h3, a classification layer and an output layer which are connected in sequence. According to the model, the uplink interference analysis efficiency of the LTE network is improved, and meanwhile, the model has relatively high 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

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Application Information

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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
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