LTE network uplink interference category identification method and system based on classifier

A recognition method and classifier technology, applied in the transmission system, neural learning method, character and pattern recognition, etc., can solve the problems of unsatisfactory classification accuracy of the model, and the algorithm is easy to fall into the local optimal value, so as to achieve the effect of improving the accuracy

Pending Publication Date: 2021-08-27
SHANDONG JIANZHU UNIV
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

On the one hand, the traditional manual feature extraction process is affected by many factors, requiring rich engineering experience and professional knowledge
On the other hand, the parameters of the deep neural network are initialized randomly, and the optimization based on gradient descent makes the algorithm easy to fall into the local optimal value, resulting in unsatisfactory classification accuracy of the model.

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 network uplink interference category identification method and system based on classifier
  • LTE network uplink interference category identification method and system based on classifier
  • LTE network uplink interference category identification method and system based on classifier

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0034] This embodiment provides a classifier-based LTE network uplink interference category identification method;

[0035] A classifier-based identification method for LTE network uplink interference category, including:

[0036] S101: Obtain LTE network uplink data to be classified;

[0037] S102: Perform feature extraction on the LTE network uplink data to be classified;

[0038] S103: Classify the extracted features using different classifiers to obtain different classification results;

[0039] S104: Perform weighted summation on different classification results to obtain a final classification result.

[0040] Further, said S101: Acquire LTE network uplink data to be classified; specifically include:

[0041] Obtain data indicators such as the uplink interference power value at the granularity of the PRB (Physical Resource Block, Physical Resource Block) to be classified, the label of the primary serving cell, and / or the reporting time.

[0042] Further, said S102: p...

Embodiment 2

[0131] This embodiment provides a classifier-based LTE network uplink interference category identification system;

[0132] Classifier-based LTE network uplink interference category identification system, including:

[0133] An acquisition module configured to: acquire LTE network uplink data to be classified;

[0134] A feature extraction module, which is configured to: perform feature extraction on the LTE network uplink data to be classified;

[0135] A classification module configured to: classify the extracted features using different classifiers to obtain different classification results;

[0136] The weighted summation module is configured to: perform weighted summation on different classification results to obtain a final classification result.

[0137] It should be noted here that the above acquisition module, feature extraction module, classification module and weighted summation module correspond to steps S101 to S104 in Embodiment 1, and the examples and applicat...

Embodiment 3

[0141] 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.

[0142] 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 (Long Term Evolution) network uplink interference category identification method and system based on a classifier. The method comprises the following steps: acquiring LTE network uplink data to be classified; performing feature extraction on the LTE network uplink data to be classified; classifying the extracted features by using different classifiers to obtain different classification results; and performing weighted summation on different classification results to obtain a final classification result. According to the invention, accurate classification of uplink interference is realized in a weighted summation mode.

Description

technical field [0001] The invention relates to the technical field of LTE network uplink interference category identification, in particular to a classifier-based LTE network uplink interference category identification method and system. 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 current mobile communication network is developing rapidly, 2G / 3G / 4G and other network standards coexist, and 5G is also commercialized on a large scale, making the wireless environment increasingly complex. At the same time, the reliability requirements of social production and life for wireless networks are also gradually increasing, which puts forward higher requirements for network operation and maintenance and optimization of communication operators, and interference optimization is one of the important contents of network optimization, which...

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
IPC IPC(8): H04W24/08H04B17/345G06N3/08G06N3/04G06K9/62
CPCH04W24/08H04B17/345G06N3/088G06N3/048G06N3/045G06F18/2411G06F18/24323G06F18/254Y02D30/70
Inventor 许鸿奎李鑫张子枫卢江坤周俊杰姜彤彤
Owner SHANDONG JIANZHU UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap