计算机仿真2024,Vol.41Issue(7) :303-307.

复杂电磁环境下LTE网络监测与分类研究

Research on LTE Classification and Network Monitoring in Complex Environment

陈金粮 张彬 杨晶晶 黄铭
计算机仿真2024,Vol.41Issue(7) :303-307.

复杂电磁环境下LTE网络监测与分类研究

Research on LTE Classification and Network Monitoring in Complex Environment

陈金粮 1张彬 2杨晶晶 1黄铭1
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作者信息

  • 1. 云南大学信息学院,云南 昆明 650091
  • 2. 云南省无线电监测中心,云南 昆明 650228
  • 折叠

摘要

由于免许可证频段电磁环境复杂,因此LTE(Long Term Evolution)业务在该频段部署实现多业务共存的关键是对其进行监测和分类.为此,采用NS-3 网络仿真模拟器建立了LTE和Wi-Fi室内业务共存模型,通过改变网络和场景的各类参数构建了监测模型,获得了文件传输FT(file transfers)、音频流VF(voice flows)和恒定比特流CBRS(constant bit rate streams)三种模式下的数据集.结果表明,采用FT数据集,在K最近邻、支持向量机、决策树和随机森林算法的分类准确率有明显提高;同时,VF与CBRS两种数据集在上述算法上准确率达到了 80%左右.

Abstract

Due to the complex electromagnetic environment in the license-free band,monitoring and classification is the key issue for ensuring the deployment of LTE(Long Term Evolution)and its coexistence with Wi-Fi.To ad-dress this issue,a coexistence model for LTE and Wi-Fi indoor services is built using the NS-3 network simulator,and a monitoring model is constructed by varying various parameters of the network and scenarios to obtain the data-sets of FT(file transfers),VF(voice flows)and CBRS(constant bit rate streams)in three modes.Results show that the classification accuracy of K-nearest neighbors,support vector machines,decision trees and random forest algo-rithms has been significantly improved when using the FT dataset;meanwhile,an accuracy of about 80%can be a-chieved when using VF and CBRS datasets.

关键词

共享频谱/机器学习/网络仿真

Key words

Shared spectrum/Machine learning/Network simulation

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基金项目

国家自然科学基金资助项目(61963037)

国家自然科学基金资助项目(61863035)

国家自然科学基金资助项目(62261059)

出版年

2024
计算机仿真
中国航天科工集团公司第十七研究所

计算机仿真

CSTPCD
影响因子:0.518
ISSN:1006-9348
参考文献量1
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