大数据网络中通信传输干扰信息识别仿真研究

摘要

对大数据网络中的通信传输干扰信息的识别,能够有效提高网络通信传输性能.对通信传输过程干扰信息进行识别,需要先压缩大数据网络中通信传输信息初始数据集合,选取干扰信号瞬时特征参数,完成对干扰信息的识别.传统方法结合双谱分析与奇异值分解有机结合的方法,从而表征大数据网络干扰信号特征,但忽略了对干扰信号瞬时特征参数的获取,导致识别精度偏低.提出基于决策分类器的识别方法,采用大数据网络中不同干扰信号的特征信息来提取的参数数量少,且分类准确率高的特征向量,从而通信传输干扰信息初始数据集合,克服了当前方法直接进行通信传输干扰信息识别运算复杂度高的缺点,选取干扰信号瞬时特征参数组成的决策分类器对通信传输干扰信息进行分类识别.实验结果表明,所提方法能将大数据网络中人为加入的低频干扰、中频干扰和高频干扰信号准确识别出来.%The aim of this article is to overcome the defect of traditional recognition method of interference information of communication transmission in big data network,such as poor recognition precision.Based on decision classifier,a new recognition method is presented.Feature vector with less parameters and high classification precision is extracted by using feature information of different interference signals in big data network.Instant feature parameter of interference information is selected to compose decision classifier.Thus,the classification recognition of interference information is completed.Simulation results show that the proposed method can accurately recognize signals with low-frequency interference,intermediate-frequency interference and high-frequency interference in big date network.