Application of Wavelet Denoising and Time- Frequency Domain Feature Extraction on Data Processing of Modulated Signals

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2021-10-01 DOI:10.1109/AINIT54228.2021.00123

Yujun Dai, Xizi Huang, Zhongrun Chen

{"title":"Application of Wavelet Denoising and Time- Frequency Domain Feature Extraction on Data Processing of Modulated Signals","authors":"Yujun Dai, Xizi Huang, Zhongrun Chen","doi":"10.1109/AINIT54228.2021.00123","DOIUrl":null,"url":null,"abstract":"Signal modulation is an essential part of the communication system. Researches on reducing the noise interference in the modulated signal and improving the signal-to- noise ratio can help recognize the signal. In this paper, it is proposed to apply the wavelet denoising and time-frequency domain feature extraction to the modulated signals. Combine the characteristics of the modulated signal and the theory of wavelet denoising and time-frequency domain feature extraction, remove the noise interference in the signal, extract the time-frequency domain feature, and input the processed data into the decision tree model for classification and recognition and evaluate the signal processing effect. In addition, the accuracy of decision trees before and after data processing under different signal-to- noise ratios is further studied. Experimental results show that wavelet denoising and time-frequency domain feature extraction obviously affect modulated signal processing and are generally applicable under different signal-to-noise ratios.","PeriodicalId":326400,"journal":{"name":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","volume":"813 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AINIT54228.2021.00123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

Abstract

Signal modulation is an essential part of the communication system. Researches on reducing the noise interference in the modulated signal and improving the signal-to- noise ratio can help recognize the signal. In this paper, it is proposed to apply the wavelet denoising and time-frequency domain feature extraction to the modulated signals. Combine the characteristics of the modulated signal and the theory of wavelet denoising and time-frequency domain feature extraction, remove the noise interference in the signal, extract the time-frequency domain feature, and input the processed data into the decision tree model for classification and recognition and evaluate the signal processing effect. In addition, the accuracy of decision trees before and after data processing under different signal-to- noise ratios is further studied. Experimental results show that wavelet denoising and time-frequency domain feature extraction obviously affect modulated signal processing and are generally applicable under different signal-to-noise ratios.

查看原文本刊更多论文

小波去噪与时频域特征提取在调制信号数据处理中的应用

信号调制是通信系统的重要组成部分。研究降低调制信号中的噪声干扰，提高信噪比，有助于信号的识别。本文提出了对调制信号进行小波去噪和时频域特征提取的方法。将调制信号的特性与小波去噪和时频域特征提取理论相结合，去除信号中的噪声干扰，提取时频域特征，将处理后的数据输入决策树模型进行分类识别，并对信号处理效果进行评价。此外，进一步研究了不同信噪比下数据处理前后决策树的准确率。实验结果表明，小波去噪和时频域特征提取对调制信号处理有明显影响，在不同信噪比下普遍适用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)

自引率

0.00%

发文量