• HOME
  • About Journal
    • Historical evolution
    • Journal Honors
  • Editorial Board
    • Members of Committee
    • Director of the Committee
    • President and Editor in chief
  • Submission Guide
    • Instructions for Authors
    • Manuscript Processing Flow
    • Model Text
    • Procedures for Submission
  • Academic Influence
  • Open Access
  • Ethics&Policies
    • Publication Ethics Statement
    • Peer Review Process
    • Academic Misconduct Identification and Treatment
    • Advertising and Marketing
    • Correction and Retraction
    • Conflict of Interest
    • Authorship & Copyright
  • Contact Us
  • Chinese
Site search        
文章摘要
基于神经网络的低压电力线载波通信信号调制识别研究
Modulation Classification of Low Voltage Power Line Communication Based on BP neural network
Received:September 10, 2014  Revised:September 10, 2014
DOI:
中文关键词: 电力线载波通信  调制识别  特征参数  BP神经网络
英文关键词: power line communication  modulation recognition  decision criteria  BP neural network
基金项目:中国博士后科学基金
Author NameAffiliationE-mail
wang yi* Chong Qing Electric Power Research Institute wangyi81@cqupt.edu.cn 
duandacheng Chong Qing Electric Power Research Institute  
Cheng Hao State Grid ChongQing Electric Power Company  
Sun Hongliang Chong Qing Electric Power Research Institute  
Zheng Ke Chong Qing Electric Power Research Institute  
Hu xiaorui Chong Qing Electric Power Research Institute  
liu huo rang BaShu Secondary school  
Hits: 1552
Download times: 618
中文摘要:
      研究低压电力线载波通信信号调制识别技术对建立相关通信领域的规范化测试标准具有重要意义。本文通过采集电力线载波芯片发送的调制信号样值,经预处理后提取信号四类特征参数,并利用BP神经网络结合双特征参数阈值判决法对特征参数进行判决归类,从而实现调制类型的自动识别。仿真及实测数据结果表明,本文提出的特征参数集和基于双特征参数阈值判决的神经网络分类器能够有效识别低压电力线载波BFSK、BPSK和QPSK调制信号,在信噪比大于10dB的情况下,该方法的识别正确率可达95%以上。
英文摘要:
      Research on the low voltage power line communication (PLC) modulation recognition technology is of great importance for establishing PLC physical layer standard test method. In this paper, a novel modulation recognition scheme with double feature parameters-threshold judgment method for narrowband PLC is proposed. The PLC modulated signal is sampled and pre-processed. With the four decision criteria derived from these samples, the BP neural network is applied to recognize and classify different modulated signals automatically. From the experiment results it can be seen that the proposed method based on double feature parameters-threshold judgment can effec-tively recognize PLC narrowband signals. When the SNR is above 8dB, the successful recognition rate is above 95%.
View Full Text   View/Add Comment  Download reader
Close
  • Home
  • About Journal
    • Historical evolution
    • Journal Honors
  • Editorial Board
    • Members of Committee
    • Director of the Committee
    • President and Editor in chief
  • Submission Guide
    • Instructions for Authors
    • Manuscript Processing Flow
    • Model Text
    • Procedures for Submission
  • Academic Influence
  • Open Access
  • Ethics&Policies
    • Publication Ethics Statement
    • Peer Review Process
    • Academic Misconduct Identification and Treatment
    • Advertising and Marketing
    • Correction and Retraction
    • Conflict of Interest
    • Authorship & Copyright
  • Contact Us
  • 中文页面
Address: No.2000, Chuangxin Road, Songbei District, Harbin, China    Zip code: 150028
E-mail: dcyb@vip.163.com    Telephone: 0451-86611021
© 2012 Electrical Measurement & Instrumentation
黑ICP备11006624号-1
Support:Beijing Qinyun Technology Development Co., Ltd