Automatic classification of analog modulation schemes

Haifeng Xiao, Yun Q. Shi, Wei Su, John A. Kosinski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper discusses automatic modulation classification (AMC) of analog schemes. Histograms of instantaneous frequency are used as classification features and Support Vector Machines (SVMs) are then applied to classify the unknown modulation schemes. This novel machine-learning based method can insure robustness in a wide range of SNR. Extensive simulation has demonstrated the validity of the proposed AMC algorithm. It is a practical algorithm in blind AMC environments.

Original languageEnglish (US)
Title of host publicationRWW 2012 - Proceedings
Subtitle of host publicationIEEE Radio and Wireless Symposium, RWS 2012
Pages5-8
Number of pages4
DOIs
StatePublished - May 11 2012
Event2012 6th IEEE Radio and Wireless Week, RWW 2012 - 2012 IEEE Radio and Wireless Symposium, RWS 2012 - Santa Clara, CA, United States
Duration: Jan 15 2012Jan 18 2012

Publication series

NameRWW 2012 - Proceedings: IEEE Radio and Wireless Symposium, RWS 2012

Other

Other2012 6th IEEE Radio and Wireless Week, RWW 2012 - 2012 IEEE Radio and Wireless Symposium, RWS 2012
Country/TerritoryUnited States
CitySanta Clara, CA
Period1/15/121/18/12

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Automatic modulation classification
  • Support Vector Machine
  • analog modulation
  • histogram

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