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A new scheme of automatic modulation classification using wavelet and WSVM

  • Dan Wu*
  • , Xuemai Gu
  • , Qing Guo
  • *Corresponding author for this work
  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper deals with automatic modulation classification of communication signals. A new scheme of automatic modulation classification using wavelet analysis and wavelet support vector machine (WSVM) is proposed. Further, a new way of training for wavelet features is carried out to adapt to signals which are non-stable and varied in a wide range of signal-to-noise rates (SNR). Through such training, a single classifier can classify modulation types with high accuracy without knowing signals' SNR if only the SNR is in a certain range. Computer simulation shows that the classifier can separate ten modulation types, i.e. 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, 16QAM, TFM, π/4QPSK, OQPSK and success rates are over 96.5% when SNR is not lower than 3 dB. Accuracy and efficiency of the proposed scheme are obviously improved.

Original languageEnglish
Title of host publicationIEE Mobility Conference 2005 - Second International Conference on Mobile Technology, Applications and Systems
Pages132
Number of pages1
Edition496 CP
DOIs
StatePublished - 2005
EventIEE Mobility Conference 2005 - The Second International Conference on Mobile Technology, Applications and Systems - Guangzhou, China
Duration: 15 Nov 200517 Nov 2005

Publication series

NameIET Conference Publications
Number496 CP

Conference

ConferenceIEE Mobility Conference 2005 - The Second International Conference on Mobile Technology, Applications and Systems
Country/TerritoryChina
CityGuangzhou
Period15/11/0517/11/05

Keywords

  • Kernel function
  • Modulation classification
  • WASVM
  • Wavelet

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