Feature based modulation classification for overlapped signals

Yizhou Jiang, Sai Huang, Yixin Zhang, Zhiyong Feng, Di Zhang, Celimuge Wu

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


This letter proposes a novel modulation classification method for overlapped sources named LRGP involving multinomial logistic regression (MLR) and multi-gene genetic programming (MGGP). MGGP based feature engineering is conducted to transform the cumulants of the received signals into highly discriminative features and a MLR based classifier is trained to identify the combination of the modulation formats of the overlapped sources instead of signal separation. Extensive simulations demonstrate that LRGP yields superior performance compared with existing methods.

Original languageEnglish
Pages (from-to)1123-1126
Number of pages4
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Issue number7
Publication statusPublished - 2018 Jul
Externally publishedYes


  • Cumulant
  • Modulation classification
  • Multi-gene genetic programming
  • Multinomial logistic regression
  • Overlapped sources

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics


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