Feature based modulation classification for overlapped signals

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

研究成果: Article査読

4 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)1123-1126
ページ数4
ジャーナルIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
E101A
7
DOI
出版ステータスPublished - 2018 7月
外部発表はい

ASJC Scopus subject areas

  • 信号処理
  • コンピュータ グラフィックスおよびコンピュータ支援設計
  • 電子工学および電気工学
  • 応用数学

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