Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning

Ben Dgani, Israel Cohen

Research output: Contribution to journalArticlepeer-review


This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have received limited attention in previous works. The simulation results show that the proposed method performs well with different signal-to-noise ratios (SNRs) and channel conditions. The classifier’s performance is superior to that of complex deep learning methods, making it suitable for deployment in CR networks’ end units, especially in military and emergency service applications. The proposed method offers a cost-effective and high-quality solution for AMC that meets the strict demands of these critical applications.

Original languageEnglish
Article number701
Issue number2
StatePublished - Jan 2024


  • cumulants
  • machine learning
  • modulation classification

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Information Systems
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Biochemistry


Dive into the research topics of 'Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning'. Together they form a unique fingerprint.

Cite this