Likelihood-ratio approaches to automatic modulation classification

Jefferson L. Xu, Wei Su, Mengchu Zhou

Research output: Contribution to journalReview articlepeer-review

135 Scopus citations

Abstract

Adaptive modulation and automatic modulation classification are highly demanded in software-defined radio (SDR) for both commercial and military applications. Various design options of automatic classifiers have attracted researchers in developing 3G and 4G wireless communication systems. There is an urgent need to investigate the different methods of coherent and noncoherent modulation estimations, discuss the challenges in cooperative and noncooperative communication environment, and understand the distinct requirements in real-time modulation classifications. This survey paper focuses on the automatic modulation classification methods based on likelihood functions, studies various classification solutions derived from likelihood ratio test, and discusses the detailed characteristics associated with all major algorithms.

Original languageEnglish (US)
Article number5606206
Pages (from-to)455-469
Number of pages15
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume41
Issue number4
DOIs
StatePublished - Jul 1 2011

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Cognitive radio
  • likelihood ratio test (LRT)
  • maximum likelihood (ML)
  • modulation classification
  • modulation recognition
  • software-defined radio (SDR)
  • wireless communication systems

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