Distributed automatic modulation classification with multiple sensors

Jefferson L. Xu, Wei Su, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

56 Scopus citations


Automatic modulation classification (AMC) has been intensively studied to enhance the successful classification rate, particularly for overcoming the physical limit that deals with weak signals received in a noncooperative communication environment. A wireless sensor network (WSN) has multiple geometrically distributed sensors to work cooperatively. The distributed signal sensing and classification performed by collaborated sensors is proven to be beneficial to increasing the modulation classification reliability. In this paper, we apply the likelihood ratio-based distributed detection fusion technique to address the issues of general binary modulation classifications. The data fusion algorithm performed in the primary node is presented. Its numerical performance with simulation results is demonstrated.

Original languageEnglish (US)
Article number5482046
Pages (from-to)1779-1785
Number of pages7
JournalIEEE Sensors Journal
Issue number11
StatePublished - 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Instrumentation
  • Electrical and Electronic Engineering


  • Cognitive radio
  • distributed classification
  • distributed detection
  • likelihood ratio test (LRT)
  • modulation classification
  • wireless sensor networks (WSN)


Dive into the research topics of 'Distributed automatic modulation classification with multiple sensors'. Together they form a unique fingerprint.

Cite this