Combined blind equalization and automatic modulation classification for cognitive radios

Barathram Ramkumar, Tamal Bose, Miloje S. Radenkovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Blind equalization and Automatic Modulation Classification (AMC) have been of significant importance for cognitive radios when the receiver has no information about the channel or modulation type. Choosing an appropriate equalizer is difficult in the absence of channel information. In this paper, an AMC based on cyclostationary feature detection and a predictor-based recursive blind equalizer is used in conjunction. The probability of classification of the AMC is used as a metric and fed back to update the blind equalizer order. The equalizer and the AMC enhance the performance of each other. Computer simulations are given to illustrate the concept and yield promising results.

Original languageEnglish (US)
Title of host publication2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings
Pages172-177
Number of pages6
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009 - Marco Island, FL, United States
Duration: Jan 4 2009Jan 7 2009

Publication series

Name2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings

Other

Other2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009
Country/TerritoryUnited States
CityMarco Island, FL
Period1/4/091/7/09

Keywords

  • AMC
  • Blind equalizer
  • Cognitive radios
  • Cyclostationarity

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Combined blind equalization and automatic modulation classification for cognitive radios'. Together they form a unique fingerprint.

Cite this