Distributed feature-based modulation classification using wireless sensor networks

Pedro A. Forero, Alfonso Cano, Georgios B Giannakis

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

30 Scopus citations

Abstract

Automatic modulation classification (AMC) is a critical prerequisite for demodulation of communication signals in tactical scenarios. Depending on the number of unknown parameters involved, the complexity of AMC can be prohibitive. Existing maximum-likelihood and feature-based approaches rely on centralized processing. The present paper develops AMC algorithms using spatially distributed sensors, each acquiring relevant features of the received signal. Individual sensors may be unable to extract all relevant features to reach a reliable classification decision. However, the cooperative in-network approach developed enables high classification rates at reduced-overhead, even when features are noisy and/or missing. Simulated tests illustrate the performance of the novel distributed AMC scheme.

Original languageEnglish (US)
Title of host publication2008 IEEE Military Communications Conference, MILCOM 2008 - Assuring Mission Success
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE Military Communications Conference, MILCOM 2008 - Assuring Mission Success - Washington, DC, United States
Duration: Nov 17 2008Nov 19 2008

Other

Other2008 IEEE Military Communications Conference, MILCOM 2008 - Assuring Mission Success
CountryUnited States
CityWashington, DC
Period11/17/0811/19/08

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