Cooperative combining of cumulants-based modulation classification in CR networks

Mahi Abdelbar, Bill Tranter, Tamal Bose

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

1 Scopus citations

Abstract

Automatic Modulation Classification (AMC) is a key enabling technology in Cognitive Radio (CR) Networks. The ability of CR transceivers to detect and classify unknown wireless signals has various applications in civilian and military domains. Performance of AMC degrades severely under low Signal-to-Noise Ratio (SNR) and variable channel conditions. Cooperative classification has been presented as a means to overcome the detrimental channel effects by combining the results from physically scattered CR nodes. In this work, Maximum Likelihood (ML) combining of classification features is presented as a data fusion algorithm that provides better classification accuracy compared to hard decision combining algorithms without high network overhead. The performance of a cumulants-based modulation classifier under Additive White Gaussian Noise (AWGN) is analyzed. The enhancement in classification performance when applying ML combining of more than one classifier is presented. Theoretical analysis as well as various simulations are presented for ML combining of CR nodes with equal SNR. In addition, analysis is extended to the case where CR nodes have different SNRs. Theory and simulations show that applying ML combining will result in a better classification accuracy, even when one of the nodes has a much lower SNR.

Original languageEnglish (US)
Title of host publicationProceedings - 2014 IEEE Military Communications Conference
Subtitle of host publicationAffordable Mission Success: Meeting the Challenge, MILCOM 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages434-439
Number of pages6
ISBN (Electronic)9781479967704
DOIs
StatePublished - Nov 13 2014
Event33rd Annual IEEE Military Communications Conference, MILCOM 2014 - Baltimore, United States
Duration: Oct 6 2014Oct 8 2014

Publication series

NameProceedings - IEEE Military Communications Conference MILCOM

Other

Other33rd Annual IEEE Military Communications Conference, MILCOM 2014
CountryUnited States
CityBaltimore
Period10/6/1410/8/14

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Cooperative combining of cumulants-based modulation classification in CR networks'. Together they form a unique fingerprint.

  • Cite this

    Abdelbar, M., Tranter, B., & Bose, T. (2014). Cooperative combining of cumulants-based modulation classification in CR networks. In Proceedings - 2014 IEEE Military Communications Conference: Affordable Mission Success: Meeting the Challenge, MILCOM 2014 (pp. 434-439). [6956799] (Proceedings - IEEE Military Communications Conference MILCOM). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MILCOM.2014.78