TY - GEN
T1 - Cooperative combining of cumulants-based modulation classification in CR networks
AU - Abdelbar, Mahi
AU - Tranter, Bill
AU - Bose, Tamal
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/11/13
Y1 - 2014/11/13
N2 - 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.
AB - 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.
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U2 - 10.1109/MILCOM.2014.78
DO - 10.1109/MILCOM.2014.78
M3 - Conference contribution
AN - SCOPUS:84912573115
T3 - Proceedings - IEEE Military Communications Conference MILCOM
SP - 434
EP - 439
BT - Proceedings - 2014 IEEE Military Communications Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 33rd Annual IEEE Military Communications Conference, MILCOM 2014
Y2 - 6 October 2014 through 8 October 2014
ER -