TY - GEN
T1 - Modulation classification of MIMO-OFDM signals by independent component analysis and support vector machines
AU - Agirman-Tosun, H.
AU - Liu, Yu
AU - Haimovich, A. M.
AU - Simeone, Osvaldo
AU - Su, Wei
AU - Dabin, Jason
AU - Kanterakis, Emmanuel
N1 - Copyright:
Copyright 2012 Elsevier B.V., All rights reserved.
PY - 2011
Y1 - 2011
N2 - A modulation classification scheme based on Independent Component Analysis (ICA) in conjunction with proposed for MIMO-OFDM signals over frequency selective, time varying channels. The method is blind in the sense that it is assumed that the receiver has no information about the channel and transmitted signals other than that the spatial streams of signals are statistically independent. The processing consists of separation of the MIMO streams followed by modulation classification of the separated signals. While in general, blind separation of signals over frequency selective channels is a difficult problem, the non-frequency selective nature of the channel experienced by individual symbols in a MIMO-OFDM system enables the application of well-known ICA algorithms. Modulation classification is implemented by maximum likelihood and by an SVM-based modulation classification method relying on pre-selected modulation-dependent features. To improve performance in time varying channels, the invariance of the is exploited across the coherence bandwidth and the time coherence. The proposed method is shown to perform with high probability of correct classification over realistic ITU pedestrian and vehicular channels.
AB - A modulation classification scheme based on Independent Component Analysis (ICA) in conjunction with proposed for MIMO-OFDM signals over frequency selective, time varying channels. The method is blind in the sense that it is assumed that the receiver has no information about the channel and transmitted signals other than that the spatial streams of signals are statistically independent. The processing consists of separation of the MIMO streams followed by modulation classification of the separated signals. While in general, blind separation of signals over frequency selective channels is a difficult problem, the non-frequency selective nature of the channel experienced by individual symbols in a MIMO-OFDM system enables the application of well-known ICA algorithms. Modulation classification is implemented by maximum likelihood and by an SVM-based modulation classification method relying on pre-selected modulation-dependent features. To improve performance in time varying channels, the invariance of the is exploited across the coherence bandwidth and the time coherence. The proposed method is shown to perform with high probability of correct classification over realistic ITU pedestrian and vehicular channels.
UR - http://www.scopus.com/inward/record.url?scp=84861325040&partnerID=8YFLogxK
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U2 - 10.1109/ACSSC.2011.6190354
DO - 10.1109/ACSSC.2011.6190354
M3 - Conference contribution
AN - SCOPUS:84861325040
SN - 9781467303231
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1903
EP - 1907
BT - Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
T2 - 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Y2 - 6 November 2011 through 9 November 2011
ER -