Low Complexity Feature-Based Modulation Classifier and Its Non-Asymptotic Analysis

作者: E. Rebeiz , D. Cabric

DOI: 10.1109/GLOCOM.2011.6134309

关键词:

摘要: In this paper, we propose a reduced-complexity modulation classifier using multi-cycle features extracted from the Spectral Correlation Function (SCF) in order to distinguish among QAM, BPSK, MSK and AM schemes. We analytically derive SCF statistics of noise signal used for classification finite number samples, use Chebyshev inequality upper bound minimum spectral averages required attain predetermined correct probability. Both theoretical simulation results show that proposed requires on 50 achieve probability 0.9 at SNR = 5 dB. The algorithm corresponding analysis presented paper can be extended classify other

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