Spectral Correlation Based Blind Automatic Modulation Classification Using Symbol Rate Estimation

Minseok KIM
Md. Abdur RAHMAN
Jun-ichi TAKADA

IEICE TRANSACTIONS on Communications   Vol.E96-B    No.5    pp.1158-1167
Publication Date: 2013/05/01
Online ISSN: 1745-1345
DOI: 10.1587/transcom.E96.B.1158
Print ISSN: 0916-8516
Type of Manuscript: PAPER
Category: Wireless Communication Technologies
modulation,  classification,  symbol rate,  estimation,  cyclostationarity,  spectral correlation,  

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Automatic modulation classification (AMC) is an important function of radio surveillance systems in order to identify unknown signals. Many previous works on AMC have utilized signal cyclostationarity, particularly spectral correlation density (SCD), but many of them fail to address several implementation issues, such as the assumption of perfect knowledge of the symbol rate. In this paper, we discuss several practical issues, e.g. cyclic frequency mismatch, which may affect the SCD, and propose compensation techniques to overcome those issues. We also propose a novel feature extraction technique from the SCD, which utilizes the SCD of not only the original received signal, but also the squared received signal. A symbol rate estimation technique which complements the feature extraction is also proposed. Finally, the classification performance of the system is evaluated through Monte Carlo simulations using a wide variety of modulated signals, and simulation results show that the proposed technique can estimate the symbol rate and classify modulation with a probability of above 0.9 down to SNRs of 5 dB.