Fast and robust modulation classification via Kolmogorov-Smirnov test

Authors:
Fanggang Wang;Xiaodong Wang
Affiliations:
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China;Dept. of Electrical Engineering, Columbia Univ., New York, NY
Venue:
IEEE Transactions on Communications
Year:
2010

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Abstract

A new approach to modulation classification based on the Kolmogorov-Smirnov (K-S) test is proposed. The KS test is a non-parametric method to measure the goodness of fit. The basic procedure involves computing the empirical cumulative distribution function (ECDF) of some decision statistic derived from the received signal, and comparing it with the CDFs or the ECDFs of the signal under each candidate modulation format. The K-S-based modulation classifiers are developed for various channels, including the AWGN channel, the flat-fading channel, the OFDM channel, and the channel with unknown phase and frequency offsets, as well as the non-Gaussian noise channel, for both QAM and PSK modulations. Extensive simulation results demonstrate that compared with the traditional cumulant-based classifiers, the proposed K-S classifiers offer superior classification performance, require less number of signal samples (thus is fast), and is more robust to various channel impairments.