  Kolmogorov-Smirnov (K-S) test-a non-parametric method to measure the goodness
of fit, is applied for automatic modulation classification (AMC) in this paper.
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 of the signal under each candidate modulation
format. The K-S-based modulation classifier is first developed for AWGN
channel, then it is applied to OFDM-SDMA systems to cancel multiuser
interference. Regarding the complexity issue of K-S modulation classification,
we propose a low-complexity method based on the robustness of the K-S
classifier. Extensive simulation results demonstrate that compared with the
traditional cumulant-based classifiers, the proposed K-S classifier offers
superior classification performance and requires less number of signal samples
(thus is fast).
