  We present a novel modulation level classification (MLC) method based on
probability distribution distance functions. The proposed method uses modified
Kuiper and Kolmogorov-Smirnov distances to achieve low computational complexity
and outperforms the state of the art methods based on cumulants and
goodness-of-fit tests. We derive the theoretical performance of the proposed
MLC method and verify it via simulations. The best classification accuracy,
under AWGN with SNR mismatch and phase jitter, is achieved with the proposed
MLC method using Kuiper distances.
