We present a novel modulation level classification (MLC) method based on
probability distribution distance functions. The proposed method uses modified
Kuiper and Kolmogorov- Smirnov (KS) distances to achieve low computational
complexity and outperforms the state of the art methods based on cumulants and
goodness-of-fit (GoF) 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.