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.
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.
R
Kolmogorov- Smirnov (KS)
Kolmogorov-Smirnov
D
(GoF)
A
,
A
,