  In this letter, we derive the optimal discriminant functions for modulation
classification based on the sampled distribution distance. The proposed method
classifies various candidate constellations using a low complexity approach
based on the distribution distance at specific testpoints along the cumulative
distribution function. This method, based on the Bayesian decision criteria,
asymptotically provides the minimum classification error possible given a set
of testpoints. Testpoint locations are also optimized to improve classification
performance. The method provides significant gains over existing approaches
that also use the distribution of the signal features.
