Computationally Efficient Modulation Level Classification Based on Probability Distribution Distance Functions

Modulation level classification (MLC) is a process which detects the transmitter's digital modulation level from a received signal, using a priori knowledge of the modulation class and signal characteristics needed for downconversion and sampling. Among many modulation classification methods [1], a cumulant (Cm) based classification [2] is one of the most widespread for its ability to identify both the modulation class and level. However, differentiating among cumulants of the same modulation class, but with different levels, i.e. 16QAM vs. 64QAM, requires a large number of samples. A recently proposed method [3] based on a goodness-of-fit (GoF) test using Kolmogorov-Smirnov (KS) statistic has been suggested as an alternative to the Cm-based level classification which require lower number of samples to achieve accurate MLC. In this letter, we propose a novel MLC method based on distribution distance functions, namely Kuiper (K) [4] [5, Sec.