Simulation and Comparison on the Performance of Different Modulation Level Classification Methods

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 down conversion and sampling. In this paper, we compared three different modulation classification methods: a cumulant (Cm) based classification, a goodness-of-fit (GoF) test using Kolmogorov-Smirnov (KS) statistic and a modified method using KS statistic (rcKS) to achieve low computational complexity. Simulation results show that rcKS method has the best performance.