Modulation Recognition of MFSK Signals Based on Multifractal Spectrum
Wireless Personal Communications: An International Journal
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This paper is focusing on the neural network based classifier design of modulation types for communication signals. A tree-structured neural network is proposed which could make correct identification among 13 modulation types by the use of comprehensive features, including power spectral features, cyclic spectral features and high-order cumulant features. The tree-structured neural network is a self-organizing, hierarchical classifier implementing a sequential linear strategy and requiring no statistical analysis of the features. The design procedure is discussed and simulation results are presented. Experiments show that these types of modulation can be recognized under low SNR in AWGN, and this method also works well for frequency modulations and some amplitude-phase modulation in multipath environment.