Modulation Classifier of Analogue Modulated Signals Based on Method of Artificial Neural Networks

Communication signals travelling in space with different modulation types and different frequencies fall in a very wide band. Usually, it is required to identify and monitor these signals for many applications. Some of these applications are for civilian sphere purposes such as signal confirmation, interference identification and spectrum management. A new subsystem used in automatic wireless signal modulation recognition based on ANN is described in this paper. The signal recognizer being developed consists of five subsystems: (1) adaptive antenna arrays, (2) pre-processing of EM signals, (3) key features extraction, (4) modulation recognizer and (5) output stage. The choice of maximum value of spectral power density of the normalized-centred amplitude, standard deviation of the absolute value of the centred non-linear component of the instantaneous phase, standard deviation of the absolute value of the normalized-centred instantaneous amplitude, standard deviation of the absolute value of the normalized-centred instantaneous frequency, spectrum symmetry measure are used as key features for the analogue modulation recognizer based on the artificial neural networks (ANNs). The new original structure of the recognizer of analogue signals is described. The modulation recognizer using the two ANNs with two hidden layers. The results are summarized for real signals with noise and fading.