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An Intelligent Digital Signal Type Recognizer

Ataollah Ebrahimzadeh, Abolfazl Ranjbar

Abstract


Digital signal type recognition is an important issue in communication intelligence system. Most of the proposed recognizers (techniques) can only identify a few kinds of digital signal and/or low order of digital signals. They usually require high levels of signal to noise ratio (SNR). This paper presents a new intelligent recognizer that includes a variety of digital signals. In this recognizer, higher order moments and higher order cumulants up to eighth are used as the features. A multilayer perceptron neural network with resilient back propagation learning algorithm is utilized to determine the membership of the received signal. The numbers of nodes in the hidden layer, along with the selection of input features are optimized using a genetic algorithm. Simulation results show that the proposed recognizer has high
performance for recognition the different kinds of digital signal even at very low SNRs. This high performance is achieved with only seven selected features and the least possible number of nodes in the hidden layer, which have been optimized using genetic algorithm.

Keywords


Statistical pattern recognition, signal type recognition, neural network, learning algorithms, optimization, higher order statistics.

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