Robust modulation classification techniques using cumulants and hierarchical neural networks

John A. DeClouet; Mort Naraghi-Pour

doi:10.1117/12.719349

7 May 2007 Robust modulation classification techniques using cumulants and hierarchical neural networks

John A. DeClouet, Mort Naraghi-Pour

Proceedings Volume 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI; 65671J (2007) https://doi.org/10.1117/12.719349
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

The problem of automatic modulation classification is to identify the modulation type of a received signal from the signal parameters. Modulation classification has both military and civilian applications and has been the subject of intensive research for more than two decades. In this paper we use a hierarchical neural network in which the first network identifies the modulation class while a second set of networks identify the constellation size (order) of that modulation class. The set of features we use include normalized standard deviations of amplitude, phase and frequency, as well as the fourth and sixth order cumulants of the signal samples. Identifying the constellation size of quadrature amplitude modulation (QAM) has been particularly difficult in the past. In this paper we introduce two new approaches for computing the features of a QAM signal. The first uses the concatenated in- phase and quadrature components of the signal to compute the features. The second method maps the in-phase and quadrature components to the first quadrant of the constellation by calculating the absolute value of each separately. The mean of the resulting constellation points is then subtracted before calculating the features. Simulation results are presented for classification of several digital modulation schemes including FSK, PSK, ASK and QAM. Our results show that the proposed method significantly improves the classification error.

Citation Download Citation

John A. DeClouet and Mort Naraghi-Pour "Robust modulation classification techniques using cumulants and hierarchical neural networks", Proc. SPIE 6567, Signal Processing, Sensor Fusion, and Target Recognition XVI, 65671J (7 May 2007); https://doi.org/10.1117/12.719349

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