Modulation classification using a neural tree network

K. R. Farrell, Richard Mammone

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations


A new classifier is presented for estimating the modulation type for digitally modulated signals. The new classifier is known as the neural tree network (NTN). The NTN is a self-organizing, hierarchical classifier that implements a sequential linear decision strategy. The NTN does not require a statistical analysis of the features, as do Bayesian methods or decision trees. The NTN also allows for a more flexible partitioning of feature space than the prior classification methods. The features used for modulation classification are obtained from an autoregressive model of the signal. These features include the instantaneous frequency, bandwidth, and derivative of the instantaneous frequency. The modulation types to be estimated are continuous wave, binary and quadrature phase shift keying, and binary and quadrature frequency shift keying. The experiment results show the NTN to perform well for low carrier to noise ratio (CNR) input signals, in addition to outperforming the decision tree classifier.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE Military Communications Conference
Editors Anon
PublisherPubl by IEEE
Number of pages5
ISBN (Print)0780309537
StatePublished - Dec 1 1993
EventProceedings of the 12th Annual IEEE Military Communications Conference - Boston, MA, USA
Duration: Oct 12 1993Oct 14 1993

Publication series

NameProceedings - IEEE Military Communications Conference


OtherProceedings of the 12th Annual IEEE Military Communications Conference
CityBoston, MA, USA

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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