Neural Network Recognition of Signal Modulation Types

Author

Daniel C. St. Clair, Missouri University of Science and Technology
B. James
G. L. Hagedorn
C. M. Miller

Abstract

In communications, the recognition of the modulation type of a signal is a difficult and labor intensive process in which a subject matter expert must perform a number of time-consuming tests on a signal. The research presented in this paper lays the foundation for constructing neural network classifiers capable of identifying signals of interest (SOIs) in a dense signal environment. Experimental results provide an evaluation of four different neural network algorithms using nineteen different signal types. The results of this research suggest a hybrid neural network which can successfully classify SOIs.

Recommended Citation

D. C. St. Clair et al., "Neural Network Recognition of Signal Modulation Types," Intelligent Engineering Systems Through Artificial Neural Networks, vol. 7, pp. 567 - 572, Dec 1997.

Department(s)

Mathematics and Statistics

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 The Authors, All rights reserved.

Publication Date

01 Dec 1997