Electrical & Computer Engineering: Faculty Publications

Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks

Document Type

Conference Proceeding

Publication Date

4-1-2023

Publication Title

IEEE SOUTHEASTCON Conference Proceedings

DOI

10.1109/SoutheastCon51012.2023.10115067

ISBN

9781665476119

Abstract

This paper proposes a neural network architecture that was designed to predict and reverse engineer frequency hopping jamming systems. The neural network was trained for frequency hopping sequences that use maximum-length sequences that utilize minimal polynomials as the primitive polynomial used in the linear-shift feedback register. This information is then used to generate a hopping sequence that reduces the jamming interference to 0 with as few as 4 jammer hopping samples. The model is also capable of determining if the jammer is utilizing a sequence that the model is trained for in as few as 25 jammer hopping samples.

Comments

Georgia Southern University faculty member, Rami J. Haddad co-authored "Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks."

Copyright

This work is archived and distributed under the repository's Standard Copyright and Reuse License (opens in new tab). End users may copy, store, and distribute this work without restriction. For all other uses, permission must be obtained from the copyright owners or their authorized agents.

Link to Full Text

Share

COinS