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.
Recommended Citation
Strickland, Charles J., Rami J. Haddad.
2023.
"Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks."
IEEE SOUTHEASTCON Conference Proceedings: 313-318: Institute of Electrical and Electronics Engineers Inc..
doi: 10.1109/SoutheastCon51012.2023.10115067 isbn: 9781665476119
https://digitalcommons.georgiasouthern.edu/electrical-eng-facpubs/184
Copyright
See the publisher's copyright and reuse policies.
Comments
Georgia Southern University faculty member, Rami J. Haddad co-authored "Predicting Jamming Systems Frequency Hopping Sequences Using Artificial Neural Networks."