Term of Award

Spring 2023

Degree Name

Master of Science, Electrical Engineering

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Digital Commons@Georgia Southern License


Department of Electrical and Computer Engineering

Committee Chair

Rami Haddad

Committee Member 1

Seungmo Kim

Committee Member 2

Sungkyun Lim


This work proposes a neural network architecture that was designed to predict and reverse engineer frequency hopping jamming systems. The neural network was initially optimized for use with a 12th order linear shift feedback register maximum length sequence utilizing a minimal polynomial as the characteristic polynomial. This neural network was then scaled to accommodate 7 different sequences, of orders 6 through 12. The neural network was trained for these sequences using training data that is 10 times the length of the sequence. 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.

Research Data and Supplementary Material