Pseudo Code Length Detection in Direct Sequence Spread Spectrum Signal Using Deep Learning
Heetae Jin, Suk Chan Kim · 2019
We designed and simulated a deep learning model to find the pseudo code length of a direct sequence spread spectrum signal. We decompose the received complex sequence into real and imaginary parts and input them into the deep learning model. We constructed training and test data sets considering various SNRs and fully reflected in the model. Finally, we conclude with a discussion of further research that can be performed.