Abstract:
Automatic Modulation classi cation plays key role in Cognitive Radios and Electronic warfare.
is thesis looks into the bene ts of using deep learning to classify wireless signal modulations,
which include ten di erent types of pulse compression radar modulations, as well
as analogue and digital communication modulations. For SNR ranges ranging from -25dB to
25dB, a data set of these waveforms for three separate IF categories has been created.For three
separate neural networks, the e ect of adjusting the IF value from low to medium and then to
high on modulation classi cation has been observed. In the competition of modulation classi -
cation between the three, the Med IF value comes out on top.Hybrid Neural Network i.e. CNN
and GRU-64 called SGCNN(Sequential Gated Convolutional Neural Network) has been proposed
for be er modulation classi cation performance. e proposed hybrid neural network,
SGCNN (CNN+GRU), outperforms the other neural networks considered in the thesis, such
as CNN, LSTM, GRU, and CNN+LSTM.Finally, it was observed that changing the activation
function from relu to elu has a negative impact on the model’s classi cation accuracy
