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Deep-Learning-based-Modulation-Classification

In this work, we investigate the value of employing deep learning for the task of wireless signal modulation classification. We generate two data sets which simulate an AWGN channel and a Rayleigh channel. We consider a baseline method using cumulants and compare it with the deep learning approach across a varying range of signal-tonoise ratios . We use a convolutional neural network (CNN) architecture, a recurrent neural network (RNN) architecture and a Convolutional Long Short-term Deep Neural Network (CLDNN) architecure for the purpose of classification. Finally we conclude with an evalution of the performance of the architectures on the RadioML 2016 dataset.

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