Deep Learning for Recognition of Digital Modulations: A Detailed Study

Abstract

The automatic modulation recognition of the received signal is very attractive in both military and civilian applications. In recent years, deep learning techniques have received much attention since their excellent performance in signal, audio, image and video processing. This paper examines the feasibility of using deep learning algorithms on automatic recognition of the received radio signals' modulation schemes. Modulation recognition has been performed on eight digital modulation types with a signal-to-noise ratio (SNR) from -20dB to 20dB. First, a vanilla neural network is used to classify the type of modulation. Then, convolutional neural networks (CNN) and recurrent neural networks are applied for modulation recognition. These neural networks are widely used in image and signal processing applications. This is followed by designing the other architectures, including densely connected neural network (DenseNet), inception network, recurrent neural network (RNN), long-short term memory network (LSTM), and convolutional long short-term memory deep neural network (CLDNN) for modulation recognition problem, and their results are presented. During this investigation, a basic model is initially considered for each architecture, and then the network performance is studied by adjusting its parameters. The simulation results show that the proposed modified CLDNN model can provide an accuracy of 98% in high SNRs.