Deep Learning Based Automatic Modulation Classification With Long-Short Term Memory Networks

Abstract

The automatic modulation classification (AMC) process is used to determine the modulation format of the transmitted signal at the receiver side without any prior knowledge. Deep learning is a type of machine learning that consists of multiple layers in which raw data is taken as input. This study analyzes the AMC process with a deep learning approach. In this context, performances of LSTM (Long-Short Term Memory) and Bi-LSTM (Bidirectional LSTM) methods on the modulation classification problem are compared. Simulation results show that Bi-LSTM method has a higher performance than does the LSTM method.