LSTM-based Automatic Modulation Classification
Quan Zhou, Xiaojun Jing, Yuan He, Yuanhao Cui, Michel Kadoch, Mohamed Cheriet
Abstract
Recently, automatic modulation classification (AMC) has been studied by more and more researchers, and a host of methods based on deep learning have been proposed. Different from image data, signal data is a sequence that changes with time, and has temporal characteristics. Like the spatial feature, the temporal feature cannot be ignored. In this paper, we add a Long Short-Term Memory (LSTM) module to the convolutional neural network (CNN) to learn the time-related features of wireless signals. LSTM learns the dependency relationship between the current element and the elements before-after through the gating structure. Experimental results show that our proposed methods perform better than the model without LSTM. The proposed network Incept-LSTM has a classification accuracy of 97.5% at a high signal-to-noise ratio (SNR). Compared to the model without LSTM, the classification accuracy of our proposed model is improved by 0.1% to 5.7%. At the same time, we compared the classification accuracy under different SNRs to explore its impact on classification performance. We found the classification ability of LSTM is more prominent at the low SNRs.