Abstract:
Automatic modulation classification technology is an important research field in wireless communication technology.Two deep learning models, convolutional neural network and long short-term memory network, have been widely used in feature-based automatic modulation classification technology. However, these two models have some problems in practical applications. The accuracy of the Convolution Neural Network model when it is classifying long-term dependence sequences is not good, and the time performance of the Long Short-Term Memory (LSTM) model will decrease significantly as the input data size increases. In response to these problems, this paper proposes a modulation recognition algorithm based on an LSTM model with attention mechanism. This algorithm first reads the original signal data sampled by a certain length, and extracts the signal features through the LSTM network.Then it uses the attention mechanism to assigns weights to the learned features to reduce data redundancy. Finally the classifier outputs the classification results according to the features learned by the LSTM network.The simulation experiment proves that the algorithm can achieve a high accuracy with a low time cost.