RLITNN: A Multi-Channel Modulation Recognition Model Combining Multi-Modal Features

Automatic modulation recognition is a critical step between signal detection and signal demodulation, and it is a critical technology for ensuring proper communication. Since the introduction of 5G technology, wireless communication systems have had massive data throughput, so combining deep learning technology with modulation recognition technology is now one of the most mainstream development directions in the field of communication. In order to effectively improve the robust recognition accuracy of modulation signals in low SNR condition, this paper proposes an end-to-end AMR model based on deep learning, called residual convolution long short memory Improved Transformer-Encoder deep neural network model (RLITNN). First, convolutional network and LSTM network extract the initial features from the communication signals of different modes. Second, the proposed Improved Transformer-Encoder module is used to capture the global and local focus of the extracted features. Then, these features are fused. Finally, the high quality features are further captured from the fused feature vector to enhance the feature representation ability of the model. Experiments on RML2016.10A and RML2016.10B datasets show that the proposed RLITNN has better feature learning ability and better recognition accuracy than other advanced (SOTA) techniques.