Abstract
Automatic modulation recognition (AMR) is a significant technology in noncooperative wireless communication systems. This paper proposes a deep complex network that cascades the bidirectional long short-term memory network (DCN-BiLSTM) for AMR. In view of the fact that the convolution operation of the traditional convolutional neural network (CNN) loses the partial phase information of the modulated signal, resulting in low recognition accuracy, we first apply a deep complex network (DCN) to extract the features of the modulated signal containing phase and amplitude information. Then, we cascade bidirectional long short-term memory (BiLSTM) layers to build a bidirectional long short-term memory model according to the extracted features. The BiLSTM layers can extract the contextual information of signals well and address the long-term dependence problems. Next, we feed the features into a fully connected layer. Finally, a softmax classifier is used to perform classification. Simulation experiments show that the performance of our proposed algorithm is better than that of other neural network recognition algorithms. When the signal-to-noise ratio (SNR) exceeds 4 dB, our model’s recognition rate for the 11 modulation signals can reach 90%.
Highlights
Sensors 2021, 21, 1577. https://As a significant technology for noncooperative wireless communication systems, automatic modulation recognition (AMR) plays an important role in practical civil and military applications, such as cognitive radio, interference recognition and spectrum monitoring [1].In the absence of prior knowledge, it can identify the modulation type of an intercepted signal, providing parameter information for subsequent demodulation [2].Traditional AMR algorithms can be divided into two categories
There are no significant differences between deep complex network (DCN)-bidirectional long short-term memory (BiLSTM) and convolutional long short-term memory deep neural network (CLDNN), CVC, convolutional neural network (CNN)-long short-term memory (LSTM) and MTL-CNN, which means that the proposed algorithm inherits the excellent performance of the four networks and can replace them in the field of modulation recognition
We propose a classification algorithm based on the DCN-BiLSTM network that achieves direct recognition of 11 different types of modulated signals
Summary
As a significant technology for noncooperative wireless communication systems, automatic modulation recognition (AMR) plays an important role in practical civil and military applications, such as cognitive radio, interference recognition and spectrum monitoring [1]. A CNN based on the constellation map was designed to identify modulation modes that were difficult to distinguish in previous CNNs and improved the ability to classify QAM signals under low SNRs. Li et al [12] studied the AMR method based on the original IQ signal under the parameter estimation error. An AMR method based on spatial transformation network (STN) is proposed, which improves the robustness under parameter estimation errors. West et al [18] applied a CNN, a residual network (Resnet), a convolutional long short-term memory deep neural network (CLDNN) and an Inception network to the modulated signal identification task and compared their respective recognition performances. In. Section 4, we report the results of an experiment conducted to evaluate the proposed method and provide the optimal parameter configuration for the DCN-BiLSTM model.
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