School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China.
Sensors (Basel). 2021 Feb 24;21(5):1577. doi: 10.3390/s21051577.
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%.
自动调制识别（AMR）是非合作无线通信系统中的一项重要技术。本文提出了一种深度复型网络，该网络级联了双向长短期记忆网络（DCN-BiLSTM）用于 AMR。针对传统卷积神经网络（CNN）的卷积操作丢失调制信号的部分相位信息，导致识别精度较低的问题，我们首先应用深度复型网络（DCN）来提取包含相位和幅度信息的调制信号的特征。然后，我们级联双向长短期记忆（BiLSTM）层，根据提取的特征构建一个双向长短期记忆模型。BiLSTM 层可以很好地提取信号的上下文信息，并解决长期依赖问题。接下来，我们将特征输入到全连接层。最后，使用 softmax 分类器进行分类。仿真实验表明，我们提出的算法性能优于其他神经网络识别算法。当信噪比（SNR）超过 4dB 时，我们的模型对 11 种调制信号的识别率可达 90%。
