基于TCNN-BiLSTM网络的调制识别算法

针对传统调制识别算法在低信噪比下识别率不高的情况,提出双路卷积神经网络级联双向长短时记忆(two-way convolutional neural network cascaded bidirectional long short-term memory, TCNN-BiLSTM)网络的调制识别算法。首先,该算法并联不同尺度卷积核的卷积层,提取调制信号不同维度的特征。然后,级联BiLSTM层,对多维特征构建LSTM时间模型。最后,使用softmax分类器完成识别。仿真实验表明,所提算法结构在加性高斯白噪声和特定信道参数的瑞利衰落信道下，性能要优于基于传统特征和其他网络结构的识别算法。在特定信道参数的瑞利衰落信道下信噪比低至6 dB时,该算法对6种数字调制信号的识别率仍可达到92%以上。

Modulation recognition algorithm based on TCNN-BiLSTM

For the traditional modulation recognition algorithm, the recognition rate is not high at low signal to noise ratio (SNR). The paper proposes a two-way convolutional neural network casaded bidirectional long short-term memory (TCNN-BiLSTM) network modulation recognition algorithm. Firstly, the algorithm parallelizes the convolutional layers with convolution kernels of different scales to extract features of different dimensions of the modulation signal. Then it cascades the BiLSTM layers to build LSTM time model for multi-dimensional features. Finally, a softmax classifier is used to complete the recognition. Simulation experiments show that the performance of the algorithm structure under additive Gaussian white noise and Rayleigh fading channels with specific channel parameters is better than the recognition algorithms based on traditional features and other network structures. When the SNR in the Rayleigh fading channel with specific channel parameters is as low as 6 dB, the recognition rate of the six digital modulation signals can still reach above 92%.