# CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR
## 5 Conclusions  
Addressing the issue of low SNR modulation recognition accuracy, the SP module is used to denoise and preprocess the input signal to eliminate the noise influence on the I/Q data samples. A neural network model of CNN-BiLSTM-DNN is proposed, which is composed of a CNN, BiLSTM, an attention mechanism, and a fully connected deep neural network and uses their complementarity and synergy to extract and classify spatiotemporal features, which solves the problem of single spatial features and temporal features of sample signals extracted by traditional networks. The incorporation of an attention mechanism further enhances the model's ability to discern relevant patterns within the data, effectively filtering out noise and irrelevant information. The model performs noticeably better in recognizing and categorizing target signals of interest thanks to this focused attention on important details.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Total Parameter** & **Training Time** & \multirow{2}{*}{**Training Epochs**} & **Highest Accuracy** & **Average Accuracy** \\  & **Quantity** & **(Second/Epoch)** & & **(\%)** & **(\%)** \\ \hline CNN-BiLSTM- & & & & & \\ DNN & 806,392 & 30 & 69 & 93.18 & 62.73 \\ MCLDNN & 406,070 & 20 & 98 & 92.77 & 61.75 \\ MCNet & 121,611 & 15 & 101 & 83.50 & 56.20 \\ CGDNet & 124,933 & 7 & 188 & 84.00 & 56.21 \\ ResNet & 3,098,283 & 25 & 124 & 83.45 & 54.74 \\ IC-AMCNet & 1,263,882 & 6 & 235 & 84.91 & 56.30 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of model computational complexity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Model** & **Highest Accuracy (\%)** &
\begin{tabular}{c} **Average Accuracy** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{2}{*}{RadioML2016.10b} & With Attention & **93.79** & **64.76** \\  & Without Attention & 93.53 & 64.25 \\ \hline \multirow{2}{*}{RadioML2016.10a} & With Attention & **93.18** & **62.73** \\  & Without Attention & 91.32 & 60.98 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of accuracy between models with and without an attention mechanism. (bold: best)Experimental results indicate that the proposed method may effectively improve the modulation recognition accuracy for low SNR wireless communication signals. Modulation recognition experiments on the benchmark datasets RML2016.10b and RML2016.10a show that the average recognition accuracies of the proposed model from \(-20\) dB to \(18\) dB are \(64.76\%\) and \(62.73\%\), respectively, and the improvement ranges of modulation recognition accuracy are \(0.29\%\) to \(5.56\%\) and \(0.32\%\) to \(4.23\%\) when the SNR is \(-10\) dB to \(4\) dB, respectively, and the computational complexity and training time are also increased. Future work will still be needed to streamline the algorithm and increase accuracy across a few perplexing modulation patterns in order to speed up training even more.  
Conceptualization: Z.L.; methodology: X.Z.; investigation: X.Z. and W.X.; writing-original draft preparation: X.Z. and W.X.; writing-review and editing: Z.L.; supervision: Z.L.; project administration: Z.L.; funding acquisition: Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61801319; in part by Sichuan Science and Technology Program under Grant 2020 JDJQ0061, 2021 YFG0099; in part by Innovation Fund of Chinese Universities under Grant 2020 HYA04001; in part by Engineering Research Center of Integration and Application of Digital Learning Technology, Ministry of Education under Grant 1321002; and in part by the 2022 Graduate Innovation Fund of Sichuan University of Science and Engineering under Grant Y2023288.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts to interest.