# CNN-BiLSTM-DNN-Based Modulation Recognition Algorithm at Low SNR  
[PERSON]  
1 School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, China; [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (W.X.) 1  
[PERSON]  
1 School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, China; [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (W.X.) 1  
[PERSON]  
1 School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, China; [EMAIL_ADDRESS] (W.X.) 1  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/10/](https://www.mdpi.com/journals/pdf/10/)]([https://www.mdpi.com/journals/pdf/10/](https://www.mdpi.com/journals/pdf/10/))  
Compared with the limitations of traditional modulation recognition methods, such as artificial feature analysis, complex algorithms, and large computational costs [9], the current deep learning (DL) AMR technology has brought new solutions to problems that cannot be overcome by traditional modulation recognition methods [10]. The advantage of modulation recognition with DL is that it can automatically extract the features of the signal and obtain the classifier by training the neural network without the need to do complex and difficult artificial features and classifier design, and it has higher recognition accuracy than the traditional modulation recognition method. One of the most common methods is to process the signal into in-phase (I) and quadrature (Q) components and extract them with deep networks. In their research, [PERSON] and his team introduced a cutting-edge DL-powered AMR system tailored for detecting signals in OFDM systems. By leveraging convolutional neural networks to analyze IQ samples of OFDM signals, they achieved remarkable results [11]. Their experiments revealed that, with a signal-to-noise ratio of 10 dB, the accuracy of signal classification exceeded 90%. However, as the signal-to-noise ratio (SNR) dropped below 10 dB, the recognition accuracy experienced a sharp decline. [PERSON] and colleagues [12] took a different approach by developing the CLRD model, a dual-channel fusion network that harnesses deep learning techniques to effectively identify signals. By simultaneously capturing temporal and spatial features of the signals, their model proved to be highly efficient in signal recognition. [PERSON] et al. [13] proposed a multi-stream neural network designed to extract features from various aspects of modulation signals, such as amplitude, phase, frequency, and raw data. Despite its innovative design, the recognition rate for certain modulation types fell short of expectations. Meanwhile, [PERSON] and his team [14] combined a convolutional neural network with a time-frequency attention mechanism to extract crucial features from signals. To address the problem of poor modulation recognition in environments with low SNRs, a cutting-edge automatic modulation recognition model leveraging deep learning techniques has been developed. This innovative model combines a convolutional neural network (CNN), a bidirectional long short-term memory (BiLSTM) network, a deep neural network (DNN), and an attention mechanism. The process begins with the preprocessing of the original IQ signal using a signal processing (SP) module, which effectively eliminates unwanted additive white Gaussian noise. Subsequently, the preprocessed I/Q multi-channel signals are separated into independent I and Q data streams. These three input data streams are then fed into the CNN to capture both multi-channel and single-channel spatial characteristics of the I/Q signals. The incorporation of a spatial attention mechanism ensures that the model focuses on crucial spatial regions while disregarding irrelevant ones.