## 1 Introduction  
In the field of wireless communication, signal modulation type recognition constitutes a crucial technology and occupies a central place in signal processing and pattern recognition. It serves as the key to the realization of signal detection and demodulation. To efficiently transmit information in the channel, communication systems utilize a variety of modulation techniques to adapt to continuously changing channel conditions. As a result, the modulation method becomes a key feature that distinguishes different communication signals. AMR technology has emerged as an innovative data-driven solution. This method can automatically extract complex features from the original signal data and make accurate recognition decisions without human intervention, thus significantly enhancing the adaptability and robustness of the system in harsh electromagnetic environments. It plays an important role in multiple scenarios, such as signal monitoring, interference recognition, and spectrum sensing [1]. In the wake of the rapid advancement of communication technology and the escalating complexity of electromagnetic environments, modulation types have proliferated and are generally of a high modulation order, thereby rendering AMR increasingly challenging [2]. Therefore, there is a need to explore more efficient and accurate AMR algorithms.  
AMR methods can be classified into three categories depending on the form of signal input: feature extraction-based methods [3; 4; 5], sequence-based methods [6; 7; 8], and image-based methods [9; 10; 11; 12]. Feature extraction-based methods process the signal to obtain one or more features, such as higher-order cumulant features [3], cyclic spectrum [4], etc. Sequence-based methods process signals into one-dimensional signal sequences, including in-phase quadrature (IQ) sequences [6; 7], amplitude-phase (AP) sequences [8], etc. Image-based methods process signals into two-dimensional matrices, enabling the use of classic image recognition algorithms for modulation recognition, which includes time-frequency images [9], bispectrum diagrams [10], constellation diagram [11], etc.  
This paper puts forward a new algorithm based on deep learning and time-frequency analysis for recognizing the modulation of communication signals under fading channels. This algorithm mainly consists of two parts: time-frequency processing and model construction. In the first part, with the assistance of STFT transform, we convert all signals into TFIs and binarize them. These binary TFIs consume very low memory and computational overhead. In the second part, we propose a lighter-weight deep neural network (DNN) to learn the features of time-frequency images and carry out recognition. The model, which combines ResNet and DenseNet [1], incorporates the Coordinate Attention (CA) mechanism. Additionally, we conduct ablation experiments to analyze the effectiveness of the proposed model. The experimental results demonstrate that our proposed method has excellent recognition performance.  
This paper is organized as follows. Section 2 presents related works. In Section 3, the signal selection and parameter setting for the generation of modulation signals, that is, the method of dataset generation, are described. Section 4 presents the proposed modulation recognition algorithm. In Section 5, the signal is simulated and the algorithm is validated. Section 6 summarizes the work of this paper.