## 6 Conclusions  
Aiming at the problem that the recognition performance of deep learning-based modulation recognition methods is limited under harsh electromagnetic environments, this paper proposes a modulation recognition algorithm based on time-frequency features and deep learning. The purpose of this algorithm is to accurately recognize the modulation type of communication signals in intricate channel. The algorithm employs STFT to preprocess the signal and generate TFIs, which are then used as inputs to the deep learning model. The CDRNN proposed in this paper conducts automatic extraction of time-frequency features through CNNs. It combines residual connectivity and dense connectivity to achieve effective sharing and reuse of features. In addition, the CA mechanism is introduced in the algorithm. This further enhances the DNN's ability to accurately locate and recognize the time-frequency features of modulated signals with little additional computational cost. This not only enhances the model's ability to characterize signal features but also maintains good robustness. In comparison with most other image-based deep learning methods, the CDRNN boasts a considerably reduced memory occupancy by over 32%. The parameter quantity of this model is reduced by more than 30%, which indicates that the CDRNN has the capacity to process samples at a significantly faster speed. Generally speaking, it is more lightweight in nature. Even in the most intricate fading channel circumstances, the proposed scheme exhibits the highest recognition accuracy. The image-based CDRNN can exhibit better resistance to CFO, SRO and multipath fading. Simultaneously, the modulation recognition algorithm proposed in this paper improves the overall accuracy by 3-6% when compared with other algorithms in fading channels.  
Conceptualization, R.Y., X.Z. and Y.Y.; methodology, R.Y., X.Z. and Y.Y.; software, Y.Y.; funding acquisition, R.Y., X.Z. and L.L.; investigation, Y.Y. and L.L.; validation, R.Y., X.Z. and Y.F.; writing--original draft, Y.Y.; writing--review and editing, Y.Y., X.Z., Y.F. and L.L. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Performance of DNNs under various fading conditions. The distinctions in the curve line styles represent different datasets. By adjusting the channel model in Section 3.2, there are three datasets, which, respectively, correspond to three fading channels.  
**Funding:** This work was supported by National Natural Science Foundation of China (Grant No. 62401473), by National Key Laboratory of Unmanned Aerial Vehicle Technology in NPU (Grant No. WR202404), by the Fundamental Research Funds for the Central Universities (Grant No. G2024 WD0159, D5000240239), by National Key Laboratory Fund Project for Space Microwave Communication (Grant No. HTKI2024 KL504010), by Guangdong Basic and Applied Basic Research Foundation (No. 2021A1515110077), by 2023 Zhongdian Tian'ao Innovation Theory and Technology Group Fund (Grant No. 2023 JSQ0101), and by Open Research Subject of State Key Laboratory of Intelligent Game (ZBKF-24-15).  
**Data Availability Statement:** The data presented in this study are available on request from the author. Please write an email to the authors.  
**Acknowledgments:** The authors wish to express their appreciation to the editors for their rigorous and efficient work and the reviewers for their helpful suggestions, which greatly improved the presentation of this paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.