# Blind Recognition of Frame Synchronization Based on Deep Learning
## 4 Conclusions  
In order to improve the error tolerance of classical blind frame synchronization recognition algorithm, we proposed a BRFS-DL algorithm based on deep learning. Inspired by the property that RGB images have distinct stripes when the number of columns of the matrix matches the frame length, we generate positive and negative samples to train the neural network model. Moreover, we interpolated the original Binary data in order to enhance the stripe features and combined the scaling factor to improve the recognition accuracy. Three recognition networks, the frame length recognition network, frame synchronization code recognition network, and combined frame length and frame synchronization code recognition network are separately trained. Finally, the recognition networks are tested and verified using a test set. Simulation results demonstrate that compared with the algorithm proposed in reference [12], BRFS-DL algorithm achieves higher recognition accuracy. Even at an error rate of 0.25, the recognition accuracy still exceeds 90%, BRFS-DL algorithm exhibits significant improvements in error tolerance.  
Nevertheless, our current algorithm is still unable to operate in a completely non-cooperative communication environment without any prior information. The trained neural network model can only recognize a limited number of predefined frame synchronization categories, indicating insufficient generalization capability. Moreover, in order to simplify the data preprocessing steps, we intentionally discarded several operations that could highlight the features of the frame synchronization code, such as gradient computation and Fourier transform. This may, to some extent, limit the further improvement of the algorithm's performance. In addition, our algorithm has only experimented with CNN networks. Future work will consider architectures such as recurrent neural network(RNN) and Transformers, with a greater focus on enhancing the model's generalization ability and optimizing data preprocessing steps, so that our algorithm can be more effectively applied in practical non-cooperative communication scenarios.  
Conceptualization, J.W. and S.Z.; methodology, J.W.; software, J.W. and M.J.; validation, X.H., D.Q. and C.C.; formal analysis, D.Q.; investigation, M.J.; resources, M.J.; data curation, J.W.; writing--original draft preparation, J.W.; writing--review and editing, J.W. and D.Q.; visualization, M.J.; supervision, S.Z., X.H., D.Q. and C.C.; project administration, X.H.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Foundation of National Key Laboratory of Electromagnetic Environment: (6142403220202), the National Natural Science Foundation of China (62072360, 62172438), the key research and development plan of Shaanxi province (2021 ZDLGY02-09, 2023-GHZD-44, 2023-ZDLGY-54), the National Key Laboratory Foundation (2023-JCJQ-LB-007), the Natural Science Foundation of Guangdong Province of China (2022A1515010988), Key Project on Artificial Intelligence of Xi'an Science and Technology Plan (23 ZDCYJSGG0021-2022, 23 ZDCYYYJCJ0008, 23 ZDCYJSGG0002-2023), and the Proof-of-concept fund from Hangzhou Research Institute of Xidian University (GNYZ2023 QC0201, GNYZ2024 QC004, GNYZ2024 QC015).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.