# Blind Recognition of Frame Synchronization Based on Deep Learning
## 1 Introduction
Finally, conduct statistical analysis on the E-SW set to filter out the codewords of the unknown synchronization word [18]. [PERSON] et al. studied a joint frame synchronization and frequency offset estimation algorithm suitable for tropospheric scatter communication, addressing the issues of multipath fading and frequency offset at both the transmission and reception ends. They employed a partial correlation frequency domain capture algorithm based on Fast Fourier Transform (FFT) to search for the maximum correlation value and frequency offset index, simultaneously achieving frame synchronization and frequency offset estimation [19]. The aforementioned algorithms rely on good communication conditions as well as sufficient received data which limits their application in the case of high bit error rates and limited intercepted data. Convolutional Neural Networks (CNN) [20] are a type of feedforward neural network that includes convolutional computations and has a deep structure. The parameter sharing of convolutional kernels within the hidden layers and the sparsity of connections between layers enable CNNs to learn grid-like topological features, such as pixels and audio, with a smaller computational load. This leads to stable performance without requiring additional feature engineering, making them widely applicable in fields such as computer vision and natural language processing [21; 22; 23]. Some researchers have introduced deep learning and CNN into frame synchronization recognition [24; 25; 26; 27]. [PERSON] utilized some one-dimensional data labeled with one-hot encoding to train the neural network and realized the synchronization recognition of equal-length frames [24]. [PERSON] and [PERSON] transformed digital sequences into images with column number equal to frame length and then trained the neural network to recognize the frame length [25]. [PERSON] combined recurrent neural network (RNN) with the sliding window and periodic sampling, and implemented the recognitionof frame synchronization under Rayleigh fading channel conditions [26]. The data preprocessing process in [24; 26] did not involve the idea of converting the sequence into an image, which did not adequately highlight the sample features. Moreover, the positive samples in [25] are limited to the case where the matrix column number is strictly equal to the frame length. [PERSON] et al. proposed a CNN-based symbol timing synchronization method for pilotless OFDM systems under severe multipath fading channels. By using a supervised CNN to classify the signals based on spectrograms, which contain power density information in both the time and frequency domains, simulation results show that this method can provide more accurate synchronization [27]. The Transformer architecture is a deep learning model designed for sequence data. It primarily utilizes a self-attention mechanism to capture long-range dependencies and allows for parallel processing of input, improving training efficiency. It excels at processing complete sequences of images by learning the global relationships within the sequence to update each patch. It has been widely applied in various fields such as image understanding, object detection, and semantic segmentation [28; 29]. When frame-synchronized sequences are converted into matrices, the frame synchronization codes scattered across the frames are rearranged spatially, forming local stripe features. Depending on the number of columns in the matrix and the parameters of the frame synchronization sequence, the stripe width and inclination of the image samples generated from the matrix vary. Considering that CNNs are more adept at detecting simple features such as edges and lines, we have decided to conduct our research based on the CNN architecture.