# Blind Recognition of Frame Synchronization Based on Deep Learning
## 1 Introduction  
In modern digital communication systems, frame synchronization is typically achieved by periodically inserting predefined synchronization code into the binary data stream. The recognition of synchronization codes is a prerequisite for subsequent signal processing tasks such as error correction and information extraction. The receiver of the cooperating party can utilize a suitable algorithm to locate the known synchronization codes easily [1; 2]. In contrast, the non-cooperative receiver needs to first obtain synchronization codes form the intercepted signals with little prior information, and then they can further get the original binary data stream, which is a more complicated process compared to the collaborative scenario [3; 4; 5]. The frame synchronization recognition includes obtaining the length of the predefined synchronization code, the specific synchronization code as well as the frame length, which is defined as the interval between the adjacent synchronization codes. In the classical frame synchronization blind recognition algorithms [6; 7; 8; 9; 10; 11; 12; 13], [PERSON] et al. first utilized small-area detection to estimate the frame length [6]. According to the characteristics of QPSK signal, [PERSON] et al. proposed a recognition algorithm based on soft-decision [7], which achieved better performance of synchronization code recognition than hard-decision. In 2017, [PERSON] realized the estimation of frame parameters by constructing a hierarchical model of equal frame length and rank finding in small regions [8]. In 2019, [PERSON] et al. proposed an improved frame synchronization recognition algorithm based onself-correlation, which achieves reasonable frame length estimation by introducing peak-to-average ratio (PAR) [9]. In 2020, [PERSON] and [PERSON] used triple correlation filtering to identify the frame length after eliminating the redundant data of the filling matrix, calculated correlation values to extract the key fields, and finally extracted the synchronization code through dispersion analysis [10]. [PERSON] et al. constructed matrices with varying column numbers and computed the first-order cumulant of these matrices. They set decision thresholds based on the frame structure characteristics to distinguish between synchronous bit columns and data bit columns, thereby achieving the recognition of frame length and synchronization codes [12]. In 2023, [PERSON] utilized high-order cumulant to detect the burst signal in time domain, and then completed the frame synchronization recognition by combining the code domain information [13]. Some researchers have utilized the encoding features of signals in their recognition of frame synchronization [14; 15; 16]. [PERSON] and [PERSON] have achieved the blind identification of encoders and blind estimation of delays by maximizing the average log-likelihood ratio [15]. In 2023, [PERSON] et al. have accomplished blind frame synchronization by utilizing soft information from frozen bits and employing polar coding assistance [16]. [PERSON] et al. improved upon the pattern matching algorithm by removing the bad character array table from the preprocessing phase and retaining only the good suffix character array table. Additionally, to address the issue of the pattern matching algorithm being limited to exact matching, the proposed algorithm introduced error tolerance, enabling fuzzy matching for bit stream data. It also modified the index shifting rules for matching, which helped avoid frame header loss due to bit errors caused by noise in the communication environment, resulting in faster frame header detection speeds and improved bit error rate performance [17]. In 2024, [PERSON] et al. divided the bitstream evenly into multiple windows, and perform a sliding XOR operation on the first two windows to obtain the Extended Synchronization Word (E-SW). Then, use the obtained E-SW to perform a sliding correlation operation with the remaining windows, resulting in the corresponding E-SWs for each window, which form the E-SW set.