# Convolutional Neural Network (CNN)-Based Frame Synchronization Method
###### Abstract  
A new frame synchronization technique based on convolutional neural network (CNN) is proposed for synchronized networks. To estimate the exact packet arrival time, the receiver typically uses the correlator between the received signal and the preamble or pilot in front of the transmitted packet. The conventional frame synchronization technique searches the correlation peak within the time window. In contrast, the proposed method utilizes a CNN to find the packet arrival time. Specifically, in the proposed method, the 1D correlator output is converted into a 2D matrix by reshaping, and the resulting signal is inputted to the proposed 4-layer CNN classifier. Then, the CNN predicts the packet arrival time. To verify the frame synchronization performance, computer simulation is performed for two channel models: additive white Gaussian noise and fading channels. Simulation results show that the proposed CNN-based synchronization method outperforms the conventional correlation-based technique by 2 dB. CNN; 2D transformation; frame synchronization; deep learning; synchronized communication networks +
Footnote †: journal: Applied  
_articles + 10  
systems [7, 8], and orthogonal frequency division multiple access (OFDM) systems [9, 10]. Optimum frame synchronization is known as the maximum likelihood (ML) technique [4, 8]. ML technique requires an exhaustive search among all possible packet arrival times. The huge computation is difficult to handle. Therefore, practical frame synchronization techniques have been researched so far [4, 5, 6, 7, 8, 9, 10]. Those practical frame synchronization techniques, regardless of modulation schemes, are based on the correlation peak search. In other words, the conventional frame synchronization methods find the packet arrival time through the correlation between the received signal and the preamble. In detail, when the output of the correlator exceeds a certain threshold, the instance is determined to be the packet arrival time. The best threshold, in general, is a function of signal-to-noise ratio (SNR). Thus, to find the optimal threshold, the SNR of the received signal should be estimated before the frame synchronization. As the frame synchronization performance is highly sensitive to the SNR estimation accuracy [11], as one of the most intuitive methods, the peak detection of the correlator output within the time window is widely used [4]. Frame synchronization based on peak search at correlator output has a long history, but it is still the most widely used technique in recent communication systems [6, 8, 10]. In this paper, to improve the synchronization performance of the synchronized networks, we propose a new frame synchronization method based on a convolution neural network (CNN) classifier. The CNN, one of the most famous deep learning methods, first appeared in the introduction of the LeNet-5 that recognizes handwritten numbers, and recently, it is widely used in the field of image processing [12, 13, 14, 15, 16] and wireless signal processing [17, 18]. Herein, we propose a new frame synchronization method by transforming the frame synchronization problem into a CNN problem. To the best of our knowledge, there is no existing work that applies CNN-based techniques to the frame synchronization problem. Specifically, the one-dimensional (1D) correlator output for frame synchronization is transformed to a two-dimensional (2D) signal, and the 2D signals are used as the training samples with the ground truth labels, which are obtained in the training signal generation. As CNN is specialized in image processing, converting the original 1D signal into 2D signal is a widely used technique to apply CNN in other applications. The training samples are generated under additive white Gaussian noise (AWGN) channels with random arrival times and SNRs. Those 2D signals are inputted to the CNN, and the CNN classifier is trained to predict the packet arrival time. We design the CNN classifier with three convolutional layers and one fully connected layer.