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http://dx.doi.org/10.6109/jkiice.2020.24.4.501

Deep Learning based Frame Synchronization Using Convolutional Neural Network  

Lee, Eui-Soo (Department of Mobile Convergence and Engineering, Hanbat National University)
Jeong, Eui-Rim (Department of Information and Communication Engineering, Hanbat National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.4, 2020 , pp. 501-507 More about this Journal
Abstract
This paper proposes a new frame synchronization technique based on convolutional neural network (CNN). The conventional frame synchronizers usually find the matching instance through correlation between the received signal and the preamble. The proposed method converts the 1-dimensional correlator ouput into a 2-dimensional matrix. The 2-dimensional matrix is input to a convolutional neural network, and the convolutional neural network finds the frame arrival time. Specifically, in additive white gaussian noise (AWGN) environments, the received signals are generated with random arrival times and they are used for training data of the CNN. Through computer simulation, the false detection probabilities in various signal-to-noise ratios are investigated and compared between the proposed CNN-based technique and the conventional one. According to the results, the proposed technique shows 2dB better performance than the conventional method.
Keywords
Convolutional Neural Network; 2-dimensional transformation; Deep learning; Frame synchronization; Synchronized networks;
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Times Cited By KSCI : 2  (Citation Analysis)
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