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Blind Frame Syncword Detection Using Deep Neural Networks with Input Linear Filtering

Authors
Song, JM[Song, Jun Min]Kil, YS[Kil, Yong-Sung]Kim, SH[Kim, Sang-Hyo]
Issue Date
2019
Publisher
IEEE
Keywords
blind communication; data preprocessing; deep neural network; frame syncword detection
Citation
2019 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC): ICT CONVERGENCE LEADING THE AUTONOMOUS FUTURE, pp.1039 - 1041
Journal Title
2019 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC): ICT CONVERGENCE LEADING THE AUTONOMOUS FUTURE
Start Page
1039
End Page
1041
URI
https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/95064
ISSN
2162-1233
Abstract
In this paper, the problem of detecting a frame syncword (SW), which is repeated in received signal stream, is addressed. We propose a preprocessing technique for deep neural networks used for estimating the SW. As SW has a specific sequence, consecutive received signals are mapped to a symbol using a linear filter in order to make the neural network train their sequence. The performance of SW detection of multi-layer perceptron (MLP) and convolutional neural network (CNN) depending on the frame length and the data preprocessing are evaluated. Simulation results show that CNN has better performance than MLP and data processing improves the performance of CNN.
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