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http://dx.doi.org/10.17662/ksdim.2020.16.2.027

CNN based IEEE 802.11 WLAN frame format detection  

Kim, Minjae (한양대학교 전자컴퓨터통신공학과)
Ahn, Heungseop (한양대학교 전자컴퓨터통신공학과)
Choi, Seungwon (한양대학교 전자컴퓨터통신공학과)
Publication Information
Journal of Korea Society of Digital Industry and Information Management / v.16, no.2, 2020 , pp. 27-33 More about this Journal
Abstract
Backward compatibility is one of the key issues for radio equipment supporting IEEE 802.11, the typical wireless local area networks (WLANs) communication protocol. For a successful packet decoding with the backward compatibility, the frame format detection is a core precondition. This paper presents a novel frame format detection method based on a deep learning procedure for WLANs affiliated with IEEE 802.11. Considering that the detection performance of conventional methods is degraded mainly due to the poor performances in the symbol synchronization and/or channel estimation in low signal-to-noise-ratio environments, we propose a novel detection method based on convolutional neural network (CNN) that replaces the entire conventional detection procedures. The proposed deep learning network provides a robust detection directly from the receive data. Through extensive computer simulations performed in the multipath fading channel environments (modeled by Project IEEE 802.11 Task Group ac), the proposed method exhibits superb improvement in the frame format detection compared to the conventional method.
Keywords
IEEE 802.11; Format Detection; Deep Learning; CNN;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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