Journal of Korea Society of Digital Industry and Information Management (디지털산업정보학회논문지)

Korea Society of Digital Industry and Information Management (디지털산업정보학회)
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CNN based IEEE 802.11 WLAN frame format detection

CNN 기반의 IEEE 802.11 WLAN 프레임 포맷 검출

  • 김민재 (한양대학교 전자컴퓨터통신공학과) ;
  • 안흥섭 (한양대학교 전자컴퓨터통신공학과) ;
  • 최승원 (한양대학교 전자컴퓨터통신공학과)
  • Received : 2020.05.07
  • Accepted : 2020.06.08
  • Published : 2020.06.30
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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.

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References

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