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

Korea Society of Digital Industry and Information Management (디지털산업정보학회)
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Deep learning-based classification for IEEE 802.11ac modulation scheme detection

IEEE 802.11ac 변조 방식의 딥러닝 기반 분류

  • 강석원 (한양대학교 전자컴퓨터통신공학과) ;
  • 김민재 (한양대학교 전자컴퓨터통신공학과) ;
  • 최승원 (한양대학교 전자컴퓨터통신공학과)
  • Received : 2020.06.01
  • Accepted : 2020.06.18
  • Published : 2020.06.30
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Abstract

This paper is focused on the modulation scheme detection of the IEEE 802.11 standard. In the IEEE 802.11ac standard, the information of the modulation scheme is indicated by the modulation coding scheme (MCS) included in the VHT-SIG-A of the preamble field. Transmitting end determines the MCS index suitable for the low signal to noise ratio (SNR) situation and transmits the data accordingly. Since data field decoding can take place only when the receiving end acquires the MCS index information of the frame. Therefore, accurate MCS detection must be guaranteed before data field decoding. However, since the MCS index information is the information obtained through preamble field decoding, the detection rate can be affected significantly in a low SNR situation. In this paper, we propose a relatively robust modulation classification method based on deep learning to solve the low detection rate problem with a conventional method caused by a low SNR.

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References

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