IEEE 802.11ac 변조 방식의 딥러닝 기반 분류 = Deep learning-based classification for IEEE 802.11ac modulation scheme detection

다국어 초록 (Multilingual 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.

참고문헌 (Reference)

1 조은숙, "딥러닝을 통한 의미ㆍ주제 연관성 기반의 소셜 토픽 추출 시스템 개발" (사)디지털산업정보학회 14 (14): 35-45, 2018

2 임상헌, "딥 러닝 기반의 악성흑색종 분류를 위한 컴퓨터 보조진단 알고리즘" (사)디지털산업정보학회 14 (14): 69-77, 2018

3 Peng, Shengliang, "Modulation classification using convolutional neural network based deep learning model" 2017

4 Korowajczuk, Leonhard, "LTE, WiMAX and WLAN network design, optimization and performance analysis" John Wiley & Sons 2011

5 Xiao, Yang, "IEEE 802.11 n: enhancements for higher throughput in wireless LANs" 12 (12): 82-91, 2005

6 Ong, Eng Hwee, "IEEE 802.11 ac:Enhancements for very high throughput WLANs" 2011

7 Lawrence, Steve, "Face recognition: A convolutional neural-network approach" 8 (8): 98-113, 1997

8 Mobasseri, Bijan G, "Digital modulation classification using constellation shape" 80 (80): 251-277, 2000

9 Mendis, Gihan J., "Deep learning-based automated modulation classification for cognitive radio" 2016

10 Wang, Tianqi, "Deep learning for wireless physical layer: Opportunities and challenges" 14 (14): 92-111, 2017

11 Kingma, Diederik P., "Adam: A method for stochastic optimization"