IEEE 802.11ac 변조 방식의 딥러닝 기반 분류
Deep learning-based classification for IEEE 802.11ac modulation scheme detection
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초록
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.
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 (11)
- Ong, Eng Hwee, "IEEE 802.11 ac:Enhancements for very high throughput WLANs", 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications, IEEE (2011)
- Korowajczuk, Leonhard, "LTE, WiMAX and WLAN network design, optimization and performance analysis" (2011), John Wiley & Sons
- 조은숙, "딥러닝을 통한 의미ㆍ주제 연관성 기반의 소셜 토픽 추출 시스템 개발", (사)디지털산업정보학회 논문지, 14(4), pp.35~45 (2018), (사)디지털산업정보학회[DOI]
- 임상헌, "딥 러닝 기반의 악성흑색종 분류를 위한 컴퓨터 보조진단 알고리즘", (사)디지털산업정보학회 논문지, 14(4), pp.69~77 (2018), (사)디지털산업정보학회[DOI]
- Wang, Tianqi, "Deep learning for wireless physical layer: Opportunities and challenges", China Communications, 14(11), pp.92~111 (2017)
- Mendis, Gihan J., "Deep learning-based automated modulation classification for cognitive radio", 2016 IEEE International Conference on Communication Systems (ICCS), IEEE (2016)
- Xiao, Yang, "IEEE 802.11 n: enhancements for higher throughput in wireless LANs", IEEE Wireless Communications, 12(6), pp.82~91 (2005)
- Mobasseri, Bijan G, "Digital modulation classification using constellation shape", Signal processing, 80(2), pp.251~277 (2000)
- Peng, Shengliang, "Modulation classification using convolutional neural network based deep learning model", 2017 26th Wireless and Optical Communication Conference (WOCC), IEEE (2017)
- Lawrence, Steve, "Face recognition: A convolutional neural-network approach", IEEE transactions on neural networks, 8(1), pp.98~113 (1997)
- Kingma, Diederik P., "Adam: A method for stochastic optimization", arXiv preprint arXiv:1412.6980, ~ (2014)