MAC Protocol Identification Using Convolutional Neural Networks

Yu Zhou; Shengliang Peng; Yudong Yao

doi:10.1109/WOCC48579.2020.9114930

MAC Protocol Identification Using Convolutional Neural Networks

Yu Zhou, Shengliang Peng, Yudong Yao

Department of Electrical and Computer Engineering

Huaqiao University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

Making network nodes aware of the spectrum parameters can help to improve the spectrum utilization and network efficiency. To achieve such goals, machine learning (ML) and deep learning (DL) have been utilized to identify spectrum parameters, such as modulation formats, power levels, medium access control (MAC) protocols, etc. This paper explores MAC protocol identification using ML and DL in additive white Gaussian noise (AWGN) and Rayleigh fading environments. We transform the received signals into spectrogram and utilize convolutional neural networks (CNN) to recognize the MAC protocols. Experimentation results demonstrate the effectiveness in MAC protocol identification using ML and DL algorithms.

Original language	English
Title of host publication	2020 29th Wireless and Optical Communications Conference, WOCC 2020
ISBN (Electronic)	9781728161242
DOIs	https://doi.org/10.1109/WOCC48579.2020.9114930
State	Published - May 2020
Event	29th Wireless and Optical Communications Conference, WOCC 2020 - Newark, United States Duration: 1 May 2020 → 2 May 2020

Publication series

Name	2020 29th Wireless and Optical Communications Conference, WOCC 2020

Conference

Conference	29th Wireless and Optical Communications Conference, WOCC 2020
Country/Territory	United States
City	Newark
Period	1/05/20 → 2/05/20

Keywords

Cellular system
convolutional neural network (CNN)
deep learning
signal classification
spectrum awareness

Access to Document

10.1109/WOCC48579.2020.9114930

Cite this

@inproceedings{9d46c183cd374fa7ad349618278949c8,

title = "MAC Protocol Identification Using Convolutional Neural Networks",

abstract = "Making network nodes aware of the spectrum parameters can help to improve the spectrum utilization and network efficiency. To achieve such goals, machine learning (ML) and deep learning (DL) have been utilized to identify spectrum parameters, such as modulation formats, power levels, medium access control (MAC) protocols, etc. This paper explores MAC protocol identification using ML and DL in additive white Gaussian noise (AWGN) and Rayleigh fading environments. We transform the received signals into spectrogram and utilize convolutional neural networks (CNN) to recognize the MAC protocols. Experimentation results demonstrate the effectiveness in MAC protocol identification using ML and DL algorithms.",

keywords = "Cellular system, convolutional neural network (CNN), deep learning, signal classification, spectrum awareness",

author = "Yu Zhou and Shengliang Peng and Yudong Yao",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 29th Wireless and Optical Communications Conference, WOCC 2020 ; Conference date: 01-05-2020 Through 02-05-2020",

year = "2020",

month = may,

doi = "10.1109/WOCC48579.2020.9114930",

language = "English",

series = "2020 29th Wireless and Optical Communications Conference, WOCC 2020",

booktitle = "2020 29th Wireless and Optical Communications Conference, WOCC 2020",

}

Zhou, Y, Peng, S & Yao, Y 2020, MAC Protocol Identification Using Convolutional Neural Networks. in 2020 29th Wireless and Optical Communications Conference, WOCC 2020., 9114930, 2020 29th Wireless and Optical Communications Conference, WOCC 2020, 29th Wireless and Optical Communications Conference, WOCC 2020, Newark, United States, 1/05/20. https://doi.org/10.1109/WOCC48579.2020.9114930

TY - GEN

T1 - MAC Protocol Identification Using Convolutional Neural Networks

AU - Zhou, Yu

AU - Peng, Shengliang

AU - Yao, Yudong

PY - 2020/5

Y1 - 2020/5

N2 - Making network nodes aware of the spectrum parameters can help to improve the spectrum utilization and network efficiency. To achieve such goals, machine learning (ML) and deep learning (DL) have been utilized to identify spectrum parameters, such as modulation formats, power levels, medium access control (MAC) protocols, etc. This paper explores MAC protocol identification using ML and DL in additive white Gaussian noise (AWGN) and Rayleigh fading environments. We transform the received signals into spectrogram and utilize convolutional neural networks (CNN) to recognize the MAC protocols. Experimentation results demonstrate the effectiveness in MAC protocol identification using ML and DL algorithms.

AB - Making network nodes aware of the spectrum parameters can help to improve the spectrum utilization and network efficiency. To achieve such goals, machine learning (ML) and deep learning (DL) have been utilized to identify spectrum parameters, such as modulation formats, power levels, medium access control (MAC) protocols, etc. This paper explores MAC protocol identification using ML and DL in additive white Gaussian noise (AWGN) and Rayleigh fading environments. We transform the received signals into spectrogram and utilize convolutional neural networks (CNN) to recognize the MAC protocols. Experimentation results demonstrate the effectiveness in MAC protocol identification using ML and DL algorithms.

KW - Cellular system

KW - convolutional neural network (CNN)

KW - deep learning

KW - signal classification

KW - spectrum awareness

UR - http://www.scopus.com/inward/record.url?scp=85091941584&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85091941584&partnerID=8YFLogxK

U2 - 10.1109/WOCC48579.2020.9114930

DO - 10.1109/WOCC48579.2020.9114930

M3 - Conference contribution

AN - SCOPUS:85091941584

T3 - 2020 29th Wireless and Optical Communications Conference, WOCC 2020

BT - 2020 29th Wireless and Optical Communications Conference, WOCC 2020

T2 - 29th Wireless and Optical Communications Conference, WOCC 2020

Y2 - 1 May 2020 through 2 May 2020

ER -

MAC Protocol Identification Using Convolutional Neural Networks

Abstract

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Conference

Keywords

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