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Classification of Medium Access Cont...

Zhou, Yu.

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Classification of Medium Access Control Protocols in Cognitive Radio Networks Using Deep Learning and Machine Learning Algorithms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Classification of Medium Access Control Protocols in Cognitive Radio Networks Using Deep Learning and Machine Learning Algorithms./
Author:	Zhou, Yu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	109 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10812879
ISBN:	9780438242043

Classification of Medium Access Control Protocols in Cognitive Radio Networks Using Deep Learning and Machine Learning Algorithms.
Zhou, Yu.

Classification of Medium Access Control Protocols in Cognitive Radio Networks Using Deep Learning and Machine Learning Algorithms. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 109 p.

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2018.

Cognitive Radio (CR) is proposed to solve the spectrum underutilization problem. CR collects network information then changes its parameters accordingly. Knowing which Medium Access Control (MAC) protocol is used in the network can help the cognitive radio users to achieve better performance. This work explored the application of deep learning (DL) and machine learning (ML) algorithms in MAC protocol recognition.

ISBN: 9780438242043Subjects--Topical Terms:

649834
Electrical engineering.

Classification of Medium Access Control Protocols in Cognitive Radio Networks Using Deep Learning and Machine Learning Algorithms.
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245 1 0 $a Classification of Medium Access Control Protocols in Cognitive Radio Networks Using Deep Learning and Machine Learning Algorithms.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 109 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 $a Adviser: Yu-dong Yao.
502 $a Thesis (Ph.D.)--Stevens Institute of Technology, 2018.
520 $a Cognitive Radio (CR) is proposed to solve the spectrum underutilization problem. CR collects network information then changes its parameters accordingly. Knowing which Medium Access Control (MAC) protocol is used in the network can help the cognitive radio users to achieve better performance. This work explored the application of deep learning (DL) and machine learning (ML) algorithms in MAC protocol recognition.
520 $a The received signals from time division multiple access (TDMA), slotted aloha and frequency hopping networks are transformed into spectrograms and convolutional neural network (CNN) is used to identify them. Several scenarios are considered, signal with fading, signal with noise and signal with noise and fading. The impact of the observation window is also evaluated for these situations. The CNN successfully identified the MAC protocols under each setting.
520 $a In MAC protocol classification with ML, features are extracted from received signals then fed into support vector machine (SVM). Power features, time features, the correlation coefficient between channels and their combinations are evaluated and selected to achieve the best classification performance.
520 $a MAC protocols are trained and tested according to signal to noise ratio (SNR) in both deep learning and machine learning methods. In the second part of this dissertation, SNR estimation with CNN is studied. The model estimates the SNR level of an input signal then performs MAC protocol classification accordingly.
590 $a School code: 0733.
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer science. $3 523869
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690 $a 0984
710 2 $a Stevens Institute of Technology. $b Computer Engineering. $3 2049827
773 0 $t Dissertation Abstracts International $g 79-12B(E).
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791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10812879