Staff View: Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain

Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain

A vehicular ad hoc network (VANET) is a major participant in reducing traffic collisions and road congestion. In VANET, an enormous amount of data is needed to be exchanged. However, the dedicated access technique for VANET, the DSRC (dedicated short-range communication), is insufficient to accommod...

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Main Author:	Mohammad Asif , Hossain
Format:	Thesis
Published:	2022
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software
Online Access:	http://studentsrepo.um.edu.my/14334/2/Mohammad_Asif.pdf http://studentsrepo.um.edu.my/14334/1/Mohammad_Asif.pdf http://studentsrepo.um.edu.my/14334/
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spelling	my.um.stud.143342023-04-05T23:02:34Z Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain Mohammad Asif , Hossain QA75 Electronic computers. Computer science QA76 Computer software A vehicular ad hoc network (VANET) is a major participant in reducing traffic collisions and road congestion. In VANET, an enormous amount of data is needed to be exchanged. However, the dedicated access technique for VANET, the DSRC (dedicated short-range communication), is insufficient to accommodate these enormous data quantities. Consequently, VANET faces a lack of spectrum bands. Cognitive radio (CR) can be used to alleviate such a crisis. CR is an intelligent and programmable radio device that accesses various frequency bands. To enter these bands, CR must conduct spectrum sensing. An SU (secondary user) or a CR user can decide whether the spectrum is occupied by some PUs (primary or licensed users) or not. This mechanism is known as spectrum sensing. It is the technique to identify any vacant spectrum (no licensed user is currently using). When it discovers that the PU does not use the licensed spectrum, SU will use it (vacant licensed spectrum) under some restrictions when the DSRC is filled. Several spectrum sensing techniques are found in the literature. Few examples are energy detection, cyclostationary detection, matched filter detection, etc. Due to the speedy nature of the vehicles, these spectrum sensing techniques are confronted with several problems such as rapid change of radio environment, heterogeneous service quality (QoS) requirements, hidden node problem, problems in multipath fading and shadowing, diffraction, various PU activity models, etc. Spectrum sensing must be accurate, faster, dynamic, and flexible due to vehicles' high velocity and constant environmental shift. The optimum spectrum sensing should consider vehicle speed, fading degree, density, radio environment, etc. To solve the above-mentioned issues and problems, this thesis presents a novel segment-based CR-VANET (Seg-CR-VANET) cooperative spectrum sensing (CSS) framework. In this thesis, roads are divided into equal segments, which are then subdivided depending on the probability value. CSS needs to perform two sensing for its operation; local sensing and global sensing. In this thesis, local sensing results are produced by SUs or vehicles by selecting the best spectrum sensing technique. The selection would be made based on the hybrid machine learning (ML) algorithm. A fuzzy-based Naive Bayes algorithm has been used in this case. For the local sensing, values of signal-to-noise ratio (SNR) and noise uncertainty are used. Instead of using a static threshold value, dynamic threshold values are used for sensing techniques. The tri-agent reinforcement learning (TA-RL) algorithm has been used by the segment spectrum agent (SSA) in this suggested CSS to make the global decision. TA-RL algorithm learns three environments (network, signal, and vehicle behavior) to assess the behaviors of PUs. The whole solution has been implemented in OMNeT++ with the required modules and the SUMO traffic generator tool. The simulation findings reveal that the Seg-CR-VANET outperforms existing works in terms of several performance metrics used in spectrum sensing such as probability of detection, false alarm rate, accuracy, throughput, delay, packet delivery ratio, etc. 2022-01 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/14334/2/Mohammad_Asif.pdf application/pdf http://studentsrepo.um.edu.my/14334/1/Mohammad_Asif.pdf Mohammad Asif , Hossain (2022) Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain. PhD thesis, Universiti Malaya. http://studentsrepo.um.edu.my/14334/
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Student Repository
url_provider	http://studentsrepo.um.edu.my/
topic	QA75 Electronic computers. Computer science QA76 Computer software
spellingShingle	QA75 Electronic computers. Computer science QA76 Computer software Mohammad Asif , Hossain Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain
description	A vehicular ad hoc network (VANET) is a major participant in reducing traffic collisions and road congestion. In VANET, an enormous amount of data is needed to be exchanged. However, the dedicated access technique for VANET, the DSRC (dedicated short-range communication), is insufficient to accommodate these enormous data quantities. Consequently, VANET faces a lack of spectrum bands. Cognitive radio (CR) can be used to alleviate such a crisis. CR is an intelligent and programmable radio device that accesses various frequency bands. To enter these bands, CR must conduct spectrum sensing. An SU (secondary user) or a CR user can decide whether the spectrum is occupied by some PUs (primary or licensed users) or not. This mechanism is known as spectrum sensing. It is the technique to identify any vacant spectrum (no licensed user is currently using). When it discovers that the PU does not use the licensed spectrum, SU will use it (vacant licensed spectrum) under some restrictions when the DSRC is filled. Several spectrum sensing techniques are found in the literature. Few examples are energy detection, cyclostationary detection, matched filter detection, etc. Due to the speedy nature of the vehicles, these spectrum sensing techniques are confronted with several problems such as rapid change of radio environment, heterogeneous service quality (QoS) requirements, hidden node problem, problems in multipath fading and shadowing, diffraction, various PU activity models, etc. Spectrum sensing must be accurate, faster, dynamic, and flexible due to vehicles' high velocity and constant environmental shift. The optimum spectrum sensing should consider vehicle speed, fading degree, density, radio environment, etc. To solve the above-mentioned issues and problems, this thesis presents a novel segment-based CR-VANET (Seg-CR-VANET) cooperative spectrum sensing (CSS) framework. In this thesis, roads are divided into equal segments, which are then subdivided depending on the probability value. CSS needs to perform two sensing for its operation; local sensing and global sensing. In this thesis, local sensing results are produced by SUs or vehicles by selecting the best spectrum sensing technique. The selection would be made based on the hybrid machine learning (ML) algorithm. A fuzzy-based Naive Bayes algorithm has been used in this case. For the local sensing, values of signal-to-noise ratio (SNR) and noise uncertainty are used. Instead of using a static threshold value, dynamic threshold values are used for sensing techniques. The tri-agent reinforcement learning (TA-RL) algorithm has been used by the segment spectrum agent (SSA) in this suggested CSS to make the global decision. TA-RL algorithm learns three environments (network, signal, and vehicle behavior) to assess the behaviors of PUs. The whole solution has been implemented in OMNeT++ with the required modules and the SUMO traffic generator tool. The simulation findings reveal that the Seg-CR-VANET outperforms existing works in terms of several performance metrics used in spectrum sensing such as probability of detection, false alarm rate, accuracy, throughput, delay, packet delivery ratio, etc.
format	Thesis
author	Mohammad Asif , Hossain
author_facet	Mohammad Asif , Hossain
author_sort	Mohammad Asif , Hossain
title	Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain
title_short	Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain
title_full	Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain
title_fullStr	Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain
title_full_unstemmed	Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain
title_sort	cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / mohammad asif hossain
publishDate	2022
url	http://studentsrepo.um.edu.my/14334/2/Mohammad_Asif.pdf http://studentsrepo.um.edu.my/14334/1/Mohammad_Asif.pdf http://studentsrepo.um.edu.my/14334/
_version_	1762837974942220288
score	13.211869

Cooperative spectrum sensing based on machine learning in cognitive radio vehicular network / Mohammad Asif Hossain

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