A resilient cognitive detection with automated channel selection for enhanced channel management in wireless local area network

Spectrum saturation problem is a critical issue now a day due to huge number of users with wireless capable devices joined the network every day, but the spectrum resources are limited. To overcome this issue, cognitive radio (CR) technology was first proposed in year of 1999. The main objective of...

Full description

Saved in:

Bibliographic Details
Format:	Thesis
Language:	English
Subjects:	Cognitive radio networks Wireless LANs Spectrum saturation Frequency spectrum Spectrum detection
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72569/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72569/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72569/4/Mohammad%20Nayeem.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-unimap-72569
record_format	uketd_dc
spelling	my-unimap-725692021-12-17T03:23:11Z A resilient cognitive detection with automated channel selection for enhanced channel management in wireless local area network Sabira, Khatun, Prof. Dr. Spectrum saturation problem is a critical issue now a day due to huge number of users with wireless capable devices joined the network every day, but the spectrum resources are limited. To overcome this issue, cognitive radio (CR) technology was first proposed in year of 1999. The main objective of CR is to use licensed primary users’ (PUs’) spectrum by secondary users (SUs) without interfering the PUs. To detect PU channels, spectrum detection technique plays a major role to find the presence or absence of PUs to avoid interference. To protect licensed PUs’ from unwanted interference, the channel detection scheme is required to perform well in low signal to noise ratio (SNR) environments. Most of the work in the literature were performed based on computer based simulations and very few with experimental workflow, but they have used heavy laboratory instruments or stationary sensors for channel detection. Considering this issue, this thesis presents a method for real-time channel detection technique in a wireless local area network (WLAN) based CR network using Android based smartphones/tablet PCs. Also, designed an automatic channel selection (ACS) algorithm, which is practically experimented with an adaptive threshold determination technique with the 2.4 GHz WLAN. The algorithm is designed to work especially with Android based smartphones and tablets. Energy detection method with free space path loss (FSPL) environment is considered throughout the experiment for available PU channel detection. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72569 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72569/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72569/1/Page%201-24.pdf 025428c86564a1ea8500e62e7902af9d http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72569/2/Full%20text.pdf abc02e8e377b18a42036e5e832688af0 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72569/4/Mohammad%20Nayeem.pdf b519786a01ebc9540389edbc4c9adb91 Universiti Malaysia Perlis (UniMAP) Cognitive radio networks Wireless LANs Spectrum saturation Frequency spectrum Spectrum detection School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Sabira, Khatun, Prof. Dr.
topic	Cognitive radio networks Wireless LANs Spectrum saturation Frequency spectrum Spectrum detection
spellingShingle	Cognitive radio networks Wireless LANs Spectrum saturation Frequency spectrum Spectrum detection A resilient cognitive detection with automated channel selection for enhanced channel management in wireless local area network
description	Spectrum saturation problem is a critical issue now a day due to huge number of users with wireless capable devices joined the network every day, but the spectrum resources are limited. To overcome this issue, cognitive radio (CR) technology was first proposed in year of 1999. The main objective of CR is to use licensed primary users’ (PUs’) spectrum by secondary users (SUs) without interfering the PUs. To detect PU channels, spectrum detection technique plays a major role to find the presence or absence of PUs to avoid interference. To protect licensed PUs’ from unwanted interference, the channel detection scheme is required to perform well in low signal to noise ratio (SNR) environments. Most of the work in the literature were performed based on computer based simulations and very few with experimental workflow, but they have used heavy laboratory instruments or stationary sensors for channel detection. Considering this issue, this thesis presents a method for real-time channel detection technique in a wireless local area network (WLAN) based CR network using Android based smartphones/tablet PCs. Also, designed an automatic channel selection (ACS) algorithm, which is practically experimented with an adaptive threshold determination technique with the 2.4 GHz WLAN. The algorithm is designed to work especially with Android based smartphones and tablets. Energy detection method with free space path loss (FSPL) environment is considered throughout the experiment for available PU channel detection.
format	Thesis
title	A resilient cognitive detection with automated channel selection for enhanced channel management in wireless local area network
title_short	A resilient cognitive detection with automated channel selection for enhanced channel management in wireless local area network
title_full	A resilient cognitive detection with automated channel selection for enhanced channel management in wireless local area network
title_fullStr	A resilient cognitive detection with automated channel selection for enhanced channel management in wireless local area network
title_full_unstemmed	A resilient cognitive detection with automated channel selection for enhanced channel management in wireless local area network
title_sort	resilient cognitive detection with automated channel selection for enhanced channel management in wireless local area network
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Computer and Communication Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72569/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72569/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72569/4/Mohammad%20Nayeem.pdf
_version_	1747836870334611456

A resilient cognitive detection with automated channel selection for enhanced channel management in wireless local area network

Similar Items