Opportunistic cognitive radio network with primary user activity model

A Cognitive radio (CR) network is the solution to increase spectrum utilization in wireless systems. The main objective of CR is to get the best frequency that is not in use based on its ability to detect the environment. The CR allows the secondary user (SU) to operate in a licensed spectru...

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Main Author:	Mohamad, Mas Haslinda
Format:	Thesis
Language:	English
Published:	2019
Subjects:	Cognitive radio networks Autonomic computing Wireless communication systems
Online Access:	http://psasir.upm.edu.my/id/eprint/85417/1/FK%202019%20150%20-%20ir.pdf
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id	my-upm-ir.85417
record_format	uketd_dc
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
advisor	Sali, Aduwati
topic	Cognitive radio networks Autonomic computing Wireless communication systems
spellingShingle	Cognitive radio networks Autonomic computing Wireless communication systems Mohamad, Mas Haslinda Opportunistic cognitive radio network with primary user activity model
description	A Cognitive radio (CR) network is the solution to increase spectrum utilization in wireless systems. The main objective of CR is to get the best frequency that is not in use based on its ability to detect the environment. The CR allows the secondary user (SU) to operate in a licensed spectrum without interfering with the primary user (PU). The SU can remain on the spectrum without reducing the transmission power levels. The CR performance is limited by interference from both PU and other CRs. Most previous studies analysed interference through different fading model. The interference to PU was usually measured as the quantity of SUs signal power at the primary receiver. Very few study evaluates temporal interference by considering spectrum occupancy in terms of time used in the spectrum. Therefore, the opportunistic access of CR to the PU network by considering the temporal interference is the main focus of the research. This research investigates the opportunistic access of a CR system which enables the SU to access the detected transmission opportunity length (TOL). The probability of collision between SU and PU and the SU throughput is observed as the PU traffic pattern change. In this part, the research presents the empirical time-dimension of PU activity pattern by the detected of TOL from an empirical measurement data. An experimental setup of a wireless local area network (WLAN) is executed to measure the TOL in the system. The experiment was run in two different scenarios which involved: scenario 1 with one PU and scenario 2 with one PU and one SU accessed the WLAN. The energy detection is performed throughout the detected signals to extract the TOL. The TOLs in both scenarios were analysed and characterised to be used for opportunistic access. An empirical model based on PU traffic for opportunistic access (EM-PuO) is developed. The EM-PuO model characterised the PU traffic pattern with a few distributions fits such as exponential, Generalized Pareto (GP), and normal distribution. Among these distributions, the GP is the best fit for idle states as the DKS = 0.2655 is the lowest. The second part of the work characterises the TOL using Primary User Activity based on a duty cycle (PUA-DC) model. The PU activity is modelled to represent the occupancy spectrum in the time domain in a realistic scenario. The spectrum occupancy in this model indicated as the percentage of a duty cycle. The probability of interference between SU and PU and the data rate of PU are observed as the PU traffic pattern change. Then, the PUA-DC model compared to the existing work to validate the behaviour of the SU and PU performance as there are changing pattern in PU activity. Next, this research studies the SU throughput by clustering the TOL in CTOL model. This model clustered TOL to large and small duration and used Markov model to maximize the SU throughput under detection probability constraint. Then, the performance of the SU is then analysed and compared with static and dynamic PU models. The results showed that the SU throughput in the CTOL model was higher than the static and dynamic models by almost 45% and 12.2% respectively. Furthermore, the probability of collisions in the network and the SU throughput were influenced by the value of the minimum contention window and the maximum back-off stage. The simulation results revealed that the higher contention window had worsened the SU throughput even though the channel has a higher number of TOLs. The last part of this work investigates the scalability effect on CTOL model. Two scenarios of scalability effect have been discussed to answer the third objective are scenario 1 with mul- tiple PU and scenario 2 with multiple SU nodes. The result discovered that SU throughput increased as the number of PU increased. But as the longer SU frame duration, the perfor- mance of throughput degraded, although there are numerous PU in the system.
format	Thesis
qualification_level	Doctorate
author	Mohamad, Mas Haslinda
author_facet	Mohamad, Mas Haslinda
author_sort	Mohamad, Mas Haslinda
title	Opportunistic cognitive radio network with primary user activity model
title_short	Opportunistic cognitive radio network with primary user activity model
title_full	Opportunistic cognitive radio network with primary user activity model
title_fullStr	Opportunistic cognitive radio network with primary user activity model
title_full_unstemmed	Opportunistic cognitive radio network with primary user activity model
title_sort	opportunistic cognitive radio network with primary user activity model
granting_institution	Universiti Putra Malaysia
publishDate	2019
url	http://psasir.upm.edu.my/id/eprint/85417/1/FK%202019%20150%20-%20ir.pdf
_version_	1747813538613690368
spelling	my-upm-ir.854172021-12-16T04:01:02Z Opportunistic cognitive radio network with primary user activity model 2019-05 Mohamad, Mas Haslinda A Cognitive radio (CR) network is the solution to increase spectrum utilization in wireless systems. The main objective of CR is to get the best frequency that is not in use based on its ability to detect the environment. The CR allows the secondary user (SU) to operate in a licensed spectrum without interfering with the primary user (PU). The SU can remain on the spectrum without reducing the transmission power levels. The CR performance is limited by interference from both PU and other CRs. Most previous studies analysed interference through different fading model. The interference to PU was usually measured as the quantity of SUs signal power at the primary receiver. Very few study evaluates temporal interference by considering spectrum occupancy in terms of time used in the spectrum. Therefore, the opportunistic access of CR to the PU network by considering the temporal interference is the main focus of the research. This research investigates the opportunistic access of a CR system which enables the SU to access the detected transmission opportunity length (TOL). The probability of collision between SU and PU and the SU throughput is observed as the PU traffic pattern change. In this part, the research presents the empirical time-dimension of PU activity pattern by the detected of TOL from an empirical measurement data. An experimental setup of a wireless local area network (WLAN) is executed to measure the TOL in the system. The experiment was run in two different scenarios which involved: scenario 1 with one PU and scenario 2 with one PU and one SU accessed the WLAN. The energy detection is performed throughout the detected signals to extract the TOL. The TOLs in both scenarios were analysed and characterised to be used for opportunistic access. An empirical model based on PU traffic for opportunistic access (EM-PuO) is developed. The EM-PuO model characterised the PU traffic pattern with a few distributions fits such as exponential, Generalized Pareto (GP), and normal distribution. Among these distributions, the GP is the best fit for idle states as the DKS = 0.2655 is the lowest. The second part of the work characterises the TOL using Primary User Activity based on a duty cycle (PUA-DC) model. The PU activity is modelled to represent the occupancy spectrum in the time domain in a realistic scenario. The spectrum occupancy in this model indicated as the percentage of a duty cycle. The probability of interference between SU and PU and the data rate of PU are observed as the PU traffic pattern change. Then, the PUA-DC model compared to the existing work to validate the behaviour of the SU and PU performance as there are changing pattern in PU activity. Next, this research studies the SU throughput by clustering the TOL in CTOL model. This model clustered TOL to large and small duration and used Markov model to maximize the SU throughput under detection probability constraint. Then, the performance of the SU is then analysed and compared with static and dynamic PU models. The results showed that the SU throughput in the CTOL model was higher than the static and dynamic models by almost 45% and 12.2% respectively. Furthermore, the probability of collisions in the network and the SU throughput were influenced by the value of the minimum contention window and the maximum back-off stage. The simulation results revealed that the higher contention window had worsened the SU throughput even though the channel has a higher number of TOLs. The last part of this work investigates the scalability effect on CTOL model. Two scenarios of scalability effect have been discussed to answer the third objective are scenario 1 with mul- tiple PU and scenario 2 with multiple SU nodes. The result discovered that SU throughput increased as the number of PU increased. But as the longer SU frame duration, the perfor- mance of throughput degraded, although there are numerous PU in the system. Cognitive radio networks Autonomic computing Wireless communication systems 2019-05 Thesis http://psasir.upm.edu.my/id/eprint/85417/ http://psasir.upm.edu.my/id/eprint/85417/1/FK%202019%20150%20-%20ir.pdf text en public doctoral Universiti Putra Malaysia Cognitive radio networks Autonomic computing Wireless communication systems Sali, Aduwati

Opportunistic cognitive radio network with primary user activity model

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