Clustering Transmission Opportunity Length (CTOL) Model over Cognitive Radio Network

This paper investigated the throughput performance of a secondary user (SU) for a random primary user (PU) activity in a realistic experimental model. This paper proposed a sensing and frame duration of the SU to maximize the SU throughput under the collision probability constraint. The throughput o...

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Main Authors: Mas Haslinda Mohamad, Aduwati Sali, Fazirulhisyam Hashim, Rosdiadee Nordin, Osamu Takyu
Format: Article
Language:English
Published: MDPI AG 2018-12-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/18/12/4351
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author Mas Haslinda Mohamad
Aduwati Sali
Fazirulhisyam Hashim
Rosdiadee Nordin
Osamu Takyu
author_facet Mas Haslinda Mohamad
Aduwati Sali
Fazirulhisyam Hashim
Rosdiadee Nordin
Osamu Takyu
author_sort Mas Haslinda Mohamad
collection DOAJ
description This paper investigated the throughput performance of a secondary user (SU) for a random primary user (PU) activity in a realistic experimental model. This paper proposed a sensing and frame duration of the SU to maximize the SU throughput under the collision probability constraint. The throughput of the SU and the probability of collisions depend on the pattern of PU activities. The pattern of PU activity was obtained and modelled from the experimental data that measure the wireless local area network (WLAN) environment. The WLAN signal has detected the transmission opportunity length (TOL) which was analyzed and clustered into large and small durations in the CTOL model. The performance of the SU is then analyzed 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.
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spelling doaj.art-7f5202e27f274f11b5fa07d10821ac9c2022-12-22T04:00:31ZengMDPI AGSensors1424-82202018-12-011812435110.3390/s18124351s18124351Clustering Transmission Opportunity Length (CTOL) Model over Cognitive Radio NetworkMas Haslinda Mohamad0Aduwati Sali1Fazirulhisyam Hashim2Rosdiadee Nordin3Osamu Takyu4Research Centre of Excellence for Wireless and Photonics Network (WiPNET), Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaResearch Centre of Excellence for Wireless and Photonics Network (WiPNET), Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaResearch Centre of Excellence for Wireless and Photonics Network (WiPNET), Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, MalaysiaCentre of Advanced Electronic & Communication Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, MalaysiaFaculty of Engineering Electrical and Computer Engineering, Shinshu University, 4-17-1, Wakasato, Nagano City 380 8553, JapanThis paper investigated the throughput performance of a secondary user (SU) for a random primary user (PU) activity in a realistic experimental model. This paper proposed a sensing and frame duration of the SU to maximize the SU throughput under the collision probability constraint. The throughput of the SU and the probability of collisions depend on the pattern of PU activities. The pattern of PU activity was obtained and modelled from the experimental data that measure the wireless local area network (WLAN) environment. The WLAN signal has detected the transmission opportunity length (TOL) which was analyzed and clustered into large and small durations in the CTOL model. The performance of the SU is then analyzed 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.https://www.mdpi.com/1424-8220/18/12/4351cognitive radioopportunistic accessprimary usersecondary usertransmission opportunity lengthWLAN
spellingShingle Mas Haslinda Mohamad
Aduwati Sali
Fazirulhisyam Hashim
Rosdiadee Nordin
Osamu Takyu
Clustering Transmission Opportunity Length (CTOL) Model over Cognitive Radio Network
Sensors
cognitive radio
opportunistic access
primary user
secondary user
transmission opportunity length
WLAN
title Clustering Transmission Opportunity Length (CTOL) Model over Cognitive Radio Network
title_full Clustering Transmission Opportunity Length (CTOL) Model over Cognitive Radio Network
title_fullStr Clustering Transmission Opportunity Length (CTOL) Model over Cognitive Radio Network
title_full_unstemmed Clustering Transmission Opportunity Length (CTOL) Model over Cognitive Radio Network
title_short Clustering Transmission Opportunity Length (CTOL) Model over Cognitive Radio Network
title_sort clustering transmission opportunity length ctol model over cognitive radio network
topic cognitive radio
opportunistic access
primary user
secondary user
transmission opportunity length
WLAN
url https://www.mdpi.com/1424-8220/18/12/4351
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