Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio

We propose the data mining-informed cognitive radio, which uses non-traditional data sources and data-mining techniques for decision making and improving the performance of a wireless network. To date, the application of information other than wireless channel data in cognitive radios has not been s...

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Main Authors: Khashayar Kotobi, Philip B. Mainwaring, Conrad S. Tucker, Sven G. Bilén
Format: Article
Language:English
Published: MDPI AG 2015-03-01
Series:Electronics
Subjects:
Online Access:http://www.mdpi.com/2079-9292/4/2/221
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author Khashayar Kotobi
Philip B. Mainwaring
Conrad S. Tucker
Sven G. Bilén
author_facet Khashayar Kotobi
Philip B. Mainwaring
Conrad S. Tucker
Sven G. Bilén
author_sort Khashayar Kotobi
collection DOAJ
description We propose the data mining-informed cognitive radio, which uses non-traditional data sources and data-mining techniques for decision making and improving the performance of a wireless network. To date, the application of information other than wireless channel data in cognitive radios has not been significantly studied. We use a novel dataset (Twitter traffic) as an indicator of network load in a wireless channel. Using this dataset, we present and test a series of predictive algorithms that show an improvement in wireless channel utilization over traditional collision-detection algorithms. Our results demonstrate the viability of using these novel datasets to inform and create more efficient cognitive radio networks.
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spelling doaj.art-1dab9f22e50a4d659410abe196063d4c2022-12-22T04:00:23ZengMDPI AGElectronics2079-92922015-03-014222123810.3390/electronics4020221electronics4020221Data-Throughput Enhancement Using Data Mining-Informed Cognitive RadioKhashayar Kotobi0Philip B. Mainwaring1Conrad S. Tucker2Sven G. Bilén3Department of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802, USADepartment of Aerospace Engineering, The Pennsylvania State University, University Park, PA 16802, USASchool of Engineering Design, Technology, and Professional Programs, The Pennsylvania State University, University Park, PA 16802, USADepartment of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802, USAWe propose the data mining-informed cognitive radio, which uses non-traditional data sources and data-mining techniques for decision making and improving the performance of a wireless network. To date, the application of information other than wireless channel data in cognitive radios has not been significantly studied. We use a novel dataset (Twitter traffic) as an indicator of network load in a wireless channel. Using this dataset, we present and test a series of predictive algorithms that show an improvement in wireless channel utilization over traditional collision-detection algorithms. Our results demonstrate the viability of using these novel datasets to inform and create more efficient cognitive radio networks.http://www.mdpi.com/2079-9292/4/2/221cognitive radio networkbig data applicationsdata miningwireless communicationdata mining-informed cognitive radio
spellingShingle Khashayar Kotobi
Philip B. Mainwaring
Conrad S. Tucker
Sven G. Bilén
Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio
Electronics
cognitive radio network
big data applications
data mining
wireless communication
data mining-informed cognitive radio
title Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio
title_full Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio
title_fullStr Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio
title_full_unstemmed Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio
title_short Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio
title_sort data throughput enhancement using data mining informed cognitive radio
topic cognitive radio network
big data applications
data mining
wireless communication
data mining-informed cognitive radio
url http://www.mdpi.com/2079-9292/4/2/221
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