Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio
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Department of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
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Department of Aerospace Engineering, The Pennsylvania State University, University Park, PA 16802, USA
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School of Engineering Design, Technology, and Professional Programs, The Pennsylvania State University, University Park, PA 16802, USA
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Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA 16802, USA
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Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802, USA
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Author to whom correspondence should be addressed.
Academic Editors: Sanqing Hu, Lian Zhao and Nazanin Rahnavard
Electronics 2015, 4(2), 221-238; https://doi.org/10.3390/electronics4020221
Received: 23 November 2014 / Revised: 14 March 2015 / Accepted: 16 March 2015 / Published: 26 March 2015
(This article belongs to the Special Issue Cognitive Radio: Use the Spectrum in a More Efficient Way)
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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Keywords:
cognitive radio network; big data applications; data mining; wireless communication; data mining-informed cognitive radio
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Kotobi, K.; Mainwaring, P.B.; Tucker, C.S.; Bilén, S.G. Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio. Electronics 2015, 4, 221-238.
AMA Style
Kotobi K, Mainwaring PB, Tucker CS, Bilén SG. Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio. Electronics. 2015; 4(2):221-238.
Chicago/Turabian StyleKotobi, Khashayar; Mainwaring, Philip B.; Tucker, Conrad S.; Bilén, Sven G. 2015. "Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio" Electronics 4, no. 2: 221-238.
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