Data-Throughput Enhancement Using Data Mining-Informed Cognitive Radio

Electronics

Khashayar Kotobi¹  Philip B. Mainwaring⁴  Conrad S. Tucker²  Sven G. Bilén¹  Sanqing Hu³  Lian Zhao³ 

[1] Department of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802, USA; E-Mail:;School of Engineering Design, Technology, and Professional Programs, The Pennsylvania State University, University Park, PA 16802, USA; E-Mail:;Department of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802, USA; E-Mail;Department of Aerospace Engineering, The Pennsylvania State University, University Park, PA 16802, USA; E-Mail:

关键词: cognitive radio network;    big data applications;    data mining;    wireless communication;    data mining-informed cognitive radio;   

DOI  :  10.3390/electronics4020221

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland