A Radio Access Technology Identification Based on Collaborative Machine Learning for Cognitive Radio Networks 


Vol. 44,  No. 9, pp. 1694-1697, Sep.  2019
10.7840/kics.2019.44.9.1694


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  Abstract

In this letter, we propose a radio access technology (RAT) identification based on collaborative machine learning for cognitive radio (CR) networks consisting of multiple primary users, a base station and multiple secondary users. In particular, a RAT identification technique can be exploited for improving radio resource utilization as well as minimizing interference between primary and secondary users through adaptively change transmission parameters at CR users. During the training period, each secondary user senses the wireless channel and records the received signal power. After the training period, all of secondary users feedback the recorded data to the fusion center. The fusion center identifies the RAT in the primary network. Via simulations, the proposed technique outperforms the conventional technique in terms of identification accuracy during the same time.

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  Cite this article

[IEEE Style]

J. Yoon, W. Son, B. C. Jung, "A Radio Access Technology Identification Based on Collaborative Machine Learning for Cognitive Radio Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 9, pp. 1694-1697, 2019. DOI: 10.7840/kics.2019.44.9.1694.

[ACM Style]

Janghyuk Yoon, Woong Son, and Bang Chul Jung. 2019. A Radio Access Technology Identification Based on Collaborative Machine Learning for Cognitive Radio Networks. The Journal of Korean Institute of Communications and Information Sciences, 44, 9, (2019), 1694-1697. DOI: 10.7840/kics.2019.44.9.1694.

[KICS Style]

Janghyuk Yoon, Woong Son, Bang Chul Jung, "A Radio Access Technology Identification Based on Collaborative Machine Learning for Cognitive Radio Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 9, pp. 1694-1697, 9. 2019. (https://doi.org/10.7840/kics.2019.44.9.1694)

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pISSN : 1226 - 4717
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