Machine Learning-Based Performance Approximation in the Uplink Random Access Network 


Vol. 46,  No. 7, pp. 1160-1163, Jul.  2021
10.7840/kics.2021.46.7.1160


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  Abstract

The stochastic geometry-based analysis is possible to understand the effect of operating variables on network performance. But it is possible in a limited environment. Thus, this paper proposed a method that applies stochastic geometry-based machine learning to uplink performance approximation. The proposed method can approximate the effect of the power control variable on the uplink performance. In addition, the proposed method can apply to other network environments.

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  Cite this article

[IEEE Style]

C. Xuan, "Machine Learning-Based Performance Approximation in the Uplink Random Access Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 7, pp. 1160-1163, 2021. DOI: 10.7840/kics.2021.46.7.1160.

[ACM Style]

Can-shou Xuan. 2021. Machine Learning-Based Performance Approximation in the Uplink Random Access Network. The Journal of Korean Institute of Communications and Information Sciences, 46, 7, (2021), 1160-1163. DOI: 10.7840/kics.2021.46.7.1160.

[KICS Style]

Can-shou Xuan, "Machine Learning-Based Performance Approximation in the Uplink Random Access Network," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 7, pp. 1160-1163, 7. 2021. (https://doi.org/10.7840/kics.2021.46.7.1160)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880