Machine Learning Based Performance Approximation for Millimeter-Wave Cellular Networks 


Vol. 47,  No. 2, pp. 228-231, Feb.  2022
10.7840/kics.2022.47.2.228


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  Abstract

Millimeter-wave multicell networks require a complex mathematical analysis or a long simulation time due to such intrinsic properties as blockage, harsh propagation loss, and beamforming. In this regard, this paper proposes the approximation method of signal-to-interference-plus-noise-ratio (SINR) distributions using polynomial logistic functions and artificial neural networks, and it demonstrates that the proposed method provides a quick yet considerably accurate performance via computer simulations.

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  Cite this article

[IEEE Style]

J. Kim and T. Kwon, "Machine Learning Based Performance Approximation for Millimeter-Wave Cellular Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 2, pp. 228-231, 2022. DOI: 10.7840/kics.2022.47.2.228.

[ACM Style]

Joeun Kim and Taesoo Kwon. 2022. Machine Learning Based Performance Approximation for Millimeter-Wave Cellular Networks. The Journal of Korean Institute of Communications and Information Sciences, 47, 2, (2022), 228-231. DOI: 10.7840/kics.2022.47.2.228.

[KICS Style]

Joeun Kim and Taesoo Kwon, "Machine Learning Based Performance Approximation for Millimeter-Wave Cellular Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 47, no. 2, pp. 228-231, 2. 2022. (https://doi.org/10.7840/kics.2022.47.2.228)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880