Machine Learning Based Backoff Scheme for Slotted-ALOHA 


Vol. 45,  No. 1, pp. 34-37, Jan.  2020
10.7840/kics.2020.45.1.34


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  Abstract

In this letter, we propose a machine learning-based backoff scheme for slotted-ALOHA systems. The proposed scheme combines the conventional binary exponential backoff technique and the technique using a constant optimal contention window size, obtained with machine learning. Since a specific controller carries out the window size decision, the proposed scheme maintains the simplicity of ALOHA. We show that the proposed scheme improves the system performance remarkably by using simulation.

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  Cite this article

[IEEE Style]

H. J. Kwon, J. H. Lee, D. G. Jeong, "Machine Learning Based Backoff Scheme for Slotted-ALOHA," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 1, pp. 34-37, 2020. DOI: 10.7840/kics.2020.45.1.34.

[ACM Style]

Hyung Jun Kwon, Jung Hoon Lee, and Dong Geun Jeong. 2020. Machine Learning Based Backoff Scheme for Slotted-ALOHA. The Journal of Korean Institute of Communications and Information Sciences, 45, 1, (2020), 34-37. DOI: 10.7840/kics.2020.45.1.34.

[KICS Style]

Hyung Jun Kwon, Jung Hoon Lee, Dong Geun Jeong, "Machine Learning Based Backoff Scheme for Slotted-ALOHA," The Journal of Korean Institute of Communications and Information Sciences, vol. 45, no. 1, pp. 34-37, 1. 2020. (https://doi.org/10.7840/kics.2020.45.1.34)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880