Slotted ALOHA Based on Reinforcement Learning with Thompson Sampling 


Vol. 46,  No. 10, pp. 1646-1649, Oct.  2021
10.7840/kics.2021.46.10.1646


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  Abstract

We analyzed the performance of the reinforcement learning technique with Thompson Sampling in slotted ALOHA scheme. The performance of the E-greedy, Upper Confidence Bound, and Thompson Sampling methods were compared in simulations. The average throughput and adaptation time to reach the optimal performance in slotted ALOHA with Thompson Sampling were dependent on the parameters values.

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  Cite this article

[IEEE Style]

Y. Jo and G. Hwang, "Slotted ALOHA Based on Reinforcement Learning with Thompson Sampling," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 10, pp. 1646-1649, 2021. DOI: 10.7840/kics.2021.46.10.1646.

[ACM Style]

Yeong-Je Jo and Gyung-Ho Hwang. 2021. Slotted ALOHA Based on Reinforcement Learning with Thompson Sampling. The Journal of Korean Institute of Communications and Information Sciences, 46, 10, (2021), 1646-1649. DOI: 10.7840/kics.2021.46.10.1646.

[KICS Style]

Yeong-Je Jo and Gyung-Ho Hwang, "Slotted ALOHA Based on Reinforcement Learning with Thompson Sampling," The Journal of Korean Institute of Communications and Information Sciences, vol. 46, no. 10, pp. 1646-1649, 10. 2021. (https://doi.org/10.7840/kics.2021.46.10.1646)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880