Multiple Access Control Protocol using Deep-Reinforcement Learning in Heterogeneous Wireless Networks 


Vol. 49,  No. 1, pp. 88-94, Jan.  2024
10.7840/kics.2024.49.1.88


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  Abstract

With the increasing number and diversity of mobile communication devices, the efficient allocation of limited frequency bands has become a critical concern. In heterogeneous network environments, where multiple devices coexist within a single network, the application of different Multiple Access Control (MAC) protocols to each device leads to inevitable collisions using conventional methods. In this paper, we propose MAC protocol based on reinforcement learning, aiming to achieve efficient coexistence in such heterogeneous networks. By utilizing reinforcement learning, our proposed protocol mitigates collisions in data transmission, even in scenarios with hardness of information exchange between devices and experimental results demonstrate performance improvements in mixed-network environments.

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[IEEE Style]

D. Kim and K. Shin, "Multiple Access Control Protocol using Deep-Reinforcement Learning in Heterogeneous Wireless Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 1, pp. 88-94, 2024. DOI: 10.7840/kics.2024.49.1.88.

[ACM Style]

Do-won Kim and Kyung-seop Shin. 2024. Multiple Access Control Protocol using Deep-Reinforcement Learning in Heterogeneous Wireless Networks. The Journal of Korean Institute of Communications and Information Sciences, 49, 1, (2024), 88-94. DOI: 10.7840/kics.2024.49.1.88.

[KICS Style]

Do-won Kim and Kyung-seop Shin, "Multiple Access Control Protocol using Deep-Reinforcement Learning in Heterogeneous Wireless Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 1, pp. 88-94, 1. 2024. (https://doi.org/10.7840/kics.2024.49.1.88)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880