@article{M648E40C3,
title = "Multiple Access Control Protocol using Deep-Reinforcement Learning in Heterogeneous Wireless Networks",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2024",
issn = "1226-4717",
doi = "10.7840/kics.2024.49.1.88",
author = "Do-won Kim, Kyung-seop Shin",
keywords = "Heterogeneous Network, Multiple Access Control, Reinforcement Learning, Packet Collisions",
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."
}