Dr. Kaiguo Yuan Profile

Dr. Kaiguo Yuan

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Proceedings Article | 8 June 2024 Paper

DRLMS: a multipath scheduler based on deep reinforcement learning

Mengyang Zhang, Kaiguo Yuan, Xiaoyong Li

Proceedings Volume 13171, 131711L (2024) https://doi.org/10.1117/12.3031955

KEYWORDS: Neural networks, Education and training, Data transmission, Telecommunication networks, Design, Environmental sensing

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Most current network devices have multiple network interfaces, and multipath transport protocols can utilize multiple network paths (e.g., WiFi and cellular) to improve the performance and reliability of network transmission. The scheduler of the multipath transmission protocol determines the path to which each data packet should be transmitted, and is a key module that affects multipath transmission. However, current multipath schedulers cannot adapt well to various user usage scenarios. In this paper, we propose DRLMS, a deep reinforcement learning based multipath scheduler. DRLMS uses deep reinforcement learning to train neural networks to generate packet scheduling policies. It optimizes the scheduling strategy through feedback to the neural network through the reward function based on the current user usage scenario and QoS. We implement DRLMS in the MPQUIC protocol and compared it with current multipath schedulers. The results show that DRLMS's adaptability to user usage scenarios is significantly outperforms other schedulers.

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