Multi-Agent Reinforcement Learning Based Channel Access Scheme for Underwater Optical Wireless Communication Networks

被引:3
作者
Zhang, Zenghui [1 ]
Zhang, Lin [1 ,2 ]
Chen, Zuwei [1 ]
机构
[1] Sun Yat Sen Univ, Sch Elect & Informat Technol, Guangzhou 510006, Peoples R China
[2] Tibet Univ, Sch Informat Sci & Technol, Lhasa 850012, Peoples R China
来源
2021 15TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION AND COMMUNICATION TECHNOLOGY (ISMICT) | 2021年
关键词
Capacity; Deep-Q network (DQN); dynamic channel access; multi-agent reinforcement learning; underwater optical wireless communication;
D O I
10.1109/ISMICT51748.2021.9434918
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In this paper, we investigate the channel access problem in underwater optical wireless communication (UOWC) networks, wherein multiple transmitters randomly select receivers to send data. The objective is to find a joint multi-transmitter strategy that maximizes the network throughput while guaranteeing the reliability of the data transmission. Considering that the underwater channel conditions are dynamically changing, we propose a multi-agent Deep-Q network (DQN) algorithm based on the reinforcement learning. In this design, the transmitters, each acting as an agent, simultaneously interact with the communication environment. These agents receive observations and evaluate the reward, then learn to choose a receiver using the gained experiences. Simulation results reveal the effectiveness of the proposed scheme, and demonstrate that the multiple agents can learn to generate appropriate access strategies to improve the total transmission capacity while retaining the reliability performances.
引用
收藏
页码:65 / 69
页数:5
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