A Scalable and Fair Power Allocation Scheme Based on Deep Multi-Agent Reinforcement Learning in Underwater Wireless Sensor Networks-Reference-Cited by-同舟云学术

A Scalable and Fair Power Allocation Scheme Based on Deep Multi-Agent Reinforcement Learning in Underwater Wireless Sensor Networks

Published:2021-11-22 Issue: Volume: Page:
ISSN:
Container-title:The 15th International Conference on Underwater Networks & Systems
language:
Short-container-title:

Author:

Zhang Tong¹,Gou Yu¹,Liu Jun²,Yang Tingting³,Song Shanshan¹,Cui Jun-Hong⁴

Affiliation:

1. College of Computer Science and Technology, Jilin University, China

2. School of Electronic and Information Engineering, Beihang University, China and Smart Ocean Technology Co., Ltd, China

3. Navigation College, Dalian Maritime University, China and Peng Cheng Laboratory, China

4. College of Computer Science and Technology, Jilin University, China and Smart Ocean Technology Co., Ltd, China

Funder

National Natural Science Foundation of China

Overseas Top Talents Program of Shenzhen

National Key Basic Research Program

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3491315.3491335

Reference15 articles.

1. DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction

2. Constrained power control

3. Xuemei Li and Li Da Xu . 2020. A review of Internet of Things—resource allocation . IEEE Internet of Things Journal( 2020 ). Xuemei Li and Li Da Xu. 2020. A review of Internet of Things—resource allocation. IEEE Internet of Things Journal(2020).

4. Distributed Energy-Efficient Multi-UAV Navigation for Long-Term Communication Coverage by Deep Reinforcement Learning

5. A Joint Time Synchronization and Localization Design for Mobile Underwater Sensor Networks

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