Reliable Water-Air Direct Wireless Communication: Kalman Filter-Assisted Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

Reliable Water-Air Direct Wireless Communication: Kalman Filter-Assisted Deep Reinforcement Learning Approach

Published:2022-09-26 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 47th Conference on Local Computer Networks (LCN)
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Author:

Wang Jinglong¹,Luo Hanjiang¹,Ruby Rukhsana²,Liu Jiangang¹,Guo Kai¹,Wu Kaishun²

Affiliation:

1. Shandong University of Science and Technology,College of Computer Science and Engineering,Qingdao,China

2. Shenzhen University,College of Computer Science and Software Engineering,Shenzhen,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9843189/9843201/09843503.pdf?arnumber=9843503

Reference11 articles.

1. Amphilight: Direct air-water communication with laser light;carver;17th USENIX Symposium on Networked Systems Design and Implementation (NSDI20),2020

2. Software-Defined Visible Light Networking for Bi-Directional Wireless Communication Across the Air-Water Interface

3. Deep Reinforcement Learning Multi-UAV Trajectory Control for Target Tracking

4. Autonomous Tracking Using a Swarm of UAVs: A Constrained Multi-Agent Reinforcement Learning Approach

5. Analysis of wavy surface effects on the characteristics of wireless optical communication downlinks

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