Enabling Reliable Water–Air Direct Optical Wireless Communication for Uncrewed Vehicular Networks: A Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

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Enabling Reliable Water–Air Direct Optical Wireless Communication for Uncrewed Vehicular Networks: A Deep Reinforcement Learning Approach

Published:2024-08 Issue:8 Volume:73 Page:11470-11486
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Wang Jinglong¹^ORCID,Luo Hanjiang¹^ORCID,Ruby Rukhsana²^ORCID,Liu Jiangang¹^ORCID,Wang Shengli³^ORCID,Wu Kaishun⁴^ORCID

Affiliation:

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

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

3. College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao, China

4. Hong Kong University of Science and Technology Guangzhou, Guangzhou, China

Funder

National Natural Science Foundation of China

Shandong Provincial Natural Science Foundation, China

Natural Science Foundation of Shandong Province for Innovation and Development Joint Funds, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/25/10637911/10468635.pdf?arnumber=10468635

Reference36 articles.

1. 6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies

2. Point-to-Point Communication in Integrated Satellite-Aerial 6G Networks: State-of-the-Art and Future Challenges

3. Recent Progress of Air/Water Cross-Boundary Communications for Underwater Sensor Networks: A Review

4. Autonomous Reinforcement Control of Visual Underwater Vehicles: Real-Time Experiments Using Computer Vision

5. 3GPP release - 17 building blocks for UAV application,2022

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