Maintaining Links in the Highly Dynamic FANET Using Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Maintaining Links in the Highly Dynamic FANET Using Deep Reinforcement Learning

Published:2023-03 Issue:3 Volume:72 Page:2804-2818
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Qiu Xiulin¹^ORCID,Yang Yuwang¹^ORCID,Xu Lei¹^ORCID,Yin Jun²^ORCID,Liao Zhenqiang³

Affiliation:

1. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

2. Jiangsu Key Laboratory for Broadband Wireless Communication and Internet of Things, Nanjing University of Post and Telecommunications, Nanjing, China

3. School of Electrical and Mechanical Engineering, Suzhou Global Institute of Software Technology, Suzhou, China

Funder

NDF

JSKS&T

SZKITI

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Aerospace Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/25/10070114/09932013.pdf?arnumber=9932013

Reference35 articles.

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4. Location-Aided Delay Tolerant Routing Protocol in UAV Networks for Post-Disaster Operation

5. Human-level control through deep reinforcement learning

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