Cooperative Multi-UAV Positioning for Aerial Internet Service Management: A Multi-Agent Deep Reinforcement Learning Approach-Reference-Cited by-同舟云学术

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Cooperative Multi-UAV Positioning for Aerial Internet Service Management: A Multi-Agent Deep Reinforcement Learning Approach

Published:2024-08 Issue:4 Volume:21 Page:3797-3812
ISSN:1932-4537
Container-title:IEEE Transactions on Network and Service Management
language:
Short-container-title:IEEE Trans. Netw. Serv. Manage.

Author:

Kim Joongheon¹^ORCID,Park Soohyun²^ORCID,Jung Soyi³^ORCID,Cordeiro Carlos⁴^ORCID

Affiliation:

1. School of Electrical Engineering, Korea University, Seoul, South Korea

2. Division of Computer Science, Sookmyung Women’s University, Seoul, South Korea

3. Department of Electrical of Computer Engineering, Ajou University, Suwon, South Korea

4. Client Computing Group, Intel Corporation, Hillsboro, OR, USA

Funder

Institute of Information & Communications Technology Planning & Evaluation

Korea government (MSIT (Ministry of Science and ICT

Development of 3D Spatial Mobile Communication Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4275028/10643497/10506797.pdf?arnumber=10506797

Reference66 articles.

1. Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility

2. Swarm of UAVs for Network Management in 6G: A Technical Review

3. Quantum Multiagent Actor–Critic Networks for Cooperative Mobile Access in Multi-UAV Systems

4. Blockchain-Based Resource Trading in Multi-UAV-Assisted Industrial IoT Networks: A Multi-Agent DRL Approach

5. Auction-Based Charging Scheduling With Deep Learning Framework for Multi-Drone Networks

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