Dual-Attention Deep Reinforcement Learning for Multi-MAP 3D Trajectory Optimization in Dynamic 5G Networks-Reference-Cited by-同舟云学术

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Dual-Attention Deep Reinforcement Learning for Multi-MAP 3D Trajectory Optimization in Dynamic 5G Networks

Published:2023-05-28 Issue: Volume: Page:
ISSN:
Container-title:ICC 2023 - IEEE International Conference on Communications
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Author:

Catté Esteban¹,Sana Mohamed¹,Maman Mickael¹

Affiliation:

1. Universite Grenoble Alpes,CEA-Leti,Grenoble,France,F-38000

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10278505/10278554/10278657.pdf?arnumber=10278657

Reference19 articles.

1. Distributed UAV-BSs Trajectory Optimization for User-Level Fair Communication Service With Multi-Agent Deep Reinforcement Learning

2. Multi-Agent Deep Reinforcement Learning for Trajectory Design and Power Allocation in Multi-UAV Networks

3. Tarmac: Targeted multi-agent communication;das;CoRR,2018

4. Resource Allocation in UAV-Assisted Networks: A Clustering-Aided Reinforcement Learning Approach

5. 3D UAV Trajectory Design and Frequency Band Allocation for Energy-Efficient and Fair Communication: A Deep Reinforcement Learning Approach

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Federated Multi-Agent Deep Reinforcement Learning for Dynamic and Flexible 3D Operation of 5G Multi-MAP Networks;2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC);2023-09-05

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