Multi-Agent Reinforcement Learning for Wireless User Scheduling: Performance, Scalablility, and Generalization-Reference-Cited by-同舟云学术

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Multi-Agent Reinforcement Learning for Wireless User Scheduling: Performance, Scalablility, and Generalization

Published:2022-10-31 Issue: Volume: Page:
ISSN:
Container-title:2022 56th Asilomar Conference on Signals, Systems, and Computers
language:
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Author:

Yang Kun¹,Li Donghao²,Shen Cong¹,Yang Jing²,Yeh Shu-ping³,Sydir Jerry³

Affiliation:

1. University of Virginia,Department of Electrical and Computer Engineering,USA

2. the Pennsylvania State University,Department of Electrical Engineering,USA

3. Intel Corporation,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10051833/10051818/10051992.pdf?arnumber=10051992

Reference17 articles.

1. Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning

2. Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks

3. Coordinated Multi-Point in Mobile Communications

4. A Novel User Selection Massive MIMO Scheduling Algorithm via Real Time DDPG

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. Distributed MARL for Scheduling in Conflict Graphs;2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton);2023-09-26

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