Dynamic Spectrum Sharing Based on Federated Learning and Multi-Agent Actor-Critic Reinforcement Learning-Reference-Cited by-同舟云学术

Dynamic Spectrum Sharing Based on Federated Learning and Multi-Agent Actor-Critic Reinforcement Learning

Published:2023-06-19 Issue: Volume: Page:
ISSN:
Container-title:2023 International Wireless Communications and Mobile Computing (IWCMC)
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Author:

Yang Tongtong¹,Zhang Wensheng¹,Bo Yulian¹,Sun Jian¹,Wang Cheng-Xiang²

Affiliation:

1. Shandong Provincial Key Lab of Wireless Communication Technologies, Shandong University,School of Information Science and Engineering,Qingdao,China,266237

2. Southeast University,National Mobile Communications Research Laboratory, School of Information Science and Engineering,Nanjing,China,210096

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10182387/10182368/10182572.pdf?arnumber=10182572

Reference15 articles.

1. Dynamic Spectrum Aggregation and Access Scheme Based on Multi-Agent Actor-Critic Reinforcement Learning

2. Dynamic Spectrum Sharing and Aggregation Scheme Based on Deep Reinforcement Learning

3. Enhancing WiFi Multiple Access Performance with Federated Deep Reinforcement Learning

4. Federated Learning over Wireless Networks: Optimization Model Design and Analysis

5. A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access

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