Multi-Agent Reinforcement Learning for Dynamic Topology Optimization of Mesh Wireless Networks-Reference-Cited by-同舟云学术

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Multi-Agent Reinforcement Learning for Dynamic Topology Optimization of Mesh Wireless Networks

Published:2024-09 Issue:9 Volume:23 Page:10501-10513
ISSN:1536-1276
Container-title:IEEE Transactions on Wireless Communications
language:
Short-container-title:IEEE Trans. Wireless Commun.

Author:

Sun Wei¹^ORCID,Lv Qiushuo¹^ORCID,Xiao Yang²^ORCID,Liu Zhi³^ORCID,Tang Qingwei¹,Li Qiyue¹^ORCID,Mu Daoming¹

Affiliation:

1. School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, Anhui, China

2. Department of Computer Science, The University of Alabama, Tuscaloosa, AL, USA

3. Department of Computer and Network Engineering, The University of Electro-Communications, Tokyo, Japan

Funder

National Natural Science Foundation of China

Natural Science Foundation of Anhui Province

111 Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7693/10678775/10466475.pdf?arnumber=10466475

Reference30 articles.

1. Wireless-Powered Sensor Networks: How to Realize

2. Two-Stage Semi-Distributed Resource Management for Device-to-Device Communication in Cellular Networks

3. Point Cloud Video Streaming: Challenges and Solutions

4. End-to-End Data Delivery Reliability Model for Estimating and Optimizing the Link Quality of Industrial WSNs

5. Wireless Multi-hop Ad-hoc Networks: A Review

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