Mutual-Interference-Aware Throughput Enhancement in Massive IoT: A Graph Reinforcement Learning Framework-Reference-Cited by-同舟云学术

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Mutual-Interference-Aware Throughput Enhancement in Massive IoT: A Graph Reinforcement Learning Framework

Published:2024-09-15 Issue:18 Volume:11 Page:30341-30353
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Yang Fan¹^ORCID,Yang Cheng¹^ORCID,Huang Jie¹^ORCID,Alfarraj Osama²^ORCID,Tolba Amr²^ORCID,Yu Keping³^ORCID,Guizani Mohsen⁴^ORCID

Affiliation:

1. School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China

2. Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia

3. Graduate School of Science and Engineering, Hosei University, Tokyo, Japan

4. Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

Funder

National Natural Science Foundation of China

Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia

Science and Technology Research Program of Chongqing Education Commission of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6488907/10660568/10596107.pdf?arnumber=10596107

Reference50 articles.

1. Enabling Massive IoT Toward 6G: A Comprehensive Survey

2. 6G Integrated Sensing and Communications Channel Modeling: Challenges and Opportunities

3. Adaptive Modulation Based on Nondata-Aided Error Vector Magnitude for Smart Systems in Smart Cities

4. Hypergraph-Based Interference Avoidance Resource Management in Customer-Centric Communication for Intelligent Cyber-physical Transportation Systems

5. DRL-Based Computation Rate Maximization for Wireless Powered Multi-AP Edge Computing

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