Computation Rate Maximization for SCMA-Aided Edge Computing in IoT Networks: A Multi-Agent Reinforcement Learning Approach-Reference-Cited by-同舟云学术

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Computation Rate Maximization for SCMA-Aided Edge Computing in IoT Networks: A Multi-Agent Reinforcement Learning Approach

Published:2024-08 Issue:8 Volume:23 Page:10414-10429
ISSN:1536-1276
Container-title:IEEE Transactions on Wireless Communications
language:
Short-container-title:IEEE Trans. Wireless Commun.

Author:

Liu Pengtao¹^ORCID,An Kang²^ORCID,Lei Jing¹^ORCID,Sun Yifu¹^ORCID,Liu Wei¹^ORCID,Chatzinotas Symeon³^ORCID

Affiliation:

1. College of Electronic Science and Technology, National University of Defense Technology, Changsha, China

2. Sixty-Third Research Institute, National University of Defense Technology, Nanjing, China

3. Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg, Luxembourg

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7693/10634844/10462923.pdf?arnumber=10462923

Reference51 articles.

1. A Deep Reinforcement Learning Scheme for SCMA-Based Edge Computing in IoT Networks

2. A Survey on Mobile Edge Computing: The Communication Perspective

3. Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices

4. DRL-Based Energy-Efficient Resource Allocation Frameworks for Uplink NOMA Systems

5. Sparse code multiple access

Cited by 4 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. Performance Analysis of NOMA-Based VLC System in Different IoT Network Environments;IEEE Internet of Things Journal;2024-09-01

2. Grant-Free SCMA Enhanced Mobile Edge Computing: Protocol Design and Performance Analysis;IEEE Internet of Things Journal;2024-08-01

3. A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions;Technologies;2024-06-03

4. Dynamic Resource Allocation-Enabled Distributed Learning as a Service for Vehicular Networks;2024 Second International Conference on Data Science and Information System (ICDSIS);2024-05-17

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