A Deep Reinforcement Learning based Approach for NOMA-based Random Access Network with Truncated Channel Inversion Power Control-Reference-Cited by-同舟云学术

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A Deep Reinforcement Learning based Approach for NOMA-based Random Access Network with Truncated Channel Inversion Power Control

Published:2022-05-16 Issue: Volume: Page:
ISSN:
Container-title:ICC 2022 - IEEE International Conference on Communications
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Author:

Chen Ziru¹,Zhang Ran²,Cai Lin X.¹,Cheng Yu¹,Liu Yong³

Affiliation:

1. Illinois Institute of Technology,Department of Electrical and Computer Engineering,Chicago,USA

2. Miami University,College of Engineering and Computing,Oxford,OH,USA

3. South China Normal University,Department of Physics and Telecommunication Engineering,Guangzhou,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9837954/9838246/09839030.pdf?arnumber=9839030

Reference11 articles.

1. Optimizing Non-Orthogonal Multiple Access in Random Access Networks

2. Analysis of RF Energy Harvesting in Uplink-NOMA IoT-Based Network

3. Data-driven random access optimization in multi-cell iot networks with noma;khairy,2021

4. Performance Study of Random Access NOMA with Truncated Channel Inversion Power Control

5. A Deep Reinforcement learning based Approach for Channel Aggregation in IEEE 802.11 ax

Cited by 3 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. A Survey on NB-IoT Random Access: Approaches for Uplink Radio Access Network Congestion Management;IEEE Access;2024

2. Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach;2023 IEEE Virtual Conference on Communications (VCC);2023-11-28

3. Energy-Efficient Multiple Access Scheme with Power Control for mMTC Networks;2023 IEEE 23rd International Conference on Communication Technology (ICCT);2023-10-20

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