Multi-Task Deep Reinforcement Learning for Terahertz NOMA Resource Allocation With Hybrid Discrete and Continuous Actions-Reference-Cited by-同舟云学术

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Multi-Task Deep Reinforcement Learning for Terahertz NOMA Resource Allocation With Hybrid Discrete and Continuous Actions

Published:2024-08 Issue:8 Volume:73 Page:11647-11663
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Hu Zhifeng¹^ORCID,Han Chong¹^ORCID,Deng Yansha²^ORCID,Wang Xudong³^ORCID

Affiliation:

1. Terahertz Wireless Communications (TWC) Laboratory, Shanghai Jiao Tong University, Shanghai, China

2. Department of Engineering, King's College London, London, U.K.

3. University of Michigan-Shanghai Jiao Tong University (UM-SJTU) Joint Institute, Shanghai Jiao Tong University, Shanghai, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/25/10637911/10480465.pdf?arnumber=10480465

Reference58 articles.

1. A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems

2. Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends

3. Application of Non-Orthogonal Multiple Access in LTE and 5G Networks

4. A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends

5. Partial Non-Orthogonal Multiple Access (NOMA) in Downlink Poisson Networks

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1. Full-Duplex Communication in NOMA Energy Harvesting Networks: Performance Assessment;2023 4th International Conference on Intelligent Technologies (CONIT);2024-06-21

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