Deep Reinforcement Learning-Based Robust Design for an IRS-Assisted MISO-NOMA System-Reference-Cited by-同舟云学术

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Deep Reinforcement Learning-Based Robust Design for an IRS-Assisted MISO-NOMA System

Published:2024 Issue: Volume:2 Page:424-441
ISSN:2831-316X
Container-title:IEEE Transactions on Machine Learning in Communications and Networking
language:
Short-container-title:Trans. Mach. Learn. Comm. Netw.

Author:

Waraiet Abdulhamed¹^ORCID,Cumanan Kanapathippillai¹^ORCID,Ding Zhiguo²^ORCID,Dobre Octavia A.³^ORCID

Affiliation:

1. School of Physics, Engineering and Technology, University of York, York, U.K

2. Department of Computer and Communication Engineering, Khalifa University, Abu Dhabi, United Arab Emirates

3. Faculty of Engineering and Applied Science, Memorial University, St. John’s, NL, Canada

Funder

U.K. Engineering and Physical Sciences Research Council

Canada Research Chairs

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9882533/10356147/10494408.pdf?arnumber=10494408

Reference51 articles.

1. Nonorthogonal Multiple Access for 5G and Beyond

2. Non-Orthogonal Multiple Access (NOMA) for Cellular Future Radio Access

3. On the Ergodic Capacity of MIMO NOMA Systems

4. The Application of MIMO to Non-Orthogonal Multiple Access

5. Beamforming Techniques for Nonorthogonal Multiple Access in 5G Cellular Networks

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