Energy-Efficient Rate-Splitting Multiple Access: A Deep Reinforcement Learning-Based Framework-Reference-Cited by-同舟云学术

Energy-Efficient Rate-Splitting Multiple Access: A Deep Reinforcement Learning-Based Framework

Published:2023 Issue: Volume:4 Page:2397-2409
ISSN:2644-125X
Container-title:IEEE Open Journal of the Communications Society
language:
Short-container-title:IEEE Open J. Commun. Soc.

Author:

Diamanti Maria¹^ORCID,Kapsalis Georgios¹^ORCID,Tsiropoulou Eirini Eleni²^ORCID,Papavassiliou Symeon¹^ORCID

Affiliation:

1. Institute of Communication and Computer Systems, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, Greece

2. Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA

Funder

European Commission through the Horizon Europe/JU SNS Project Hexa-X-II

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/8782661/10008219/10273617.pdf?arnumber=10273617

Reference27 articles.

1. Energy Efficiency in C-RAN using Rate Splitting and Common Message Decoding

2. Globally Optimal Spectrum- and Energy-Efficient Beamforming for Rate Splitting Multiple Access

3. Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks

4. Energy Efficient Rate Splitting Multiple Access (RSMA) with Reconfigurable Intelligent Surface

5. Energy Efficiency of Rate-Splitting Multiple Access, and Performance Benefits over SDMA and NOMA

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