On Learning Generalized Wireless MAC Communication Protocols via a Feasible Multi-Agent Reinforcement Learning Framework-Reference-Cited by-同舟云学术

On Learning Generalized Wireless MAC Communication Protocols via a Feasible Multi-Agent Reinforcement Learning Framework

Published:2024 Issue: Volume:2 Page:298-317
ISSN:2831-316X
Container-title:IEEE Transactions on Machine Learning in Communications and Networking
language:
Short-container-title:Trans. Mach. Learn. Comm. Netw.

Author:

Miuccio Luciano¹^ORCID,Riolo Salvatore¹^ORCID,Samarakoon Sumudu²^ORCID,Bennis Mehdi²^ORCID,Panno Daniela¹^ORCID

Affiliation:

1. Department of Electrical, Electronics and Computer Engineering (DIEEI), University of Catania, Catania, Italy

2. Centre for Wireless Communications, University of Oulu, Oulu, Finland

Funder

European Union through the Italian National Recovery and Resilience Plan (NRRP) of NextGenerationEU, partnership on “Telecommunications of the Future”

“RESTART.”

Italian Ministero dell’Università e della Ricerca (MUR), Azioni IV.4 “Dottorati e contratti di ricerca su tematiche dell’innovazione,”

Research Council of Finland (former Academy of Finland) 6G Flagship Programme

European Union through the projects CENTRIC

6G-INTENSE

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9882533/10356147/10440615.pdf?arnumber=10440615

Reference38 articles.

1. Toward a 6G AI-Native Air Interface

2. The Roadmap to 6G: AI Empowered Wireless Networks

3. A Wasserstein GAN Autoencoder for SCMA Networks

4. 6G Wireless Networks: Vision, Requirements, Applications and Challenges

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