ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey-Reference-Cited by-同舟云学术

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

Published:2022 Issue: Volume:10 Page:83507-83528
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Bartsiokas Ioannis A.¹^ORCID,Gkonis Panagiotis K.²^ORCID,Kaklamani Dimitra I.¹,Venieris Iakovos S.³

Affiliation:

1. Microwave and Fiber Optics Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, Athens, Greece

2. Department of Digital Industry Technologies, National and Kapodistrian University of Athens, Sterea Ellada, Dirfies Messapies, Greece

3. Intelligent Communications and Broadband Networks Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, Athens, Greece

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09851417.pdf?arnumber=9851417

Reference117 articles.

1. Machine Learning Techniques for 5G and Beyond

2. A survey on 5G energy efficiency: Massive MIMO, lean carrier design, sleep modes, and machine learning;lopez-perez;arXiv 2101 11246,2021

3. Power-Efficient Resource Allocation in a Heterogeneous Network With Cellular and D2D Capabilities

4. Smart Multi-RAT Access Based on Multiagent Reinforcement Learning

5. AI for Beyond 5G Networks: A Cyber-Security Defense or Offense Enabler?

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