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Emerging Technologies for 6G Communication Networks: Machine Learning Approaches

The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultr...

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Autores principales: Puspitasari, Annisa Anggun, An, To Truong, Alsharif, Mohammed H., Lee, Byung Moo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534410/
https://www.ncbi.nlm.nih.gov/pubmed/37765765
http://dx.doi.org/10.3390/s23187709
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author Puspitasari, Annisa Anggun
An, To Truong
Alsharif, Mohammed H.
Lee, Byung Moo
author_facet Puspitasari, Annisa Anggun
An, To Truong
Alsharif, Mohammed H.
Lee, Byung Moo
author_sort Puspitasari, Annisa Anggun
collection PubMed
description The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.
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spelling pubmed-105344102023-09-29 Emerging Technologies for 6G Communication Networks: Machine Learning Approaches Puspitasari, Annisa Anggun An, To Truong Alsharif, Mohammed H. Lee, Byung Moo Sensors (Basel) Review The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks. MDPI 2023-09-06 /pmc/articles/PMC10534410/ /pubmed/37765765 http://dx.doi.org/10.3390/s23187709 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Puspitasari, Annisa Anggun
An, To Truong
Alsharif, Mohammed H.
Lee, Byung Moo
Emerging Technologies for 6G Communication Networks: Machine Learning Approaches
title Emerging Technologies for 6G Communication Networks: Machine Learning Approaches
title_full Emerging Technologies for 6G Communication Networks: Machine Learning Approaches
title_fullStr Emerging Technologies for 6G Communication Networks: Machine Learning Approaches
title_full_unstemmed Emerging Technologies for 6G Communication Networks: Machine Learning Approaches
title_short Emerging Technologies for 6G Communication Networks: Machine Learning Approaches
title_sort emerging technologies for 6g communication networks: machine learning approaches
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10534410/
https://www.ncbi.nlm.nih.gov/pubmed/37765765
http://dx.doi.org/10.3390/s23187709
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