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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/18/7709 |
| _version_ | 1797577104777281536 |
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| author | Annisa Anggun Puspitasari To Truong An Mohammed H. Alsharif Byung Moo Lee |
| author_facet | Annisa Anggun Puspitasari To Truong An Mohammed H. Alsharif Byung Moo Lee |
| author_sort | Annisa Anggun Puspitasari |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T22:03:18Z |
| format | Article |
| id | doaj.art-5d11f89e29374f2891d1ca48d504728b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T22:03:18Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5d11f89e29374f2891d1ca48d504728b2023-11-19T12:53:06ZengMDPI AGSensors1424-82202023-09-012318770910.3390/s23187709Emerging Technologies for 6G Communication Networks: Machine Learning ApproachesAnnisa Anggun Puspitasari0To Truong An1Mohammed H. Alsharif2Byung Moo Lee3Department of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of KoreaDepartment of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of KoreaDepartment of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul 05006, Republic of KoreaDepartment of Intelligent Mechatronics Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of KoreaThe 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.https://www.mdpi.com/1424-8220/23/18/7709deep learning (DL)emerging technologiesmachine learning (ML)reinforcement learning (RL)sixth generation (6G) communication6G visions and requirements |
| spellingShingle | Annisa Anggun Puspitasari To Truong An Mohammed H. Alsharif Byung Moo Lee Emerging Technologies for 6G Communication Networks: Machine Learning Approaches Sensors deep learning (DL) emerging technologies machine learning (ML) reinforcement learning (RL) sixth generation (6G) communication 6G visions and requirements |
| 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 | deep learning (DL) emerging technologies machine learning (ML) reinforcement learning (RL) sixth generation (6G) communication 6G visions and requirements |
| url | https://www.mdpi.com/1424-8220/23/18/7709 |
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