A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach
The rapid evolution of wireless communication has affected unmanned aerial vehicles (UAV), which are expected to be used in diverse applications in smart cities, military operations, and cellular networks. To address the significant impacts of rapid wireless communication advancements, along with th...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10494323/ |
| _version_ | 1797206126375206912 |
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| author | Meyra Chusna Mayarakaca Byung Moo Lee |
| author_facet | Meyra Chusna Mayarakaca Byung Moo Lee |
| author_sort | Meyra Chusna Mayarakaca |
| collection | DOAJ |
| description | The rapid evolution of wireless communication has affected unmanned aerial vehicles (UAV), which are expected to be used in diverse applications in smart cities, military operations, and cellular networks. To address the significant impacts of rapid wireless communication advancements, along with the escalating demand for user equipment (UE), multiple access technique approaches, such as non-orthogonal multiple access (NOMA), have been proposed. NOMA has the key distinguishing feature of supporting more UE, particularly UAV-enabled communication networks. Moreover, the successful implementation of such enhancements relies on the acquisition of high-quality predictions. These predictions, driven by in-depth insights derived from data, are facilitated by machine learning (ML). The integration of ML further enhances UAV capabilities to pave the way for optimized wireless communication. In this paper, we present a survey on the potential of NOMA techniques applied to UAVs using ML methods to enhance UAVs in wireless communication networks. Specifically, a basic overview of UAV and NOMA will first introduced. The role of NOMA in UAV networks is then divided into two categories: the principles and application of NOMA in UAV networks. Finally, implement ML on NOMA for UAVs by representing the diverse applications of ML systems. In addition, we highlight several open research problems as possible directions for future research. |
| first_indexed | 2024-04-24T09:02:03Z |
| format | Article |
| id | doaj.art-a4e739cd74aa4b46b7a33e0e1e7ba5ca |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T09:02:03Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a4e739cd74aa4b46b7a33e0e1e7ba5ca2024-04-15T23:00:45ZengIEEEIEEE Access2169-35362024-01-0112511385116510.1109/ACCESS.2024.338586010494323A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning ApproachMeyra Chusna Mayarakaca0Byung Moo Lee1https://orcid.org/0000-0003-3675-929XDepartment of Intelligent Mechatronics Engineering, Sejong University, Seoul, South KoreaDepartment of Intelligent Mechatronics Engineering, Sejong University, Seoul, South KoreaThe rapid evolution of wireless communication has affected unmanned aerial vehicles (UAV), which are expected to be used in diverse applications in smart cities, military operations, and cellular networks. To address the significant impacts of rapid wireless communication advancements, along with the escalating demand for user equipment (UE), multiple access technique approaches, such as non-orthogonal multiple access (NOMA), have been proposed. NOMA has the key distinguishing feature of supporting more UE, particularly UAV-enabled communication networks. Moreover, the successful implementation of such enhancements relies on the acquisition of high-quality predictions. These predictions, driven by in-depth insights derived from data, are facilitated by machine learning (ML). The integration of ML further enhances UAV capabilities to pave the way for optimized wireless communication. In this paper, we present a survey on the potential of NOMA techniques applied to UAVs using ML methods to enhance UAVs in wireless communication networks. Specifically, a basic overview of UAV and NOMA will first introduced. The role of NOMA in UAV networks is then divided into two categories: the principles and application of NOMA in UAV networks. Finally, implement ML on NOMA for UAVs by representing the diverse applications of ML systems. In addition, we highlight several open research problems as possible directions for future research.https://ieeexplore.ieee.org/document/10494323/Aerial networksmachine learning (ML)non-orthogonal multiple access (NOMA)unmanned aerial vehicles (UAV) |
| spellingShingle | Meyra Chusna Mayarakaca Byung Moo Lee A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach IEEE Access Aerial networks machine learning (ML) non-orthogonal multiple access (NOMA) unmanned aerial vehicles (UAV) |
| title | A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach |
| title_full | A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach |
| title_fullStr | A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach |
| title_full_unstemmed | A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach |
| title_short | A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach |
| title_sort | survey on non orthogonal multiple access for unmanned aerial vehicle networks machine learning approach |
| topic | Aerial networks machine learning (ML) non-orthogonal multiple access (NOMA) unmanned aerial vehicles (UAV) |
| url | https://ieeexplore.ieee.org/document/10494323/ |
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