Artificial Intelligence Aided Low Complexity RRM Algorithms for 5G-MBS
For the upcoming 5G-Advanced, the multicast/broadcast services (5G-MBS) capability is one of the most appealing use cases. The effective integration of point-to-multipoint communication will address the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-laten...
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| Veröffentlicht in: | IEEE transactions on broadcasting Jg. 70; H. 1; S. 1 - 0 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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IEEE
01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9316, 1557-9611 |
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| Abstract | For the upcoming 5G-Advanced, the multicast/broadcast services (5G-MBS) capability is one of the most appealing use cases. The effective integration of point-to-multipoint communication will address the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-latency applications. This paper proposes novel approaches for the dynamic access technique selection and resource allocation for multicast groups (MGs) subject to the 5G-MBS paradigm. Our proposal is oriented to address and contextualize the complexity associated with multicast radio resource management (RRM) and the implications of fast variations in the reception conditions of the MG members. We propose a solution structured by a multicast-oriented trigger to avoid overrunning the algorithm, a K-means clustering for group-oriented detection and splitting, a classifier for selecting the most suitable multicast access technique, and a final resource allocation algorithm. To choose the multicast access technique that better fits the specific reception conditions of the users, we evaluate heuristic strategies and machine learning (ML) multiclass classification solutions. We consider the conventional multicast scheme (MCS) and subgrouping based on orthogonal/non-orthogonal multiplex access (OMA/NOMA) as access techniques. We assess the effectiveness of our solution in terms of the quality of service (QoS) parameters and complexity. The proposed technical solution is validated through extensive simulation for a single-cell 5G-MBS use case in the microwave <inline-formula> <tex-math notation="LaTeX">(\mu</tex-math> </inline-formula>Wave) and millimeter wave (mmWave) band with different mobility behaviors. |
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| AbstractList | For the upcoming 5G-Advanced, the multicast/broadcast services (5G-MBS) capability is one of the most appealing use cases. The effective integration of point-to-multipoint communication will address the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-latency applications. This paper proposes novel approaches for the dynamic access technique selection and resource allocation for multicast groups (MGs) subject to the 5G-MBS paradigm. Our proposal is oriented to address and contextualize the complexity associated with multicast radio resource management (RRM) and the implications of fast variations in the reception conditions of the MG members. We propose a solution structured by a multicast-oriented trigger to avoid overrunning the algorithm, a K-means clustering for group-oriented detection and splitting, a classifier for selecting the most suitable multicast access technique, and a final resource allocation algorithm. To choose the multicast access technique that better fits the specific reception conditions of the users, we evaluate heuristic strategies and machine learning (ML) multiclass classification solutions. We consider the conventional multicast scheme (MCS) and subgrouping based on orthogonal/non-orthogonal multiplex access (OMA/NOMA) as access techniques. We assess the effectiveness of our solution in terms of the quality of service (QoS) parameters and complexity. The proposed technical solution is validated through extensive simulation for a single-cell 5G-MBS use case in the microwave [Formula Omitted]Wave) and millimeter wave (mmWave) band with different mobility behaviors. For the upcoming 5G-Advanced, the multicast/broadcast services (5G-MBS) capability is one of the most appealing use cases. The effective integration of point-to-multipoint communication will address the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-latency applications. This paper proposes novel approaches for the dynamic access technique selection and resource allocation for multicast groups (MGs) subject to the 5G-MBS paradigm. Our proposal is oriented to address and contextualize the complexity associated with multicast radio resource management (RRM) and the implications of fast variations in the reception conditions of the MG members. We propose a solution structured by a multicast-oriented trigger to avoid overrunning the algorithm, a K-means clustering for group-oriented detection and splitting, a classifier for selecting the most suitable multicast access technique, and a final resource allocation algorithm. To choose the multicast access technique that better fits the specific reception conditions of the users, we evaluate heuristic strategies and machine learning (ML) multiclass classification solutions. We consider the conventional multicast scheme (MCS) and subgrouping based on orthogonal/non-orthogonal multiplex access (OMA/NOMA) as access techniques. We assess the effectiveness of our solution in terms of the quality of service (QoS) parameters and complexity. The proposed technical solution is validated through extensive simulation for a single-cell 5G-MBS use case in the microwave <inline-formula> <tex-math notation="LaTeX">(\mu</tex-math> </inline-formula>Wave) and millimeter wave (mmWave) band with different mobility behaviors. |
| Author | Angueira, Pablo Gonzalez, Claudia Carballo Pupo, Ernesto Fontes Iradier, Eneko Murroni, Maurizio Montalban, Jon |
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| SubjectTerms | 5G mobile communication 5G-MBS Algorithms Artificial intelligence Cluster analysis Clustering Complexity computational complexity Machine learning Millimeter wave communication Millimeter waves mmWave multicast access techniques Multicast algorithms Multicasting Multimedia NOMA Proposals Quality of service Resource allocation Resource management Vector quantization |
| Title | Artificial Intelligence Aided Low Complexity RRM Algorithms for 5G-MBS |
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