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Machine Learning-based Multicasting Radio Resource Management over 6G O-RAN Framework
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  • Ernesto Fontes Pupo ,
  • Claudia Carballo Gonzalez ,
  • Eneko Iradier ,
  • Jon Montalban ,
  • Pablo Angueira ,
  • Maurizio Murroni
Ernesto Fontes Pupo
University of Cagliari

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Claudia Carballo Gonzalez
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Eneko Iradier
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Jon Montalban
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Pablo Angueira
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Maurizio Murroni
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 The upcoming 6G will represent a complete paradigm shift for global communications. Addressing the critical research verticals toward the envisioned 2030 will require a compelling mix of enabling radio access technologies (RAT) and native softwarized, disaggregated, and intelligent conceptions such as the Open Radio Access Network (O-RAN) architecture. Integrating the Multicast/Broadcast Services (MBS) capability is an appealing feature to overcome the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-latency applications. This article discusses the insertion of machine learning (ML)â\euro“based multicasting Radio Resource Management (RRM) solutions in the 6G O-RAN. We review the expected evolution of the MBS capability, including enabling technologies and challenges for the IMT-2030 framework. Moreover, we cover essential aspects at the intersection of MBS, ML-based RRM solutions, and the disaggregated O-RAN architecture, identifying possible scenarios as feature extensions of O-RAN. We present the outcomes of a comprehensive MBS use case simulation oriented to validate our approach, highlighting critical remarks and conclusions.Â