Multicasting Over 6G Non-Terrestrial Networks: A Softwarization-Based Approach

Gonzalez, Claudia Carballo; Pizzi, Sara; Murroni, Maurizio; Araniti, Giuseppe

doi:10.1109/mvt.2022.3232919

Cited by 24 publications

(9 citation statements)

References 11 publications

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“…The first phases of the 5G standardization (Rel-15 and 16) focus on the solo unicast capability [2]. Then, Rel-16 added the Terrestrial (T)-Broadcast targeting Enhanced Television (EN-TV), and after that, in Rel-17 [9], the 3GPP starts standardizing the novel MBS paradigm for the 5G system architecture.…”

Section: Multicast/broadcast Services: Potentialities and Challengesmentioning

confidence: 99%

“…Current Rel-18 marks the start of the 5G-Advanced era. It aims to enhance the efficiency in multicast/broadcast resource utilization over heterogeneous networks and sharing scenarios supporting the multicast reception in inactive RRC state [2]. The envisaged New Radio (NR) MBS must include KPIs to reduce computational complexity and increase energy efficiency with ML solutions based on decentralized and softwarized network elements.…”

Section: Multicast/broadcast Services: Potentialities and Challengesmentioning

confidence: 99%

“…• The slicing paradigm adds flexibility, prioritization, and isolation by creating several logical network slices (NSs) utilizing a shared physical infrastructure. Based on the application type, the users' distribution, and network conditions, a shared content flow must be dynamically mapped into unicast/multicast/broadcast slice instances, exploiting network and radio resources economically and efficiently [2], [13].…”

Section: A Enabling Mbs Technologiesmentioning

confidence: 99%

“…Consequently, the multicast RRM solutions must effectively cope with the tradeoff between optimal network performance and complexity [5]. This becomes essential because the upcoming evolution will be tied to the 3D ultra-dense HetNet [2] , differentiated advanced applications and tight requirements, making managing and exploiting network resources even more complex.…”

Section: Rrm Challenge Over Mbsmentioning

confidence: 99%

“…Addressing the challenging research verticals shaping the 6G context will require a compelling mix of enabling radio access technologies (RAT), innovative technical solutions, and native softwarized/intelligent conceptions. According to [2], the native Multicast/Broadcast Services (MBS) support will be crucial, bringing point-to-multipoint (PTM) delivery mechanisms for efficient resource utilization, load balancing, reliability, overhead, and delay reduction. Nevertheless, optimized Radio Resource Management (RRM) solutions are required to offer a cost-effective PTM service with considerable capacity and quality of service (QoS) gain.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Machine Learning-based Multicasting Radio Resource Management over 6G O-RAN Framework

Fontes Pupo,

Carballo Gonzalez,

Iradier

et al. 2023

Preprint

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<p> 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)–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. </p>

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Section: Multicast/broadcast Services: Potentialities and Challengesmentioning

confidence: 99%

Section: Multicast/broadcast Services: Potentialities and Challengesmentioning

confidence: 99%

Section: A Enabling Mbs Technologiesmentioning

confidence: 99%

Section: Rrm Challenge Over Mbsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning-based Multicasting Radio Resource Management over 6G O-RAN Framework

Fontes Pupo,

Carballo Gonzalez,

Iradier

et al. 2023

Preprint

View full text Add to dashboard Cite

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On Use of LEO-GEO Hybrid Model for Optimized Data Traffic Performance

Ali,

Abouelatta,

Youssef

2024

Wireless Pers Commun

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Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey

Iqbal,

Tham,

Wong

et al. 2023

IEEE Access

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6G networks can support global, ubiquitous and seamless connectivity through the convergence of terrestrial and non-terrestrial networks (NTNs). Unlike terrestrial scenarios, NTNs pose unique challenges including propagation characteristics, latency and mobility, owing to the operations in spaceborne and airborne platforms. To overcome all these technical hurdles, this survey paper presents the use of artificial intelligence (AI) techniques in learning and adapting to the complex NTN environments. We begin by providing an overview of NTNs in the context of 6G, highlighting the potential security and privacy issues. Next, we review the existing AI methods adopted for 6G NTN optimization, starting from machine learning (ML), through deep learning (DL) to deep reinforcement learning (DRL). All these AI techniques have paved the way towards more intelligent network planning, resource allocation (RA), and interference management. Furthermore, we discuss the challenges and opportunities in AI-powered NTN for 6G networks. Finally, we conclude by providing insights and recommendations on the key enabling technologies for future AI-powered 6G NTNs.

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Multicasting Over 6G Non-Terrestrial Networks: A Softwarization-Based Approach

Cited by 24 publications

References 11 publications

Machine Learning-based Multicasting Radio Resource Management over 6G O-RAN Framework

Machine Learning-based Multicasting Radio Resource Management over 6G O-RAN Framework

On Use of LEO-GEO Hybrid Model for Optimized Data Traffic Performance

Empowering Non-Terrestrial Networks With Artificial Intelligence: A Survey

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