RAN Slicing in Multi-CU/DU Architecture for 5G Services

Hirayama, Haruhisa; Tsukamoto, Yu; Nanba, Shinobu; Nishimura, Kosuke

doi:10.1109/vtcfall.2019.8891584

Cited by 12 publications

(8 citation statements)

References 6 publications

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“…Towards an efficient RAN slicing, currently, some of the radio processing functionalities of a next-generation NodeB (gNB) in the NG-RAN are accommodated as VNFs [110], namely the centralized unit (CU) and distributed unit (DU), and some others are distributed as physical network function (PNF), namely the radio unit (RU). The VNFs run on points of presence (PoPs) [111], while the PNFs are implemented on dedicated hardware in the cellular network sites.…”

Section: ) Softwarization and Virtualizationmentioning

confidence: 99%

“…Focusing on the function split in the NG-RAN, the current distribution of the radio processing functions over gNB components is executed statically without taking into consideration the type of RAN slices. When supporting a large amount RAN slices in types of eMBB, URLLC, and mMTC, the one-size-fits-all function splitting architecture in NG-RAN is not efficient in terms of resource allocation and network performance [110]. We strongly believe that a customized and dynamic distribution of the gNB functionalities is needed in order to satisfy the service requirements of the three aforementioned types of RAN slices in future networks.…”

Section: ) Softwarization and Virtualizationmentioning

confidence: 99%

See 1 more Smart Citation

The Road Towards 6G: A Comprehensive Survey

Jiang

Han

Habibi

et al. 2021

IEEE Open J. Commun. Soc.

919

241

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As of today, the fifth generation (5G) mobile communication system has been rolled out in many countries and the number of 5G subscribers already reaches a very large scale. It is time for academia and industry to shift their attention towards the next generation. At this crossroad, an overview of the current state of the art and a vision of future communications are definitely of interest. This article thus aims to provide a comprehensive survey to draw a picture of the sixth generation (6G) system in terms of drivers, use cases, usage scenarios, requirements, key performance indicators (KPIs), architecture, and enabling technologies. First, we attempt to answer the question of "Is there any need for 6G?" by shedding light on its key driving factors, in which we predict the explosive growth of mobile traffic until 2030, and envision potential use cases and usage scenarios. Second, the technical requirements of 6G are discussed and compared with those of 5G with respect to a set of KPIs in a quantitative manner. Third, the state-of-the-art 6G research efforts and activities from representative institutions and countries are summarized, and a tentative roadmap of definition, specification, standardization, and regulation is projected. Then, we identify a dozen of potential technologies and introduce their principles, advantages, challenges, and open research issues. Finally, the conclusions are drawn to paint a picture of "What 6G may look like?". This survey is intended to serve as an enlightening guideline to spur interests and further investigations for subsequent research and development of 6G communications systems.

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Section: ) Softwarization and Virtualizationmentioning

confidence: 99%

Section: ) Softwarization and Virtualizationmentioning

confidence: 99%

The Road Towards 6G: A Comprehensive Survey

Jiang

Han

Habibi

et al. 2021

IEEE Open J. Commun. Soc.

919

241

View full text Add to dashboard Cite

show abstract

“…Towards an efficient RAN slicing, currently, some of the radio processing functionalities of a next-generation NodeB (gNB) in the NG-RAN are accommodated as VNFs [100], namely the centralized unit (CU) and distributed unit (DU), and some others are distributed as physical network function (PNF), namely the radio unit (RU). The VNFs run on points of presence (PoPs) [101], while the PNFs are implemented on dedicated hardware in the cellular network sites.…”

Section: ) Softwarization and Virtualizationmentioning

confidence: 99%

“…Focusing on the function split in the NG-RAN, the current distribution of the radio processing functions over gNB components is executed statically without taking into consideration the type of RAN slices. When supporting a large amount RAN slices in types of eMBB, URLLC, and mMTC, the one-size-fits-all function splitting architecture in NG-RAN is not efficient in terms of resource allocation and network performance [100]. We strongly believe that a customized and dynamic distribution of the gNB functionalities is needed in order to satisfy the service requirements of the three aforementioned types of RAN slices in future networks.…”

Section: ) Softwarization and Virtualizationmentioning

confidence: 99%

The Road Towards 6G: A Comprehensive Survey

Jiang¹,

Han²,

Habibi³

et al. 2021

Preprint

101

158

View full text Add to dashboard Cite

<p>As of today, the fifth generation (5G) mobile communication system has been rolled out in many countries and the number of 5G subscribers already reaches a very large scale. It is time for academia and industry to shift their attention towards the next generation. At this crossroad, an overview of the current state of the art and a vision of future communications are definitely of interest. This article thus aims to provide a comprehensive survey to draw a picture of the sixth generation (6G) system in terms of drivers, use cases, usage scenarios, requirements, key performance indicators (KPIs), and enabling technologies. First, we attempt to answer the question of “Is there any need for 6G?” by shedding light on the key driving factors of 6G, in which we predict the explosive growth of mobile traffic until 2030, and envision potential use cases and usage scenarios. Second, the technical requirements of 6G are discussed and compared with those of 5G with respect to a set of KPIs in a quantitative manner. Third, the state-of-the-art 6G research efforts and activities from representative institutions and countries are summarized, and a tentative roadmap of definition, specification, standardization, and regulation is projected. Then, we identify a dozen of potential technologies and introduce their principles, advantages, challenges, and open research issues. Finally, the conclusions are drawn to paint a picture of “What 6G may look like?”. This survey is intended to serve as an enlightening guideline to spur interests and further investigations for subsequent research and development of 6G communications systems.</p>

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“…With the evolution of RAN to address the rapid growth of fronthaul traffic and the stringent requirement of latency, more flexible RAN architectures such as NG-RAN have been analyzed. Reference [22] proposed a resource allocation policy for RAN slicing in Multi-CU/DU architectures, the result showed that the proposed method reached the satisfactory performance and guaranteed the isolation between slices. The effective management strategy for the agile DU-CU deployment was investigated in terms of power consumption in [23], a mixed ILP model and a graph-based heuristic method was proposed to optimize the consumption of reconfigurable add/drop multiplexer, Ethernet switch, optical transponder and so on.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Policy for Baseband Function Placement and Routing of RAN in 5G and Beyond

Gao

Yan

Zhang

et al. 2022

J. Lightwave Technol.

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In this paper, we propose a deep reinforcement learning (DRL)-based algorithm to generate policies of Baseband Function (BBF) placement and routing. In order to explore the performance of the proposed algorithm in practical systems, the online scenario with the completely random requests is used in the simulation considering C-RAN and NG-RAN architectures. Besides, an Integer Linear Programming (ILP) model is formulated to generate the optimal solution as the benchmark. The simulation results show that DRL-based algorithm converges in a short time, and its performance closes to the optimal benchmark obtained by ILP in terms of latency and bandwidth for the online scenarios. In addition, the performance of the generated policies based on DRL is compared with a classic heuristic algorithm, i.e., first-fit algorithm. The performance of DRL-based algorithm is superior to the first-fit algorithm from above two perspectives. The fast convergence and the nearoptimal performance prove that the DRL-based algorithm is a promising approach for the BBF placement and routing of RAN in 5G and Beyond.

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RAN Slicing in Multi-CU/DU Architecture for 5G Services

Cited by 12 publications

References 6 publications

The Road Towards 6G: A Comprehensive Survey

The Road Towards 6G: A Comprehensive Survey

The Road Towards 6G: A Comprehensive Survey

Deep Reinforcement Learning-Based Policy for Baseband Function Placement and Routing of RAN in 5G and Beyond

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