SlicedRAN: Service-Aware Network Slicing Framework for 5G Radio Access Networks

Ojaghi, Behnam; Adelantado, Ferrán; Antonopoulos, Angelos; Verikoukis, Christos

doi:10.1109/jsyst.2021.3064398

Cited by 26 publications

(15 citation statements)

References 31 publications

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“…In [7] and [18], slice-centric functional split and user association are performed while considering functional and traffic splitting, though slice delay requirements are not considered. Moreover, these works also consider a single CU in the network, due to which different baseband function placement options are not considered.…”

Section: Related Workmentioning

confidence: 99%

Slice Aware Baseband Function Placement in 5G RAN Using Functional and Traffic Split

Sen

Franklin

2023

IEEE Access

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5G and Beyond 5G (B5G) are undergoing numerous architectural changes to enable higher flexibility and efficiency in mobile networks. Unlike traditional mobile networks, baseband functions in 5G are disaggregated into multiple components -Radio Unit (RU), Distributed Unit (DU), and Centralized Unit (CU). These components can be placed in different geographical locations based on the latency sensitivity and available capacity in the network. Processing baseband functions in a centralized location offer various advantages (known as centralization benefit in RAN) to mobile network operators, which have been a point of interest for several research works. However, achieving maximum centralization is challenging due to various factors such as limited capacity in the midhaul network, delay requirement of different functional splits and network slices, etc. In this work, we aim to address these challenges by proposing a slice-aware baseband function placement strategy. Our primary objective is to maximize the degree of centralization in the network by appropriate selection of functional split. To achieve this objective, we jointly consider functional split, traffic split, different placement options for baseband functions, and network slice-specific requirements. We also consider the minimization of active processing nodes in cloud infrastructure of different levels (edge and regional) to provide additional resource efficiency. To this end, we formulate an optimization model using Mixed Integer Linear Programming (MILP) and compare its performance with different baseline techniques. We show that the proposed model achieves 6.5% more degree of centralization than the state-of-the-art while placing baseband functions in the network. To tackle the high computational complexity of the MILP model, we also present a polynomial-time heuristic algorithm for solving the problem in large-scale scenarios. We show that although the optimization model achieves around 4% more degree of centralization than the heuristic, the heuristic solves the problem in a reasonable amount of time, making it suitable for real deployment scenarios.

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Section: Related Workmentioning

confidence: 99%

Slice Aware Baseband Function Placement in 5G RAN Using Functional and Traffic Split

Sen

Franklin

2023

IEEE Access

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“…In parallel to the SDOs and industry, a considerable amount of effort has been made in academia to study the extension of virtualization to the RAN architecture and the slicing of underlying resources. The overwhelming majority of available theoretical research focuses on (i) the slicing of radio resources [32]- [39], (ii) the allocation of virtual resources [40]- [42], (iii) the energy consumption of various types of RAN slices [43]- [45], and (iv) the application of AI techniques (notably ML-assisted algorithms) in the operations of RAN architecture [46], [47].…”

Section: ) Conceptual and Analytical Models For Ran Slicing In The Ng...mentioning

confidence: 99%

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

Habibi

Yousaf²,

Schotten

2022

IEEE Open J. Commun. Soc.

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The mapping of a virtual network service onto a physical network infrastructure is a challenging task due to the joint allocation of virtual resources across nodes and links, the diverse technical requirements of end-users, the coordination between multiple host domains, and others. This issue is exacerbated further by the extension of virtualization to the next-generation radio access network (NG-RAN) architecture and the provisioning of radio access network (RAN) slicing. To that end, this article focuses on the mapping problem of the virtual network functions (VNFs), as well as their internal and external virtual links (VLs), of a RAN slice subnet onto intelligent points of presence (I-PoPs) and transport networks in the NG-RAN architecture. In this context, in contrast to the majority of the state-of-the-art proposals, which frequently fail to achieve performance objectives and neglect resource allocation constraints, this article introduces automation and intelligence at an architectural level to map VNFs and VLs onto their corresponding physical nodes and links, with the goal of achieving superior efficiency in virtual resource utilization while granting the performance of a RAN slice subnet. Benefiting from a top-down approach, the key contributions of this article are: (i) to extend the architectural framework of network slicing towards the NG-RAN architecture and provide a comprehensive overview and critical analysis of the components and functionalities of a RAN slice subnet; (ii) to integrate the Experiential Network Intelligence (ENI) framework into a joint architecture of the network functions virtualization-management and orchestration (NFV-MANO), Third Generation Partnership Project-network slicing management system (3GPP-NSMS), and I-PoPs in order to render automation and intelligence to the management and orchestration aspects of a RAN slice subnet in the NG-RAN architecture; and (iii) to propose a learning-assisted architectural solution for mapping the VNFs, as well as their internal and external VLs, of a RAN slice subnet onto the underlying I-PoPs and transport networks.

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“…Following our previous work [9], where capacity and structure of the X-haul were analyzed, hereafter the impact of the RAN density (i.e. the number of deployed DUs) on the joint slicing and functional splitting is investigated.…”

Section: Impact Of Network Densification On Joint Slicing and Functio...mentioning

confidence: 99%

“…In our previous work [9] a dynamic joint functional split and RAN slicing algorithm was proposed. It was apparent that instantiating each slice with the most suitable functional split can enhance the performance of the network.…”

Section: Introductionmentioning

confidence: 99%

Impact of Network Densification on Joint Slicing and Functional Splitting in 5G

et al. 2022

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The virtualization of the 5 th Generation (5G) Radio Access Network (RAN), which distributes the Next Generation Node B (gNB) functions between a Central Unit (CU) and a Distributed Unit (DU), along with the emergence of slicing as a cornerstone of mobile communications networks, pose new challenges. One of these challenges is the management of the joint slicing and allocation of appropriate distribution of functions between CU and DU, known as functional split. In this work, a discussion on the challenges and open issues of joint slicing and functional splitting is posed, taking into account the additional complexity resulted from instantiating multiple slices per DU, each one with a different functional split. It is shown that the additional complexity, which is translated into higher cost, is worthwhile depending on the RAN deployment density.

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SlicedRAN: Service-Aware Network Slicing Framework for 5G Radio Access Networks

Cited by 26 publications

References 31 publications

Slice Aware Baseband Function Placement in 5G RAN Using Functional and Traffic Split

Slice Aware Baseband Function Placement in 5G RAN Using Functional and Traffic Split

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

Impact of Network Densification on Joint Slicing and Functional Splitting in 5G

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