On the automation of RAN slicing provisioning: solution framework and applicability examples

Ferrús, R.; Sallent, O.; Pérez‐Romero, Jordi; Agustí, R.

doi:10.1186/s13638-019-1486-1

Cited by 10 publications

(11 citation statements)

References 18 publications

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“…The NG-RAN (specifically the gNB) interacts over N2 (or next-generation [NG]) interface with the 5GC (see Fig. 2) in order to provide required NFs and resources to each US-NSI [8]. The gNBs are interconnected over Xn interfaces, and deliver full NG-RAN functionality to interact with the UEs using the NR interface.…”

Section: A the Architectural Framework Of The Use-case Specific Network Slicingmentioning

confidence: 99%

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How Should Network Slice Instances Be Provided to Multiple Use Cases of a Single Vertical Industry?

Habibi

Han

Yousaf

et al. 2020

IEEE Comm. Stand. Mag.

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There are a large number of vertical industries implementing multiple use cases, each use case characterized by diverging service, network, and connectivity requirements -such as automobile, manufacturing, power grid, etc. Such heterogeneity cannot be effectively managed and efficiently mapped onto a single type of network slice instance (NSI). Thus the tailored provisioning of an end-to-end network slicing solution to a vertical industry that consists of multiple use cases is a critical issue, which motivates this article, aimed at exploring this never-addressed and challenging research problem by proposing the Use-case Specific Network Slicing and the Sub Network Slicing concepts that enable the provisioning of Use-case Specific NSI (US-NSI) and GeNeric NSI (GN-NSI), respectively. Both approaches tackle the same technical issue of provisioning, management, and orchestration of per-vertical per-use-case NSIs in order to improve resource allocation and enhance network performance. The article also presents the architectural frameworks for managing US-NSI and GN-NSI, which extend the service deployment concept and system architecture of network slicing for vertical customers of fifth generation (5G) mobile networks.

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Section: A the Architectural Framework Of The Use-case Specific Network Slicingmentioning

confidence: 99%

“…The US-NSIs can be offered to the UEs from the same or different gNB(s) [8]. 3GPP also specifies that a UE can be served by at most eight NSIs (i.e., US-NSIs) concurrently over a single NG-RAN, sharing a common AMF in all US-NSIs and an individual SMF to every US-NSI [7] in the following two scenarios (see the bottom-right side of Fig.…”

mentioning

confidence: 99%

How Should Network Slice Instances Be Provided to Multiple Use Cases of a Single Vertical Industry?

Habibi

Han

Yousaf

et al. 2020

IEEE Comm. Stand. Mag.

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“…Ensuring energy efficient allocation is directly associated with reduced OPEX. Several research works [1]- [5] focused on energy efficient resource allocation processes, leveraging on the application of the aforementioned paradigm. Resources, in that context, can be antennas or Baseband Units (BBU).…”

Section: Introductionmentioning

confidence: 99%

“…There have been valuable research efforts on the application of SDN/NFV and autonomous management of RAN, in 5G RAN sharing [3] and leveraging these for 5G RAN Slicing [4]- [5]. On the other hand, others have proposed EE RA for C-RANs [1]- [2].…”

Section: Introductionmentioning

confidence: 99%

On Energy Efficient Resource Allocation in Shared RANs: Survey and Qualitative Analysis

Marzouk

Barraca

Radwan

2020

IEEE Commun. Surv. Tutorials

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An expansion of services and unprecedented traffic growth is anticipated in future networks, aligned with the adoption of the long-awaited Fifth Generation (5G) of mobile communications. To support this demand, without exposing mobile operators to the pressure of CAPEX and OPEX, 5G uses new frequency bands, and adopts promising trends, including: densification, softwarization, and autonomous management. While the first technology is proposed to handle the traffic growth requirements, the softwarization and autonomous management are expected to play, in synergy, to ensure the desired trade-off between reducing the CAPEX and OPEX, while guaranteeing the quality of service (QoS). Softwarization is expected to transform the network design, from one size fits all, to more demand oriented adaptive resource allocation. In this work, we focus on this point, by discussing how these technologies act in synergy towards enabling RAN sharing. Particularly, we focus on how they fit into the issue of energy efficient Multi-Operator Resource Allocation (MO-RA). After a survey and classification of schemes leveraging this synergy for distinct resource allocation (RA) objectives, we present a detailed survey and qualitative classification of RA schemes with respect to energy efficiency. This work presents an innovative survey, since it concentrates on multiple operators, and the enabling of Mobile Virtual Network Operators (MVNOs), which will come into play with the complete virtualization of mobile networks. Based on the deep literature analysis of the different operations that can bring energy savings to MO-RA, we conclude the work with listing open challenges and future research directions.

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“…The creation and management of network slices is especially challenging in the Radio Access Network (RAN), where multiple slices can be delivered over the same radio channel and the system shall guarantee that the allocation and distribution of the radio resources within the radio channel is done so that specific requirements per slice can be fulfilled (e.g. guaranteed capacity) while using radio resources efficiently [7]- [14]. Remarkably, the automation of the life-cycle management of network slices in the RAN requires two main functionalities: slice admission control and crossslice resource optimization.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-assisted cross-slice radio resource optimization: Implementation framework and algorithmic solution

Agustí¹,

Vilà²,

Sallent³

et al. 2020

ITU J-FET

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Network slicing is a central feature in 5G and beyond systems to allow operators to customize their networks for different applications and customers. With network slicing, different logical networks, i.e. network slices, with specific functional and performance requirements can be created over the same physical network. A key challenge associated with the exploitation of the network slicing feature is how to efficiently allocate underlying network resources, especially radio resources, to cope with the spatio-temporal traffic variability while ensuring that network slices can be provisioned and assured within the boundaries of Service Level Agreements / Service Level Specifications (SLAs/SLSs) with customers. In this field, the use of artificial intelligence, and, specifically, Machine Learning (ML) techniques, has arisen as a promising approach to cater for the complexity of resource allocation optimization among network slices. This paper tackles the description of a feasible implementation framework for deploying ML-assisted solutions for cross-slice radio resource optimization that builds upon the work conducted by 3GPP and O-RAN Alliance. On this basis, the paper also describes and evaluates an ML-assisted solution that uses a Multi-Agent Reinforcement Learning (MARL) approach based on the Deep Q-Network (DQN) technique and fits within the presented implementation framework.

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On the automation of RAN slicing provisioning: solution framework and applicability examples

Cited by 10 publications

References 18 publications

How Should Network Slice Instances Be Provided to Multiple Use Cases of a Single Vertical Industry?

How Should Network Slice Instances Be Provided to Multiple Use Cases of a Single Vertical Industry?

On Energy Efficient Resource Allocation in Shared RANs: Survey and Qualitative Analysis

Machine learning-assisted cross-slice radio resource optimization: Implementation framework and algorithmic solution

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