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A Base Station Agnostic Network Slicing Framework for 5G

Authors: Georgia Tseliou; Ferran Adelantado; Christos V. Verikoukis;

A Base Station Agnostic Network Slicing Framework for 5G

Abstract

The major investment required to roll out new 5G networks has been partially overcome with the definition of a set of migration paths from current 4G deployments to full 5G NR standalone deployments. However, these migration paths, which reduce the time to market of 5G services and the initial investment, result in heterogeneous networks with higher complexity. In this article, we propose a base station agnostic framework for Network Slicing (NetSliC), a new virtualization layer that creates slices taking into account a set of specific constraints per BS. The proposed mechanism is able to effectively deal with these new network architectures by abstracting the complexity of BSs and slicing the network to guarantee the traffic requirements. It is shown that NetSliC can outperform the existing literature. For instance, NetSliC achieves around 67 percent average throughput gain and 16.6 percent baseband unit processing load reduction when compared to baseline scenarios.

Grant numbers : SPOT5G - Single Point of attachment communications heterogeneous mobile data networks (TEC2017-87456-P). © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

network, slicing, virtualization, 5G

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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