March 29, 2021 Journal article Open Access

Behnam Ojaghi; Ferran Adelantado; Angelos Antonopoulos; Christos Verikoukis

DCAT Export

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        <foaf:name>Behnam Ojaghi</foaf:name>
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        <foaf:name>Ferran Adelantado</foaf:name>
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        <foaf:name>Angelos Antonopoulos</foaf:name>
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        <foaf:name>Christos Verikoukis</foaf:name>
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    <dct:title>SlicedRAN: Service-Aware Network Slicing Framework for 5G Radio Access Networks</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2021</dct:issued>
    <dcat:keyword>5G mobile communication , Radio access networks , Bandwidth , Throughput , Quality of service , Network topology , Virtualization</dcat:keyword>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2021-03-29</dct:issued>
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    <dct:description>&lt;p&gt;5G mobile networks are envisioned to substantiate new vertical services with diverse performance requirements. Slicing in the radio access network (RAN) promises an efficient solution for these diversified needs of 5G networks, which foresees the separation of the base station functionality between the central unit (CU) and the distributed remote radio heads. In this article, we formulate a mixed integer programming (MIP) framework that maximizes the throughput by jointly selecting the optimal functional split and the routing path from a connected user equipment to the CU, while satisfying the agreed service level agreements (SLAs) of each service. Furthermore, we propose an effective heuristic, SlicedRAN, which creates isolated RAN slices premised on the service requirements connected through a fronthaul/backhaul (FH/BH) network and obtains near-optimal solutions in a short computing time compared to the MIP framework. Our results show that there is a tradeoff between the architecture of the FH/BH network and the minimum SLA of each slice, which provides a solution to efficiently design a virtualized network infrastructure. According to the results, the SlicedRAN outperforms existing state-of-the-art up to 112% gain in throughput. Results are shown close to the optimal results, with a loss below 5%.&lt;/p&gt;</dct:description>
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    <dct:title>Single Point Of aTtachment communications empowered by cLoud computing and bIG data analytics running on-top of massively distributed and loosely-coupled Heterogeneous mobile data neTworks</dct:title>
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