Performance Model for Video Service in 5G Networks

Jiao Wang, Jay Weitzen, Oguz Bayat, Volkan Sevindik and Mingzhe Li
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Jiao Wang: Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01854, USA
Jay Weitzen: Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01854, USA
Oguz Bayat: Graduate School of Science and Engineering, Altinbas University, 34217 Istanbul, Turkey
Volkan Sevindik: Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01854, USA
Mingzhe Li: Q Factor Communications, 255 Bear Hill Road, Waltham, MA 02451, USA

Future Internet, 2020, vol. 12, issue 6, 1-21

Abstract: Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.

Keywords: interference coordination (IC); network slicing; 5G; quality of service (QoS); massive multiple input and multiple output (MIMO) (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
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