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.
Highlights
Fifth generation (5G) cellular networks are required to support cell capacity of multi-gigabit per second (Gbps) and cell edge throughput of tens of megabits per second (Mbps)
Network slicing tries to slice the whole network into slices, each of which is tailored to the specific service requirements that are agreed to in a service level agreement (SLA) with customers
This scenario simulated a network with a cell radius of 100 meters (ISD = cell radius × 3), 10/20/30 UEs per sector, 4 beams to horizontally cover the sector area, a maximum 3 sectors per cluster, and a maximum of 4 MU-multiple input and multiple output (MIMO)
Summary
Fifth generation (5G) cellular networks are required to support cell capacity of multi-gigabit per second (Gbps) and cell edge throughput of tens of megabits per second (Mbps). In addition to the pure performance metrics, such as the rate, latency, reliability, and allowed connections, the scope of 5G incorporates the transformation of the mobile network ecosystem and accommodates heterogeneous services using one infrastructure [1]. To achieve this goal, 5G networks incorporate a technique named network slicing. Network slicing is an emerging business for operators that allows them to sell the customized network slices to various tenants at different prices [1]. As part of network slicing, radio access network (RAN) slicing defines a shared RAN connected to each of the multiple tenants’ core networks, with radio resources distributed by the central controller to different tenants, to maximize the radio resource usage
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