Characterization of Radio Access Network Slicing Scenarios With 5G QoS Provisioning

5G systems are envisaged to support a wide range of application scenarios with variate requirements. To handle this heterogeneity, 5G architecture includes network slicing capabilities that facilitate the partitioning of a single network infrastructure into multiple logical networks on top of it, each tailored to a given use case and provided with appropriate isolation and Quality of Service (QoS) characteristics. Network slicing also enables the use of multi-tenancy networks, in which the same infrastructure can be shared by multiple tenants by associating one slice to each tenant, easing the cost-effective deployment and operation of future 5G networks. Concerning the Radio Access Network (RAN), slicing is particularly challenging as it implies the configuration of multiple RAN behaviors over a common pool of radio resources. In this context, this work presents a Markov model for RAN slicing capable of characterizing diverse Radio Resource Management (RRM) strategies in multi-tenant and multi-service 5G scenarios including both guaranteed and non-guaranteed bit rate services. The proposed model captures the fact that different radio links from diverse users can experience distinct spectral efficiencies, which enables an accurate modeling of the randomness associated with the actual resource requirements. The model is evaluated in a multi-tenant scenario in urban micro cell and rural macro cell environments to illustrate the impact of the considered RRM polices in the QoS provisioning.