Abstract
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.
Highlights
The forthcoming Fifth Generation (5G) systems target the simultaneous support of a wide variety of application scenarios and vertical industries with distinct and variate requirements [1]. 5G will enable both the evolution of the current business models and the emergence of new ones
One key feature of the 5G system architecture is network slicing, which is based on Software-Defined Networking (SDN) and Network Function Virtualization (NFV) technologies [3] and allows the sharing of a common infrastructure among diverse end-to-end logical networks, each tailored for a given use case [4]
Regarding non-Guaranteed Bit Rate (GBR) services and considering that the number of assigned Physical Resource Blocks (PRB) to the non-Guaranteed Bit Rate (non-GBR) users depends on the spare PRBs after the allocation to GBR users, the average aggregated throughput Thx,s,n for non-GBR users is independent of its actual spectral efficiency and is given by: Thx,s,n = ax,s,n · Seff · B
Summary
The forthcoming Fifth Generation (5G) systems target the simultaneous support of a wide variety of application scenarios and vertical industries (e.g. automotive, utilities, smart cities, high-tech manufacturing) with distinct and variate requirements (i.e. high data rates, low latency, high mobility) [1]. 5G will enable both the evolution of the current business models and the emergence of new ones. This paper assumes that the OAM provides each gNB with the per-tenant parameters required to configure the RRM functionalities, as described in the detailed models for the L3 admission control and L2 resource allocation functions that are given in Sections III and IV, respectively. A. RESOURCE ALLOCATION TO GBR SERVICES For a given state x = S(u1,1,...,uMN ,N ), the target here is to model the aggregate number of assigned PRBs to the users of a given GBR service s of tenant n, denoted as ax,s,n. Assuming that each user experiences independent propagation conditions, the pdf of the aggregate number of required PRBs rx,s,n of the s-th service of the n-th tenant in state x can be computed as: frx,s,n (r ) =.
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