Abstract
For accommodating the heterogeneous services that are anticipated for the fifth-generation (5G) mobile networks, the concept of network slicing serves as a key technology. Spanning both the core network (CN) and radio access network (RAN), slices are end-to-end virtual networks that share the resources of a physical network. Slicing the RAN can be more challenging than slicing the CN since RAN slicing deals with the distribution of radio resources, which have fluctuating capacity and are harder to extend. Improving multiplexing gains, while assuring the slice isolation is the main challenging task for RAN slicing. This paper provides a flexible and configurable framework for RAN slicing, where diverse requirements of slices are simultaneously taken into account, and slice management algorithms adjust the control parameters of different radio resource management (RRM) mechanisms to satisfy the slices’ service level agreements (SLAs). One of the proposed algorithms is based merely on heuristics and the other one utilizes an artificial neural network (ANN) to predict the behavior of the cellular network and make better decisions in the adjustment of the RRM mechanisms. Furthermore, a protection mechanism is devised to prevent the slices from negatively influencing each other’s performances. A simulation-based analysis demonstrates that in presence of local or global overload of one of the slices, the ANN-based method increases the number of key performance indicators (KPIs) that fulfill their defined SLA targets. Finally, we show that the proposed protection mechanism can force the negative effects of an overloading slice to be contained to that slice and the other slices are not affected as severely.
Highlights
It is anticipated that the fifth-generation (5G) mobile networks will support a multitude of heterogeneous services, such as enhanced Mobile Broadband, Ultra Reliable Low Latency Communications (URLLC) and massive Machine Type Communications [1]
We start by specifying the different types of slices and propose an entity called radio access network (RAN) slice orchestrator that monitors the slice key performance indicators (KPIs) and network conditions. It ensures the simultaneous fulfillment of the slices’ KPIs. If this entity detects that certain KPIs are below their targets, it tries to fine-tune the control parameters of the packet scheduler (PS) and the admission control (AC) such that service level agreements (SLAs) fulfillment is achieved for all slices
SLICE MANAGEMENT ALGORITHM we introduce an adaptive algorithm that iteratively changes the resource management (RRM) control parameters such that the multi-objective optimization that is introduced in Subsection III-D is solved, i.e., the number of fulfilled KPIs is maximized
Summary
It is anticipated that the fifth-generation (5G) mobile networks will support a multitude of heterogeneous services, such as enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communications (URLLC) and massive Machine Type Communications (mMTC) [1]. The dynamic sharing of the common physical infrastructure, i.e., radio resources in RAN slicing, can bring about massive multiplexing gains. We start by specifying the different types of slices and propose an entity called RAN slice orchestrator that monitors the slice KPIs and network conditions. It ensures the simultaneous fulfillment of the slices’ KPIs. It ensures the simultaneous fulfillment of the slices’ KPIs If this entity detects that certain KPIs are below their targets, it tries to fine-tune the control parameters of the packet scheduler (PS) and the admission control (AC) such that SLA fulfillment is achieved for all slices.
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