A three-level slicing algorithm in a multi-slice multi-numerology context

Abstract

Fifth generation (5G) mobile networks feature a new concept called Bandwidth Part (BWP) that permits applying various multiple Orthogonal Frequency-Division Multiplexing (OFDM) subcarrier spacing, also coined numerologies, in the same band. With flexible numerology, the sought-after purpose is achieving lower latency for the Ultra Reliable Low Latency Communications (URLLC) service. However, the lucrative multiple numerology gains might not stand because of the ensuing Inter-Numerology Interference (INI) problems. In this paper, we assess the feasibility, as well as the profitability, of multi-numerology in a multi-slice setting. In such a context, it is paramount to study the radio resource allocation problem at the Radio Access Network (RAN) level. To that aim, we propose a three-level slicing algorithm that efficiently selects the BWP serving the URLLC users from a set of BWPs using different numerologies, designs a guard band among these BWPs to reduce INI without wasting radio resources, and attributes a bandwidth to each slice depending on multiple parameters such as the users’ Key Performance Indicators (KPIs), channel conditions, INI, and the incurred monetary cost. This work considers the three 5G services, namely the enhanced Mobile BroadBand (eMBB) service, the URLLC service and the massive Machine-Type Communications (mMTC) service. The proposed algorithm combines various tools from Game Theory, Deep-Q learning Networks as well as savvy heuristics. The performance evaluation demonstrates the high efficiency of our solution in terms of latency and throughput compared to well-known approaches in the State-of-the-Art.