Joint Admission Control and Slice Dimensioning Based on Symbol-Level Resource Allocation in 5G+

With the development of 5G, network slicing was proposed to enable service provisioning for diverse sets of Ultra-Reliable Low-Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communications (mMTC) users which are characterized by different Quality of Service (QoS) demands. Within network slicing, Radio Access Network (RAN) slicing plays a central role for efficient resource management. In addition, user admission control poses a major challenge. In this context, the problem of joint slice dimensioning and user admission control is investigated in this paper. To this end, an optimization problem based on symbol-level resource allocation with the objective of maximizing an operator's revenue while fulfilling the traffic requirements of all users is formulated. Afterward, the optimization problem is reduced to a knapsack problem and integrated into a Long-Term Revenue Maximization (LTRM) algorithm. Using data from real-world 5G measurements, the efficiency of the LTRM algorithm is verified, and the impact of various resource granularities in the time domain (symbol vs. slot) and frequency domain (varying Resource Block Group (RBG) sizes) is investigated. The revenue gain of the proposed joint algorithm over a sequential slice dimensioning and user admission control scheme is 24%, while symbol-level resource allocation offers at least 13% gain over a slot-based allocation for specific slices.