Data-Driven Model for Sliced 5G Network Dimensioning and Planning, Featured With Forecast and “what-if” Analysis

Abstract

Network Slicing is an enabler for new use cases and an improved network performance, especially for 5G private networks, which opens new business opportunities for vendors and applications for customers. On the other hand, the slicing mechanism adds another level of complexity to network management that significantly increases total cost of ownership. Full automation is a must, which is also evident in the standardization work on autonomous and zero-touch mobile networks under the umbrella of 3GPP and ITU organizations. Moreover, there is a clear methodological gap in research related to mobile network slicing, i.e. capacity dimensioning and planning for such infrastructure. The concept of the network modeling tool has been updated with an emphasis on adding functionality of mobile network capacity dimensioning and planning, which is presented in this article. Data-driven framework with thoroughly verified methods is outlined (e.g., Prophet, Neural Networks, VARMAX and its univariate equivalent - ARMA). Special attention is paid to traffic forecasting as the basis for the dimensioning and planning process. We evaluate how to use the framework as a scenario simulator to estimate the impact of traffic changes in any slice on quality of service (namely throughput and delay) of all. Finally, we explain how this solution realizes the concept of Digital Twin-based network simulator.

Keywords

• 5G mobile communication
• autoregressive processes
• capacity planning
• digital twins
• network slicing
• neural networks