Digital Twin for 5G Networks

Abstract

The current 5th Generation Mobile Networks (5G) standardization is aiming to significantly raise the applicability of communication networks for a wide variety of use cases spanning from industrial networks, automotive, content acquisition, multimedia broadcasters and eHealth (NGMN Alliance. 5G white paper. Next generation mobile networks, white paper 1, 2015). At the same time, this presumes that a smaller size, dedicated 5G network must be integrated into an existing complex communication infrastructure, specific to the use case. This becomes particularly challenging with a 5G network as it is a highly complex systems by itself with highly complex network management requirements in terms of fault, performance, and security. To address this issue, existing work suggests that the use of Digital Twins (DT) or Asset Administration Shells (AAS) within the industrial domain, to model information about the 5G network and to use this data to plan, evaluate and make decisions on how to optimize the behavior of the system. However, the DT based modelling of 5G systems remains a relative new topic. Within this chapter, we provide a comprehensive overview of how the exiting 5G network management uses a sort of Digital Twin (DT) approach and how a full DT paradigm would optimize the 5G networks. First, the 5G network as a complex system will be described with the specific automation and optimization capabilities as well as underlining its limitations. The additional opportunities for a more flexible DT of the 5G network, due to its softwarization would be further analyzed especially concentrating on the extension of the DT model towards an even more complexity as well as towards the new opportunities of dynamic resource scheduling as representative elements for the 5G network management functionality. A short analysis on the impact of the network between the DT and the 5G system will be provided to understand the impact of the network characteristics such as delay, capacity, and packet loss on the functioning of the system. To conclude, the presented considerations can act as robust enablers for future 6G networks including multiple self-reconfiguration mechanisms. A short set of considerations are made on the governance of the multiple decision points and potential ways to implement such multi-decision models.