AI-driven Planning of Private Networks for Shared Operator Models

Private networks represent a key innovation in current 5G and future 6G networks, offering significant benefits, particularly for vertical industries with mission-critical industrial applications. Compared to public networks, the deployment of numerous potential private networks demands automated network planning while simultaneously meeting higher performance requirements for targeted applications. Emerging approaches have successfully utilized AI-based methodologies as a basis for automated network planning in greenfield deployments within licensed but purely private frequency bands. However, these approaches fail to include and extend brownfield implementations in public mobile networks, which would be crucial for private networks running a shared operator model. Thus, this paper presents an AI-based automated network planning methodology augmented by our recently introduced and thoroughly validated data-driven channel modeling approach, DRaGon. Further, this combined AI-based planning methodology is extended to provide automated network planning solutions for shared private networks within public macro networks. The overall planning accuracy was successfully validated with only minor deviations using public network deployments as ground truth. As a key result, we demonstrate that the performance of the presented AI-based planning method can reliably and accurately plan demand-driven network expansions for professional applications with the highest quality requirements.