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AI-ML smart network CAPEX

URN M24.5.725
Topics 5G, AI (Artificial Intelligence), Autonomous networks

AI Smart CAPEX approach can help speed up the 5G expansion decision making and the CAPEX efficiency

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In the previous Catalyst, we built a pilot to optimize CapEx allocation of the Radio Access Network. In this new phase to propose to extend Smart CapEx beyond the access network into the Transport Network. Access and Transport networks are different nature and have different CapEx allocation and design rules. Access networks connect end devices, like computers and phones, to the nearest network node, primarily focusing on reaching users. Transport networks, on the other hand, link different network nodes, spanning large geographical areas to efficiently transfer data between access networks, emphasizing high-capacity, long-distance data transmission. In this phase the aim is to Improve the CAPEX allocation process by addressing the double challenge of forecasting traffic more accurately, and automating and optimizing a network rollout plan that covers the traffic growth while maintaining CX. More accurate traffic forecasts require a user-friendly application to share Marketing’s commercial projections with the Network team, and translate them to the street level for both traditional and new use cases. Calculating the optimal network rollout will make heavy use of AI algorithms in a Big Data environment, typically on the cloud. The business values are 15-20% CAPEX and OPEX savings on the new Network investments and a significant OPEX saving on the existing Network Infrastructure. In this phase we focus on Transport Network Planning, compared to the previous phase, in which we focused on the Radio Access Network.

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