Accurate RAN Planning Saves Billions
Planet RF coverage image courtesy of Infovista
Continuous RAN Planning enables new revenue
RAN planning tools are evolving into RAN optimization tools, that use a continuous cycle to determine the optimal network configuration based on constantly updated map data and capacity/demand data. The idea is to use AI and Machine Learning to create a digital representation of the network, so that the operators can quickly consider changes. One example of a high-impact network change is the Network Slice. Consider an enterprise that wants to buy a Network Slice from a mobile operator, with guaranteed reliability and latency for their own private network. The process of setting up a Network Slice is not well automated today, and the operator could spend weeks investigating the resource allocations that would be necessary in processors, memory, radio resource blocks, and other aspects of the network. A sophisticated model could simulate a network slice within minutes, and then drive a re-provisioning sequence that sets aside resources throughout the end-to-end network. To reduce slice assessment times from potentially weeks down to hours, or even minutes, will require a planning tool that is tightly integrated into the network. Automation workflows to constantly update the planning tool with the latest network data and orchestration API’s to impleme nt the relevant network changes will be key components. The speed and tight integration to the network required to fulfil this use case means a cloud- native planning tool is the best approach. As an example of this, Rakuten is implementing a cloud-native “on - demand” radio network planning solution with Infovista that allows its engineering teams to utilize advanced planning and design features devised to efficiently reduce the time and cost of network
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