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The business case for a modern planning approach As discussed above, modernizing RAN planning with fit-for-purpose solutions can drive CAPEX savings. AI-driven RAN planning can improve accuracy, saving potentially millions on network buildout. But the business rationale is about more than reducing CAPEX. Cloud-native, AI-driven RAN planning, with its pre-calibrated propagation models and on-demand geodata can also drive significant savings in both time and money in the RAN planning process itself. These OPEX savings can be broadly bucketed as follows:
Geodata – when you need it, where you need it Geodata delivered as a service through APIs integrated directly into the RAN planning software means organizations can access the geodata they need, when they need it. What was once a fixed, capital-intensive process, now becomes a flexible, cost-controlled process. Geodata is no longer purchased, it is subscribed to. In a time-limited project – for example, the planning of a single network cluster for a mobile network operator by a 3rd party – the economics of acquiring, installing and validating geodata simply don’t stack up. But subscribing to validated, accurate geodata for just that specific area, for only as long as the project requires, changes the OPEX equation. Propagation – pre-configured for accuracy Alongside geodata, well-calibrated propagation models are the foundation of accurate network design. But complex calibration, often spanning multiple frequencies, requires dedicated time from valuable RF engineering resources.
Cloud-based propagation modeling, with its specialized wireless propagation algorithms and pre-calibration using massive datasets across multiple frequency bands, means time- consuming and costly manual propagation model tuning is no longer required. IT overheads – cloud collaboration and scale Planning today’s complex networks is by its very nature a compute-intensive process, with its vast datasets and AI modeling. Legacy desktop planning tools are increasingly not fit-for-purpose. For example, in a scenario where a small regional operator is planning and optimizing a nationwide network, that could mean a team of 10+ RF engineers, each bringing their own IT overhead and siloed workflows. Intensive RF modeling can take advantage of cloud compute power, be it in a private, hybrid or public cloud, significantly reducing planning time. Improved collaboration between teams, all working in parallel with the same pre-calibrated propagation models and validated data sets, improves the efficiency of the end-to-end planning process.
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