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7. Scalability: With high resolution geodata, 3D predictions and raytracing or ray-launching propagation models, calculating coverage becomes an extremely processor intensive exercise. Planning tools need to be able to harness large amounts of hardware to complete predictions in a reasonable time. A cloud-based micro services approach provides the elastic horizontal scalability to undertake large computations without the need to have large amounts of dedicated hardware which would spend a lot of time underutilized.
Advanced Propagation Models of mmWave
As mentioned earlier, propagation accuracy is probably the most important aspect of a planning tool. If coverage predictions are optimistic, too few sites will be built resulting in coverage black spots and poor signal quality, leading to churn and uncaptured revenues. While if coverage predictions are pessimistic too many sites will be built increasing CAPEX expenditure by up to 20% and reducing profitability. It is therefore critical that propagation models are as accurate as possible to avoid these scenarios.
Figure 21: 3D traffic demand map
To deliver highly accurate propagation requires a model which supports the following:
1. mmWave: Perhaps obvious, but when designing a mmWave network, the chosen propagation model must support mmWave frequencies and must include critical features such as building and vegetation through loss support, rain modeling and assessment of line-of-sight and non-line-of-sight coverage. 2. Calibration: Calibration is a critical component of a mmWave propagation model. Models should be pre-calibrated using machine learning and massive data sets, so they have a high degree of accuracy out of the box. They can then be further calibrated with drive or walk test data gathered from the specific network. Finally, they should be continuously benchmarked against crowdsources and/or geo-located trace data to validate they are aligned with actual customer experiences.
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