Radio frequency planning is the process to generate coverage models before actually building any technical systems in the field. We generate predictive models to show what the coverage would be like once the physical systems are in place. This process permits us to see flaws in our plans before construction. We can tweak the plans and optimize them in advance of the deployments. This can save huge sums of money at a relative low upfront cost. Now, predictive modelling is not perfect and it needs to be optimized and corrected in an iterative manner to build greater trust in the models. We do that by drive testing a preliminary test deployment and then compare that physical data back to the predictive data and then adjust the predictive data to reflect the physical test data from the field. That way, we can reuse the predictive models in other geographic areas with similar technology and know that it is closer to reality. Predictive modelling can only ever go so far, it will never be perfect. So, it is wise to retain a budget to perform back-fill of the build once holes in the coverage are identified.
We begin by sourcing data for the location. We need maps, photographs, elevation data, and more. Terrain elevation data can be sourced from existing databases, if they exist. If they are not readily available, then they can be generated from drones, airplanes, or satellites. A common source of data in the Jet Propulsion Laboratories’ SRTM online data sets. The highest-resolution topographic data generated from NASA’s Shuttle Radar Topography Mission (SRTM) in 2000 was to be released globally by late 2015. SRTM data for regions outside the United States were sampled for public release at 3 arc-seconds, which is 1/1200th of a degree of latitude and longitude, or about 90 meters (295 feet). The new data have been released with a 1 arc-second, or about 30 meters (98 feet), sampling that reveals the full resolution of the original measurements.
Below are a series of three images of the same location, the Village of Banff, Alberta. First, we apply the SRTM data to understand the terrain elevation changes, second, we place a map over top of the SRTM data to provide a human reference to the location. Finally, third, we model the RF coverage, in this case, a smart city Wi-Fi coverage to a 3D model to show where the signals will be present and more importantly where they will not be available.
A terrain profile is add to to 3D model to enhance the context to the elevation changes and to show these elevation changes will impact the signal propagation. Trees, buildings, and other surface morphology elements were included in this modelling example to further enhance the accuracy of the proposed realistic signal coverage.