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GPU propagation engine

5G cell

We have developed a fast GPU RF propagation engine.

We’ve been busy behind the scenes designing and developing the next generation of fast radio simulation engines for urban modelling with NVIDIA CUDA technology and Graphics Processing Units (GPU).

The engine was made to meet demand across many sectors for speed and accuracy and to enable an automated best-site-analysis capability, which will accelerate planning and improve efficiency whilst keeping a human in the loop.

Designed for 5G

5G networks are much denser than legacy standards due to limited range of mmWave signals, necessary for high bandwidth data. The same limitation means these signals are very sensitive to obstructions, and line of sight coverage is essential for performance.

A dense network means more low power (small) cells are needed, which means more efficient planning is needed.

You can’t just place 5G cells on big hills and crank up the power like it’s the 1990s as the low power handsets would not be able to talk back to them. To achieve an economic and balanced low power urban network requires careful and thorough planning.

Core features

Our GPU engine has several modes, for different use cases. Here’s two we’re focusing on for this quarter.

LOS viewshed

Real time urban analysis

The simplest mode is a fast line of sight “2.5D” viewshed (with a path loss model) which creates a point-to-multipoint heatmap around a given site using LiDAR data. This is comparable to using the current CPU engine with LOS mode – only much quicker. This is up to 50 times faster than our multi-threaded CPU engine, SLEIPNIR.

Demo video:

ETA: February 2022

Best site analysis

A heatmap of options..

Best Site Analysis (BSA) is a monte-carlo analysis technique across a wide area of interest to identify the best locations for a transmitter. Now we have the GPU speed, this can be done quickly with a new /bsa API call. Presently our GPU based BSA implementation can search a radius around a location, using the 2.5D viewshed technique, to grade locations. The output will identify optimal sites, and just as important, inefficient sites.

This feature will replace the “best site” tool currently in the web interface which is not GPU accelerated

This feature is powerful for IoT gateway placement, 5G deployments and ad-hoc networking where the best site might presently be determined by a map study based on contours as opposed to a LiDAR model.

ETA: March 2022

High speed

Our GPU engine is up to 50 times faster through the API than the current (CPU) engine SLEIPNIRTM

By harnessing the power of high performance graphics cards, we are able to complete high resolution LiDAR plots in near real time, negating the need for a “start” button, or even a progress bar! This speed enables API integration with autonomous drones which will need to model propagation to make better decisions, especially when they’re off the grid. It was designed around consumer grade cards like the GeForce series but will scale to enterprise Tesla grade cards due to our design.

Open API

When it goes live, it will be an option in our /area API so you can use it from any interface by setting the engine option in the request body. The OpenAPI 3.0 compliant API returns JSON which contains a PNG image so for existing API integrations using our PNG layers there will be no code changes required to enable it.

At the time of writing the API integration is undergoing bench testing (see video). This feature is scheduled for public Beta testing in February 2022.


Using GPU cards to model Physics, including EM propagation, is an established concept dating back 20 years, despite sales-first businessmen claiming otherwise. Advances in gaming in particular have made ray tracing a mainstream term but there’s a big difference between ray tracing a visual perspective (in view) and modelling a high resolution raster or voxel map to generate a deliverable output. One is pretty and good for games, investors and technology hype-beasts and the other is actually useful for radio engineering.

What is novel here is making this exciting technology accessible to users priced out of premium tools using consumer grade GeForce cards.

Staying true to CloudRF’s accessible and affordable principles, we’ll include it in our service as an optional processing engine this year. Quite what this means for market incumbents and upstarts who currently charge SMEs a small fortune for a basic capability will be interesting. We’ll let the market answer that one.

CloudRF is a member of the free-to-join NVIDIA inception program