API Archives - CloudRF

Posted on 2023-11-152023-11-15

An API for Cognitive Radio

Cognitive radio, in it’s present form, describes a spectrum sensing radio which can change channel to avoid noise. This capability is standard now on domestic Wi-Fi routers.

With our modelling API and modern radios with APIs we can do much more. Here we present two demos using a raspberry Pi as the server. By compiling our API for ARM and hosting it on a small computer we negate the need for a network to use the API which means we can develop functions to assist radios which may have fallen off the network.

Dynamic transmit power

Problem

Automatic Gain Control (AGC) is an established concept which uses a feedback loop to adjust power to maintain a desired level. This technology is what makes a mobile phone battery last so long. When the phone is near a tower it radiates a fraction of the power it would if it were far away in the countryside. It’s able to do this as the network infrastructure is fixed and there is a signalling channel to make these adjustments eg. “reduce power 10dB”.

A terrestrial broadcast radio network is a much more dynamic environment and as a result it’s common for radios to transmit more power than they need to as power control is simple (low, medium or high on a dial) and once a radio is set to high, the operator is not incentivised to change it as “it works”. This inefficiency reduces battery life and creates excess spectrum noise.

Solution

Using our “Path” API, we perform tests to a distant station to establish the modelled signal to noise (SNR) ratio. This distant station could be the nearest node or a repeater. With the SNR result, we are able to compute an adjustment to the local radio to meet a desired signal level. This could be -90dBm for example.

In our demo, we fixed a receiver location 5km west and moved our transmitter on a north to south path through mixed clutter. The variance exercised the dynamic power management up until the transmitter was behind a deep forest and could no longer communicate after which the LCD reported a failure.

A button to increase radio battery life

Demo source on Github: https://github.com/Cloud-RF/CloudRF-API-clients/tree/master/integrations/AGC%20demo

Best site analysis

Problem

Radio networks have signal dead spots caused by topography like a valley or clutter like buildings and trees. When a VHF/UHF radio operator finds themselves without communication (if they’re aware) they will resort to their training and experience to remedy the issue. These remedies can be technical such as adjusting transmit power, antenna configuration or elevation, all of which will improve a signal, or physical such as moving.

Operators are taught to attempt an initial movement of at least a wavelength (10m at 30MHz) to overcome multi-path effects where out-of-phase reflected signals cause nulls followed by bigger movements. Depending on the scenario, this could easily result in an operator making unnecessary, and potentially risky, movements to acquire a signal. Each movement would be a guess based on local observations of terrain…

Solution

Using our “Points” API, we perform multiple tests in all directions around the radio’s position to a distant station. As before this could be a repeater or a recent neighbour node. The points API is very efficient and takes an array of positions for which it returns an array of simulated values. With this response we can determine the best location to move to.

This search is conducted at an expanding search radius until a signal is found. A signal is defined as being above our desired threshold for the system, in this case -90dBm. This result is presented to the operator as a short message composed of a bearing, distance and expected signal strength.

If the operator wants to take control of the search, they can do this using the keypad and virtually walk around the area as a grid. Results for each grid are presented to the operator via the LCD so they can find out if a hill is worth climbing without climbing it.

A button to find a better location for a radio

A look forward

More radios are featuring APIs which allow for dynamic control of the radio, and eventually the network. APIs are the key to enabling a truly cognitive network and we have the modelling API, 11 years in the making, capable of supporting the scale, speed and accuracy needed which can be installed on ARM via a container. As a look forward, we will be installing this on a Samsung S21 where aside from powering our ATAK plugin, we will be able to do powerful modelling capabilities at the edge without a network – or a heatmap.

If you are a hardware OEM interested in enhancing your product please get in touch.

Posted on 2023-10-152023-10-15

Planning for noise

The trouble with radio planning software

Radio planning software has a patchy reputation. Regardless of cost, the criticism, especially from novice users, is generally that results “do not match the real world”. The accuracy of modelling software can be improved with training, better data, tuned clutter etc but if you do not plan for the local spectrum noise, it will be inaccurate.

The reason modelling does not match the real world, is the real world is noisy, and noise is everything in digital communications. Spectrum noise will limit your network’s coverage and equipment’s capabilities. A radio that should work miles can be reduced to working several feet only when the noise floor is high enough.

Anyone expecting simulation software to produce an accurate result without offering an accurate noise figure will be forever disappointed as software cannot predict what the noise floor is in a given location at a given moment – you need hardware for that…

Spectrum sensing radios

Modern software defined radios are capable of sensing the noise figure for the local environment. This allows operators, and cognitive radios, to make better choices for bands, power levels and wave-forms as narrow wave-forms perform better in noise than wider alternatives due to channel noise theory where noise increases with bandwidth.

For example, you can have a radio capable of 100Mb/s but it won’t deliver that speed at long range, at ground level, as it requires a generous signal-to-noise margin to function. This is why a speed demo is always at close range.

When spectrum data is exposed via an API, like in the Trellisware family of radios, it provides a rich source of spectrum intelligence which can be used for radio network management, and dynamic RF planning with third party applications. When we integrated this radio API last year, we were focused on acquiring radio locations, not spectrum noise. At the time we could only consider a universal noise floor value in our software so the same noise value was applied for all radios which was vulnerable to error as radios in a network will report different noise values.

Interference: a growing issue

The single biggest communications problem we hear about, from across market sectors, is interference, either deliberate, accidental, or just ambient like in a city. The number of RF devices active in the spectrum, especially ISM and cellular bands, is increasing and in markets which were relatively “quiet”, such as agriculture. Some have always been problematic, such as motorsport, where the noise floor increases significantly on race days.

Spectrum management is a huge problem which won’t be fixed with management consultants or artificial intelligence. Regulators can, and are, restructuring spectrum for dynamic use but to use this finite resource efficiently, hardware and software vendors need to publish APIs and competing vendors need to be incentivised to work to common information standards.

As noise increases, the delta between low-noise RF planning results and real world results has the potential to grow. There’s anecdotal evidence that some private 5G network operators are experiencing so much urban noise they’ve given up on RF planning altogether, and have opted to take their chances using a wet finger and local knowledge. Skipping RF planning is a managed risk when a company has experienced staff (or they get paid for failure), but it does not scale and is a significant risk when working in a new area and/or with inexperienced staff.

A solution: The noise API

To address this challenge, we’ve developed a noise API to eliminate human error, and guesswork for noise floor values which has undermined the reputation of “low-noise” radio planning software.

Manual entry can now be substituted for a feed of recent, or live, spectrum intelligence to enable faster and more accurate network planning. Combined with our real-time GPU modelling, the API can model coverage for moving vehicles, with real noise figures.

There are two new API requests in v3.9 of our API; /noise/create; for adding noise, and /noise/get; for sampling noise. The planning radius is used as a search area so you can upload 1 or thousands of measurements, private to your account. The planning API will reference the data, if requested, and if recent (24 hours) local noise is available for the requested frequency, it will sample it and compensate for the proximity to the transmitter(s).

When no noise is available within the search radius, an appropriate thermal noise floor will be used based on the channel bandwidth and the Johnson-Nyquist formula. The capability can be used by our create APIs (Area, Path, Points, Multisite) by substituting the noise figure in the request eg. “-99” for the trigger word “database”.

{
  "site": "2sites",
  "network": "MULTISITE",
  "transmitters": [
    {
      "lat": 52.886259202681785,
      "lon": -0.08311549136814698,
      "alt": 2,
      "frq": 460,
      "txw": 2,
      "bwi": 1,
      "nf": "database",
      "ant": 0,
      "antenna": {
        "txg": 2.15,
        "txl": 0,
        "ant": 39,
        "azi": 0,
        "tlt": 0,
        "hbw": 1,
        "vbw": 1,
        "fbr": 2.15,
        "pol": "v"
      }
    },
    {
      "lat": 52.879223835785716,
      "lon": -0.06069882048039804,
      "alt": 2,
      "frq": 460,
      "txw": 2,
      "bwi": 1,
      "nf": "database",
      "ant": 0,
      "antenna": {
        "txg": 2.15,
        "txl": 0,
        "ant": 39,
        "azi": 0,
        "tlt": 0,
        "hbw": 1,
        "vbw": 1,
        "fbr": 2.15,
        "pol": "v"
      }
    }
  ],
  "receiver": {
    "alt": 2,
    "rxg": 2,
    "rxs": 10
  },
  "model": {
    "pm": 11,
    "pe": 2,
    "ked": 1,
    "rel": 80
  },
  "environment": {
    "clm": 0,
    "cll": 2,
    "clt": "SILVER.clt"
  },
  "output": {
    "units": "m",
    "col": "SILVER.dB",
    "out": 4,
    "res": 4,
    "rad": 3
  }
}:

In the example JSON request above, two adjacent UHF sites are in a single GPU accelerated multisite request. The sites both have a noise floor (nf) key with a value of “database”. Noise will be sampled separately for each site.

Demo 1: Motorsport radio network on race day

The local noise floor jumps ups significantly on race day compared with the rest of the time making planning tricky.

Demo 2: Importing survey data to model the “real” coverage across a county

By importing a spreadsheet of results into the API, we can generate results sensitive to each location.

A look forward to cognitive networks

Autonomous cognitive radio networks require lots of data to make decisions.
Currently, they can use empirical measurements of values such as noise to inform channel selection and power limits at a single node.
What they cannot do is hypothesise what the network might look like without actually reconfiguring. To do that requires a fast and mature RF planning API, integrated with live network data. Only then can you begin to ask the expansive questions like, which locations/antennas/channels are best for my network given the current noise or the really interesting future noise whereby the state now is known but the state in the future is anticipated.

As our GPU multisite API can model dozens of sites in a second, the future could be closer than you think…

References

API reference: https://cloudrf.com/documentation/developer/swagger-ui/

Hosted noise client: https://cloud-rf.github.io/CloudRF-API-clients/integrations/noise/noise_client.html

GPU multisite racetrack demo: https://cloud-rf.github.io/CloudRF-API-clients/slippy-maps/leaflet-multisite.html

Posted on 2023-04-272023-04-28

Live noise floor modelling

“I’ve never seen modelling that matches the real world”
Anon

Noise in the RF spectrum is a growing issue. It undermines the performance of systems and requires careful spectrum management and deconfliction to mitigate its impact. Sources of noise can be natural, industrial or man made. Regardless of the source, or intent, the issue needs understanding and dealing with to operate effectively in congested spectrum.

Radio users working in cities know about this problem all too well. They can describe the symptoms but are unable to visualise the full impact, and in doing so realise a solution such as an adjustment, through a lack of tooling. Many organisations have bought receivers for use at the edge, and back office software for planning but the two rarely meet…

Noise floor and SNR

The noise floor describes the average minimum RF power in the spectrum. There are different ways to measure it, and depending on the requirement you may want the peak power but for our purposes we are using the mean dBm value within the channel. A quiet noise floor value is -120dBm, subject to bandwidth. The higher this value, the less range you are communicating.

Signal to Noise Ratio (SNR), measured in dB, describes a signals power above the noise. A good signal might be 20dB above the noise and a weak signal only 3dB. Different signals have different SNR requirements; GPS for example uses a BPSK signal with a very low 2dB requirement so it’s barely visible in the noise whereas a DVB QAM signal needs a prominent 20dB SNR to deliver an error free video signal.

In CloudRF, both noise and SNR can be defined to simulate different environments and different waveforms.

A good guess – Johnson-Nyquist noise

Without the presence of man made signals, the RF spectrum has a natural noise floor called the thermal noise floor. This can be calculated with temperature (noise increases with temperature) and bandwidth (More bandwidth equals more noise).

Noise dBm = -173.8 + 10 log10(Frequency in Hertz)

Calculators exist to compute this value based on the Johnson-Nyquist formula. We use this in our interface so when you change bandwidth the noise floor is set. This is a good start, consider it a 75% guess, but is not the real noise. To get that you need to go to that place and measure it.

Measuring noise with an RFeye Node

To measure the noise floor accurately you need a high quality receiver. Low quality SDR receivers are easy to come by but will not be able to give you a more accurate noise value than the previously mentioned bandwidth formula.

The CRFS RFeye Node is a high performance RF receiver with an excellent dynamic range, industry leading low noise figure, and sensitivity. The API enabled receiver is in use worldwide for remote spectrum monitoring making it an ideal candidate for integration, especially since it has open source client scripts!

Integration with the CloudRF API

We imported the NCP client library into our open source API client so we could query the noise for our target frequency and bandwidth. Every time our script processes a site, the noise is tested and the result spliced into our site request.

In return we get a model which uses a real noise floor value. Typically this is higher than the formula method resulting in reduced coverage.

The beauty of this integration is the receiver can be in another county but the modelling can be conducted with high precision from home. With the scalability of the API it unlocks several possibilities:

Model a spreadsheet for a large network, and sample noise floor from local receivers instead of using a generic best guess value
Model a route for a drone with different noise values along the route. If you’ve ever lost communications with a distant 2.4GHz drone that had LOS this was likely WiFi noise
Model a radio and/or a waveforms performance in a remote location without visiting or deploying equipment which is expensive and time consuming

Demo video

In this video we put it all together to incorporate live noise into our modelling. We’re executing one site at a time, but with a spreadsheet, the API client will automatically process a network of sites.

Dynamic noise floor modelling with a CRFS RFeye receiver

References

RFeye python library https://github.com/CRFS/python3-ncplib

CloudRF python script https://github.com/Cloud-RF/CloudRF-API-clients/tree/master/integrations/CRFS

CloudRF API reference https://cloudrf.com/documentation/developer/

Posted on 2022-12-162022-12-18

Multisite API

Today we have launched a new GPU accelerated API called “Multisite” which is capable of modelling hundreds of sites in the time we previously would have done one. This exponential increase in capability grew from our Best Site Analysis (BSA) capability which uses a Monte Carlo technique to test hundreds of random locations, and from the intermediate outputs, reduce them to reveal the optimal location(s) for radio communication.

The difference in Multisite is that the locations are not random, they are defined by the client.

A link is not coverage

Following customer feedback on our MANET planning tool which up until now created point-to-point links between nodes we investigated the feasibility of showing all the coverage, or in classic modelling terminology a point-to-multipoint aka a heatmap.

The reason this is important in MANET planning is you need to see the whole network coverage to understand where you are serving and where your gaps are. Links won’t show you coverage gaps unless it involves the node location so you will find out about these when it’s too late. Links are useful for seeing a network’s geometry and overall health but are of limited use for tactical RF planning where the network members are expected to be agile.

A bonus feature is that a heatmap scales better than links which get very cluttered in an interface when many overlap. A heatmap is much easier for a human to digest than a game of radio kerplunk which can overload an operator.

Faster, cleaner more scalable MANET planning

As this is GPU powered it is faster than the previous CPU powered link calculations. By removing the links, the view is less cluttered for busy networks and this can scale to a significant number of nodes in a single request.

During testing we were modelling 100 nodes in under 2 seconds, which includes communication latency to a production server in Europe. We achieved 3.1 seconds for 200 nodes at 10m resolution with 1km radius each and are confident we can do 500 nodes, like BSA does, and reduce the time further with infrastructure adjustments and a local server. Most of the delay is in data preparation and post processing, the actual computation is milliseconds so real-time modelling for multiple fast moving tracks is viable.

The heatmap exists in your archive as a standard layer so can be exported to KMZ, KML, GeoTIFF, SHP, URL or HTML via the normal archive functions.

Urban demo, NYC

In this early demo, MANET nodes are manually placed and adjusted. The coverage heatmap and links automatically update as nodes are added, removed or moved.

Multisite demo with MANET radios in a Urban environment

How do I enable the heatmap?

If you have a Gold account, open the MANET planning tool from the top bar then click the cog on the MANET window which appears. In the tools options, you can select either links, heatmap or both.

Documentation

The capability is available in API and UI version 3.7.

User docs: https://cloudrf.com/documentation/

Developer’s reference: https://cloudrf.com/documentation/developer/

A Gold subscription or a private server is required to use the MANET heatmap and/or the Multisite API which powers it.

This restriction does not apply to the MANET links and the points API which that uses.

Developer’s guide

A multisite request uses the same JSON structure and values as a area request only instead of a single transmitter object, it has an array of transmitters with no upper limit.

Each transmitter can have a different antenna so you could define several omni directional nodes with a single distant directional node, a common MANET design.

In this example, two MANET radios with 20MHz bandwidth in SNR mode are modelled together. The transmitters differ in location and power but the receiver, environment, model and output sections and common.

{
  "site": "TEST",
  "network": "MYNET",
  "transmitters": [
    {
      "lat": 51.773826,
      "lon": -2.403563,
      "alt": 1.5,
      "frq": 1350,
      "txw": 1,
      "bwi": 20,
      "ant": 0,
      "antenna": {
        "txg": 2.15,
        "txl": 0,
        "ant": 39,
        "azi": 0,
        "tlt": 0,
        "hbw": 360,
        "vbw": 360,
        "fbr": 2.15,
        "pol": "v"
      }
    },
    {
      "lat": 51.77450,
      "lon": -2.392245,
      "alt": 1.5,
      "frq": 1350,
      "txw": 2,
      "bwi": 20,
      "ant": 0,
      "antenna": {
        "txg": 2.15,
        "txl": 0,
        "ant": 39,
        "azi": 0,
        "tlt": 0,
        "hbw": 360,
        "vbw": 360,
        "fbr": 2.15,
        "pol": "v"
      }
    }
  ],
  "receiver": {
    "alt": 1.5,
    "rxg": 2.15,
    "rxs": 5
  },
  "model": {
    "pm": 1,
    "pe": 2,
    "ked": 1,
    "rel": 95
  },
  "environment": {
    "clm": 0,
    "cll": 0,
    "clt": "Minimal.clt"
  },
  "output": {
    "units": "m",
    "col": "SNR.dB",
    "out": 4,
    "nf": -100,
    "res": 5,
    "rad": 2
  }
}

A look forward

Now that we’ve published the API, the fun part of making interfaces for it has already started. The first of which is the enhancement to the MANET planning tool. Expect to see more interfaces and demos, at scale, shortly covering moving vehicles, large networks and ATAK integration where we plan to redefine the meaning of “radio check” with a whole network coverage map.

If you are an integrator or developer of a map and are interested in incorporating this unique layer, get in touch and save yourself years of R&D. White labeling options available.

Posted on 2022-11-28

System updates – 3.6 (Nov 22)

Here’s a roundup of what’s changed since June. As always it’s been a busy period of development which we’ve matched with a focus on maintenance and bugfixes. Our backlog of feature and change requests stands at 28 for the UI and 23 for the API with all priority issues resolved.

The deck is now clear for a dynamic network planning feature which will be integrated in December.

UI changes, June to November

With 19 new features/improvements and 69 bugfixes in this period, the user interface has matured considerably due in large part to user feedback from the community. We’ve said no to a few feature requests also which is necessary to keep the interface technology agnostic and easy to use. That doesn’t mean we can’t support complexity or special waveforms directly in the API.

Radius increased to 400km for aircraft (3.0)
GPU engine integrated with best server and CSV analysis tools (3.0)
New 300m resolution for airborne broadcasting (3.0)
Received Signal Received Power (RSRP) option added to measured units (3.1)
Account information dialog (3.2)
GPU Best Site Analysis (3.2)
Custom WMS and WMTS map sources (3.4)
Satellite database updated (3.4.2)
Multi azimuth antenna patterns (3.5)
Route, multipoint and path path tools updated for all possible measurement units (3.5.1)
JSON templates system (3.6)

The long surviving Google Earth interface lives on and has been given a fresh wind thanks to code compatibility libraries which mean we no longer have to worry about breaking it with new Javascript. Long live Google Earth!

UI Changelog

## Version 3.6.1 (2022-11-28)

- Fix: Layer from archive would not load when clicking on the layer name or selecting "Add tower(s) only to map" button.
- Fix: Processing modal window getting stuck when using the Interference or Super Layer tools.
- Fix: Display the power of items loaded from the archive in the selcted power unit.
- Fix: Dynamically move navigator controls down to make space for imagery layer list.
- Fix: Prevent crash when disabling path profile analysis before calculation completes.
- Fix: Refreshes displayed clutter after deleting all clutter using the clutter manager.
- Fix: Selecting a template in the Google Earth interface crashes.
- Fix: MANET nodes sometimes clipped by the terrain resulting in partially hidden markers.
- Fix: Best site analysis tool sometimes goes into "zombie" state under bad network conditions.

## Version 3.6 (2022-11-10)

- Feature: Templates functionality makes use of the new API endpoints and JSON files.
- Feature: Added button to download the JSON template for both custom and system templates.
- Feature: Added modal to add/remove favourite system templates.
- Feature: Dropdown select of templates indicates if the template is a system or custom template by prepending with either "System" or "Custom" respectively.
- Fix: Styling issues on custom template save/delete modal.
- Fix: Reorganised "My Account" modal as was becoming cluttered. Logout button moved to top and managers split into a single row. API usage moved to the bottom.
- Fix: Some API errors which are returned as an array of values would not be parsed as an array, instead the error displayed would be a generic "Request failed." message which wasn't indicative of the error.
- Fix: Don't fire a GPU request when loading a GPU template.
- Fix: Adjusting the `rxs` field manually would not update the receiver sensitivity slider.

## Version 3.5.1 (2022-10-31)

- Feature: Route, Multipoint and Path tools able to display SNR, dBuV and RSRP units as well as dBm.

## Version 3.5.0 (2022-10-18)

- Feature: Multi-azimuth antenna support by sending a comma-separated list of values, for example, `0,120,240`.

## Version 3.4.2 (2022-09-21)

- Update: Satellite database updated to 2022-09-22.
- Feature: Fly to PPA when loading from the archive.
- Fix: Custom pattern vertical beamwidth not visible in the Google Earth interface.
- Fix: Profile drop down not populated and opening the clutter manager in the Google Earth interface.
- Fix: Sensitivity is loaded when selecting a template.
- Fix: Map attribution interfered with satellite timeline controls.
- Fix: Loading PPA from archive and then hovering over chart would cause a crash.

## Version 3.4.1 (2022-09-14)

- Fix: Template saves coax type.
- Fix: Problem toggling the azimuth mode in the Google Earth interface.
- Fix: Problem loading a template in the Google Earth interface.
- Fix: Switching between templates with engine set to GPU and CPU runs area calculation with incorrect engine.
- Fix: Saving a custom map URL cuts off part of URL after an "&".
- Fix: Suspends error logging when library blocked by client.
- Fix: Handle missing preferences.

## Version 3.4.0 (2022-08-31)

- Feature: Custom WMS and WMTS URLs for imagery layers.
- Feature: Attribution text lists API providers.
- Fix: Pruned dead and unsuitable map sources, such as "Blue Marble".
- Fix: Problems with editing coordinates.
- Fix: Antenna patterns not loading properly in some browsers.

## Version 3.3.0 (2022-08-23)

- Feature: Sets free space model with diffraction off and shows warning for best site analysis.
- Feature: Select last network when opening archive to match the last row.
- Feature: Make clutter go to ground.
- Fix: Improved area calculation error handling.
- Fix: Selecting a network in archive drop down when some items have no network would cause a crash.
- Fix: Legacy colour key images not displayed.
- Fix: Super layer KMZ quick-download button fixed for "Merge network"

## Version 3.2.0 (2022-08-15)

- Feature: Account Information dialog.
- Feature: GPU Best Site Analysis.
- Fix: Updates to colour schemas to match recent changes to colour schema API.
- Fix: Buildings layer cannot be removed.
- Fix: Receiver sensitivity icons missing.
- Fix: Creating a clutter profile does not refresh the list.
- Fix: RSRP breaks MANET.
- Fix: Opacity slider doesn't work.
- Fix: MANET reload by name click and best server for imperial units mi/f.
- Fix: Crash clicking layer checkbox while layer is loading.
- Fix: Default RSRP schema not always selected.
- Fix: Interference tool is trying to map colours to colour key.
- Fix: Crash saving clutter polylines with less than 2 coordinates or polygons with less than 3 coordinates imported from kml.
- Fix: Crash downloading MANET metrics report when node evauation failed.
- Fix: Power unit in MANET node details dialog displayed as undefined.
- Fix: Node percentanges on MANET metrics report sometimes NAN.
- Fix: Points CSV validation no longer works.
- Fix: Entity already exist in collection when disabling MANET.
- Fix: Crash adjusting bsa sliders when result loading failed.
- Fix: BSA images not recoloured on colour schema changed.
- Fix: Custom antenna polar plot not displayed in Google Earth.

## Version 3.1.0 (2022-07-18)

- Feature: Added Received Signal Received Power (RSRP) as an option to the "Measured Units" dropdown.
- Fix: Deleting multiple selected networks will now give the name of the networks rather than the ID.
- Fix: Selecting a PSK/QAM modulation will only show supported PSK/QAM bit-error-rate values, and likewise when selecting a LoRa modulation.
- Fix: Deleting a network from the archive now gives a better modal message rather than a browser `alert()` window.
- Fix: Occasional crash when computing azimuth and tilt for the mouse tootltip for satellite mode when the position has not been computed yet.
- Fix: Can't find variable `Map` in Google Earth interface.
- Fix: Crash in Google Earth when logging error information.
- Fix: Prevents crash in Google Earth interface when changing clutter profiles.
- Fix: Formatting issue in Google Earth.
- Fix: Helper dialog describing channel noise adjustment when changing bandwidth
- Fix: Tx Height AGL and RF Power textboxes extending over unit drop downs.
- Fix: Crash in Google Earth interface when you change properties that would update the map location when in web UI.

## Version 3.0.1 (2022-07-06)

- Feature: Added support for 180m and 300m resolutions.
- Fix: GPU is populated in dropdown of processing engines when GPU isn't available.

## Version 3.0 (2022-07-04)

- Feature: Radius limit now 400km.
- Feature: GPU processing no longer handled through WebSocket, instead goes through the same API as CPU processing for simplicity. Removed button to close GPU processing engine, instead you now just switch back over to CPU mode from the dropdown in the "Output" menu.
- Feature: Integration of GPU engine with other tools including best server and CSV import.
- Fix: Colour schema list gets populated with some empty values when using GPU mode.
- Fix: Prevents failure on area calculation due to invalid input from logging an error.
- Fix: Interference dialog not always populating networks.
- Fix: Duplicate entry for gpu in the engine drop down.
- Fix: Some antenna patterns were not being saved in templates.

API changes, v3.0 June to 3.6 November

With 30 improvements and features and 28 bugfixes, the API has largely remained the same from a developer’s perspective but it now has more consistency between the CPU and GPU engines, more choice with defining antenna azimuths and more useful error messages to help developer’s make better choices and save time.

Under the hood, the ATAK interface has had a major refactor to support customer’s official TAK Server’s with our infamous chatbot. A new chatbot management interface has been especially created which can validate certificate chains using OpenSSL to warn of issue before you get to them eg. “This certificate is invalid” or “This key is incorrect” or “Cannot ping your TAK server”.

GPU integration

The new GPU engine has received several updates throughout this period to add models, gains, antenna patterns, multi-azimuth patterns and output styling inline with the CPU engine, SLEIPNIR.

In September, the powerful Best Site Analysis capability was integrated with ATAK. When the SOOTHSAYER bot is connected to the TAK server, any polygon sent to it will generate a BSA layer.

Due to demand, the maximum radius for the GPU engine was increased to 150km to support broadcasting. During GPU testing we were generating 100 mega pixel (100,000,000 points) heatmaps in a few seconds which is too large for a client. We are working on a scalable solution to visualise images of this resolution as an image pyramid.

SLEIPNIR mods

The SLEIPNIR CPU engine is now at version 1.6.1 and has conditional smoothing of coarse DTM, knife edge diffraction from clutter (note we already do this for surface models) and slightly (3dB) more optimistic diffraction following feedback from a mountain rescue team.

Received Signal Received Power (RSRP) is not native to the engine instead of the API and the reliability variable can now be used for non-ITM models to provide a 10dB tuning margin where 50% is 0dB and 99% is 9.9dB path loss.

The maximum radius has been increased to 400km for airborne platforms. To support the larger radius, we’re also offering a new low resolution of 300m.

API management

The V1 “URL” API will be retired at the end of this year. Users who have integrated with this API should migrate to the new JSON API as soon as possible. The new JSON API has been powering the web interface for almost two years.

Reference: https://cloudrf.com/documentation/developer/

API Changelog

## Version 3.6.0 (2022-11-25)

- Deprecation: Added deprecation message to `v1` API endpoints. The `area` and `path` will be removed on the 31st December 2022. Please migrate any scripts to the new JSON API and see https://cloudrf.com/documentation/developer/
- Fix: Chatbot welcome-storm with other chatbots.
- Fix: Obsolete paths on terrain coverage map and HTML PPA response.
- Fix: Improvements to automation testing.
- Fix: Sending malformed JSON to a `v2` API endpoint now returns a useful error message.
- Fix: Deprecated `v1` API endpoints were incorrectly counting API credits.
- Fix: Power value of 1mW for GPU fails.

## Version 3.5.0 (2022-11-10)

- Feature: Moved from database-driven to JSON-driven templates with new API endpoints to manage custom templates.
- Feature: System templates can be retrieved and saved as a favourite using new endpoints.
- Fix: Race condition on GPU processing files as its so damned fast.
- Fix: Public shares for GPU layers needed reprojecting to EPSG 3857.

## Version 3.4.0 (2022-10-18)

- Feature: Gain support for GPU engine.
- Feature: Antenna support with azimuth and tilt for GPU engine.
- Feature: Multi-azimuth support for antennas by sending a comma-separated list of values in the `antenna.azi` value, for example, `0,120,240`.
- Fix: Path tool on TAK successfully returning only about 50% of the time.
- Fix: Area requests to `v1` API returning colour key data in improperly formed string values.
- Fix: `clutter` command on TAK chatbot working again.
- Fix: Resolved issue with custom colour schemas and low resolution screens.
- Fix: Sanity check ADF files.
- Mod: Default clutter templates adjusted. Urban height reduced to no more than 2m.
- Mod: GPU engine max radius capped at 10km (CPU max = 400km).

## Version 3.3.0 (2022-09-22)

- Feature: Support for TAK server 4.7 through Chatbot and ATAK.
- Feature: Extended response information when executing `id` to Chatbot through ATAK.
- Feature: Support for GPU BSA on Chatbot with the multipoint tool through ATAK.
- Feature: Added new chatbot manager to spin up chatbots on demand which interact between TAK servers and API.
- Feature: Extended preferences functionality to allow for custom imagery.
- Feature: Delete all clutter button added to clutter form.
- Fix: Processing engine saved in templates.
- Fix: Commercial restrictions messages now return HTTP 403 (Forbidden) rather than HTTP 400 (Bad Request).

## Version 3.2.0 (2022-08-15)

- Feature: System colour schemas no longer able to be deleted by a user so that users will always have a list of schemas to choose from.
- Feature: Height ceiling increased to 120km over 60km.
- Feature: When using Greyscale GIS colour schema no longer returns validation error for mixing outputs and schema.
- Feature: Improvements to the PPA KMZ export with fresnel support and new icons for transmitter, receiver and obstructions.
- Fix: Improvements to the functionality of the colour schema generator.
- Fix: Creating a BER colour schema fails with a HTTP 500.
- Fix: Update to `preferences` stripping out underscores from colour schemas.
- Fix: Google Earth layer no longer allows to manage colour schemas. Added notice with workaround instructions.

## Version 3.1.0 (2022-07-18)

- Feature: Improvements to `preferences` to extend to additional values and give information about a users plan.
- Feature: Improvements to precendence of preferences, where in some cases a previous request values will be taken over a users preferences.
- Feature: Colour palette bucket limit increased to 75.
- Feature: Return request to `preferences` as a correct JSON response.
- Feature: Users with a private server will now be indicated when they make a request to `preferences`.
- Feature: Colour pallette creator extended to support Bit-Error-Rate measured units.
- Feature: Bit-Error-Rate modulation extended from 10e-6 to 10e-8.
- Feature: Allowed support for Received Signal Received Power (RSRP) measured units with an `out` value of `6`.
- Feature: Added default RSRP colour key.
- Feature: SLEIPNIR 1.6.1 calculates and returns (LTE) RSRP if selected
- Feature: SLEIPNIR 1.6.1 uses reliability for non-ITM models with a 10dB range. 50% = +0dB, 99% = +9.9dB
- Fix: Successful deletion of networks in the archive are returned with a success `message` rather than `error`.
- Fix: Correct names of plans when making a request to `preferences`.
- Fix: BER colour palette in JSON response was missing unit.
- Fix: Improvements to the default `PATHLOSS.dB` colour key.
- Fix: Hata model high-plateau issue resolved in SLEIPNIR 1.6.1

## Version 3.0.1 (2022-07-06)

- Feature: Resolution supported up to 300m.

## Version 3.0 (2022-07-04)

- Feature: Improvements to GPU processing with the use of remote processing nodes.
- Feature: GPU produced images are now styled in the API based on the specified colour schema.
- Feature: Extended `data` API to retrieve a tile based only on tile name.
- Fix: Logic issue with users mixing credits and plan was incorrectly refusing service to users with expired plans, but spare credits.
- Fix: Custom clutter profiles are created with the list of 101 to 199 clutter codes.
- Fix: Obtaining height data from OpenStreetMaps would sometimes fail.
- Fix: KMZ download failing on equator

That’s all folks.

Posted on 2022-06-222022-06-22

Dynamic network planning with hardware APIs

Location aware radios

Modern digital radio systems often have Application Programming Interfaces (APIs) for remote management. They also commonly have Global Navigation Satellite System (GNSS) modules for location awareness which enables “network maps” of where the nodes are. These vendor maps are great at showing where nodes are now (provided they are in coverage!) but what they lack is the ability to plan where nodes could be moved to.

When you combine these key features with our open standards modelling API you get a powerful new capability which allows you to observe the network now, and by moving or adding a simulated node, see the network in the future.

Case study – Trellisware

Trellisware^TM are a market leader in Mobile Ad-hoc Networks (MANET) whose versatile radios work where others fail due to their spectrum efficient TSM waveform. It’s currently in service with Government, Commercial and First responder markets.

The radios have a comprehensive API and integrated software services which allows for remote operation on a IP based network.

We were loaned some TW-950 Shadow radios to explore API integration. In a few days we were able to create an API client (in Python) to interface between the Trellisware API and our RF modelling API on SOOTHSAYER 1.3 which has a MANET planning tool.

Our Python client would interface with the radio network via the Ethernet connector on the side of a TW-950 radio from where the API would expose node metadata.

We would fetch this metadata (as JSON) and package it into a JSON document compatible with our Points API, which powers our MANET and route analysis tools. In the interface’s MANET tool, a new ‘play’ button pulls in the network document and models it through our fast API. Each link is tested using the real parameters, with minimal user interaction.

The flowchart is depicted below.

Ethernet adapter for connecting the hardware to SOOTHSAYER

Once the data is displayed in the SOOTHSAYER interface, an operator can choose to move a node by dragging it or add an entirely new node, using settings of their choice, into the mix. This new node will be modelled alongside the rest of the nodes (many-to-many) to visualise what the impact of the new node will be.

Trellisware (Blue) and CloudRF (Yellow) APIs and components

A faster OODA loop

The Observe, Orient, Decide, Act (OODA) planning loop is an established concept which determines success in fast moving communications environments such as a fire or Police incident.

By combining real-time situational awareness with live modelling we can exercise scenarios and reach sound decisions much faster than by either guessing or using trial and error as is often the case in tactical communications in complex environments. The benefit is faster more efficient use of limited resources.

A Trellisware network map enhanced with two planned nodes to the right.

Demo: Do we launch the drone?

Deploying a drone equipped with a radio is a guaranteed way to fix MANET network issues, especially in cluttered urban environments. It’s resource intensive and risky as the drone is valuable, the radio, and network it enables, is arguably more valuable plus the battery life is very limited so this asset must be used sparingly, which is where planning comes in.

Placing a repeater drone “overhead” is not needed, unless you’re using it for observation also. If the drone has a vertical dipole, overhead would actually be the worst place for it due to the donut shaped antenna pattern as some early OEMs learnt the hard way.

A better place for a communications relay drone is at altitude but on the edge of the target network where it will be at less risk (and will present less risk to net members below it in the event of failure) but it will still be able to serve as an effective repeater.

As recent MANET demos have proven, this repeater could be effective from several miles away.

Demo of dynamic MANET planning in SOOTHSAYER with Trellisware radios

Look ahead

Now that we have a simple standards based, repeatable, method for adding live radios we’ll roll this into future SOOTHSAYER releases, the next of which is 1.4, scheduled for Q3 2022.

Customers can make their own plugins in any language to get their hardware data packaged as a JSON document for either SOOTHSAYER’s MANET tool or direct to the points API which powers it. You can find example scripts in various languages on our Github site and we are available to assist with bespoke integrations.

References

API examples: https://github.com/Cloud-RF/CloudRF-API-clients/tree/master/APIv2

API specification: https://cloudrf.com/documentation/developer/swagger-ui/

Contact

If you are a hardware manufacturer or integrator looking to add this type of capability into either your own map or ours, get in touch as we know what we’re doing and it will save you years of R&D.

Email support@cloudrf.com for more information on how you can achieve dynamic planning quicker.

Posted on 2022-06-152022-06-15

System updates – June 2022

We’ve been busy as always improving our service. Here’s a visual roundup of updates we’ve pushed recently for API version 2.9 and UI version 2.8, current as of the 13th June 2022.

For the latest updates click the versions in the corner of the interface.

New features

Variable building heights

Our global building data has had a lot of attention recently from 5G operators and (counter) drone companies.

Previously you could use a mixture of sources to define a building from manipulating your clutter profile values to adding your own as GeoJSON or KML. Our third party buildings which we offer as an enrichment option in the Landcover menu were lacking height data so we used a user defined value from the clutter profiles. For many users on default settings, this resulted in buildings at 6m height, adequate for a suburb with similar architecture but no use for a vertical city like Chicago.

Our building data now has unique heights and we’ve modified our clutter system to match it. Our SLEIPNIR engine could already handle user defined height data due to previous modifications around custom clutter.

In the CloudRF UI, you can define the 9 system codes and only 9 custom codes.

Customers with private servers can define up to 255 clutter codes in clutter files on their system.

Diffraction for 3D clutter

Previously we modelled single knife edge diffraction only for the surface model, and considered through building attenuation for the clutter but now we can do it for clutter.

In the screenshot below, the COST model is used with 3D clutter for a suburb with a treeline along a train track. Both the buildings and trees are displaying diffraction, recognised by a shadow beyond an obstacle which gradually comes back into coverage as the obstacle angle decreases.

DTM option instead of LiDAR

We’ve worked with LiDAR in modelling for years and its great but has its limitations, especially with non line of sight communications where LiDAR will show a strong signal for the roof of a building and produce a very conservative “shadow” immediately behind an obstacle until knife edge diffraction kicks in.

Being conservative in RF planning isn’t a bad thing but with proper building height data we can enable more accurate through-building attenuation in cities, worldwide.

Nothing beats LiDAR for LOS analysis so we’ll still consider requests for uploading public LiDAR but for most of the world where there isn’t LiDAR, we now have an improved solution.

For rooftop planning, we recommend LiDAR but for through building/tree modelling at ground level use calibrated clutter instead. You can enable this option in the Clutter menu.

You can access this mode in the interface “Clutter” menu or in the JSON API with {clm: 2} in the environment object.

Delete-all custom clutter

API users can now delete all their custom clutter by requesting to delete id=0. This will soon be added to the UI with a button.

Added my-metrics endpoint for API usage

We’re now logging metrics for all the analytics APIs which count against your API use. We have an API to generate charts and will soon add client side charts so you can see how your API use breaks down by tool and time.

Automated testing

Testing is a critical part of maintaining our quality of service which is becoming increasingly complex. We’ve added automated workflows to our development environment to help catch bugs earlier and complement the manual interface testing.

This is implemented for the API and UI repositories at the code function level as well as our regular regression testing at the API level and now third party automated exception handling.

Improvements

Conditional terrain smoothing

We’re smoothing terrain for those super sampled locations. For example if you went to Africa which is mostly 30m DTM, and requested a 2m plot, we would be super-sampling the DTM by a factor of 15 which used to result in ugly artefacts on hillsides. We’ve fixed that and are able to have smooth hills and precision 2m clutter in remote areas 🙂

Rwanda with super sampled 30m DTM and 2m buildings with diffraction

Up to 255 clutter codes

In the CloudRF UI, you can define the 9 system codes and only 9 custom codes but the backend system supports up to 255 unique classes of clutter. You set the height and density for each so this could be skyscrapers for a city or crops.

Customers with private servers can define up to 255 clutter codes in clutter files on their system.

Diffraction loss adjusted down by 3dB

Following feedback from Mountain rescue teams using our service who were surprised to find coverage in modelled dead spots, we investigated our diffraction model and found it was too conservative by at least 5dB. We’ve adjusted the loss it applies down by 3dB so it’s now more optimistic, but still cautiously conservative.

Extended southern limit to -89N

You can model on Antarctica now. We don’t have DTM there so it’s flat as far as our service is concerned but if you are using the Satellite tool, this now works on the continent for testing for the horizon on a route etc.

Fixes

Replaced source for 3D buildings from a commercial supplier to Openstreetmap.org
Points requests was failing to handle some responses from the engine
Returning correct HTTP status codes for errors now
Template list is returned as JSON in the UI
Template authentication has been upgraded to the header “key”
Credits balance was reported incorrectly for some API calls
Remote tile fetching was corrupting some local tiles
Sanity check unworkable paths before passing into engine (eg. 1m or 1000km)
Some JSON responses malformed
Splicing of points near the Rx in a points request works better for a figure of 8 route.
Preferences was breaking if no lat or lon set
Performance improvements to “sea tiles” used for offshore planning.
Changed noise floor validation to -130 to -50dBm
Fixed issue with some interference API calls missing id values

Posted on 2022-02-022022-02-02

4D Satellite visibility tool

Satellite communications are readily accessible, unless you live in a polar, mountainous or dense urban area.

Using our modelling API and third party satellite data courtesy of CELESTRAK, we are able to determine if a chosen satellite is visible from a given point on earth. Unlike “satellite finder” apps, we can provide a lot more than a location in the sky. Our tool can:

Show if the view is obstructed or not
Show the azimuth and elevation toward the satellite upon a 3D map
Consider ground clutter such as buildings and trees
Consider the fresnel zone clearance based upon the frequency (A UHF signal can have a 50m fresnel zone at 10km)
Consider the location at a given time for LEO satellites (Starlink, OneWeb etc)

Route analysis

Satellite_norway — Route analysis in Norway for a equatorial satellite

Points analysis

Time based analysis for LEO

Low Earth Orbit satellites move fast at a relatively low altitude. For this we can select a time.

Searching for a satellite

The CELESTRAK database has every satellite, going back to Sputnik 1 in 1957. We have filtered active satellites which you can search by name or NORAD designator.

Urban?

We have global 3D buildings and 10m Landcover so you can model obstacles. You can also draw your own obstacles using the custom clutter tools.

More information

Instructions for using the tool are in the documentation.

A premium (Silver or Gold) plan is required to use this functionality. If you are an integrator and would like to build your own interface using our powerful modelling API this is encouraged, and will save you years of R&D and expensive hosting.

You can get the satellite TLE data over at CELESTRAK.

API schema is here: https://cloudrf.com/documentation/developer/swagger-ui/

Posted on 2022-01-172023-06-01

GPU propagation engine

Our fast GPU engine is perfect for modelling wireless coverage

We have developed the next generation of fast radio simulation engines for urban modelling with NVIDIA CUDA technology and Graphics Processing Units (GPUs).

The engine was made to meet demand across many sectors, especially FWA, 5G and CBRS for speed and accuracy.

As well as fast viewsheds, it enables a new automated best-site-analysis capability, which will accelerate site selection and improve efficiency whilst keeping a human in the loop. As we can do clutter attenuation, it’s suitable for VHF and LPWAN also.

Designed for 5G

5G networks are much denser than legacy standards due to the 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 (LOS) coverage is essential for performance.

With 1 metre accuracy and support for LiDAR, 3D clutter and custom clutter profiles, you can model rural and urban areas with high precision.

We can do Trees too

Unlike simplistic viewshed GPU tools designed for speed, we can model actual tree attenuation for beyond line of sight sub-GHz signals such as LPWAN and VHF. Trees can be configured as clutter profiles, along with shrubs, swamps, urban areas and 18 classes of Land cover and custom clutter.

Area coverage

The simplest mode is a fast “2.5D” viewshed (with a path loss model) which creates a point-to-multipoint heatmap around a given site using LiDAR data. Ours has better Physics than some of the “line of sight” eye candy on the market and doesn’t have trouble with Sub-GHz frequencies which are harder to model accurately.

This is up to 50 times faster than our multi-threaded CPU engine, SLEIPNIR.

GPU demo January 2022

In this mode we can do diffraction and material attenuation with our custom clutter classes.

Best site analysis

Best Site Analysis (BSA) is a monte-carlo analysis technique across a wide area of interest to identify the best locations for a transmitter. This can be done quickly with a new /bsa API call. The output will identify optimal sites, and just as important, inefficient sites.

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.

Best Site Analysis on ATAK

High speed

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

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 “go” button, or even a progress bar. This speed enables API integration with vehicles and robots which will need to model wireless propagation quickly to make better decisions, especially when they’re off the grid. It was designed around consumer grade cards like the GeForce series but supports enterprise Tesla grade cards due to our card agnostic design.

Economical

Our implementation is efficient by design. We want speed to model wireless coverage but not if it requires kilowatts of power. During testing we worked with older GeForce consumer cards and were able to model millions of points in several milliseconds with less than 50W of power. Or in other words, the same power as flicking a light bulb on and off.

Any fool can buy large cards and waste electricity, but we’re proud to have a solution which is fast and economical. This also means it can be run on a laptop as it’s available now as our SOOTHSAYER product.

Open API

The GPU engine is an “engine” parameter in our /area API so you can use it from any interface (or your own custom 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.

Self-hosted GPU server

Instead of buying milk every month you can buy the cow. We also sell SOOTHSAYER which is a self-hosted server with our GPU engine onboard. It supports NVidia GPU cards from Maxwell architecture onwards and most enterprise Hypervisors like ESXi and Proxmox. You get to use your existing LiDAR data too, so you’re not buying it twice.

To see how easy it is to setup a GPU card with SOOTHSAYER we’ve made a video:

SOOTHSAYER GPU setup

Accessible

Using GPU cards to model Physics, including EM propagation, is an established concept dating back 20 years, despite businessmen claiming otherwise. What is novel here, is making this exciting technology accessible to users priced out of premium tools.

Staying true to CloudRF’s accessible and affordable principles, we’ve included it in our service as an optional processing engine.

Posted on 2021-11-122021-11-12

Enhancing accuracy with environment profiles

In radio planning, accurate terrain data is only half the story.

The other data you need, if you want accurate results, is everything above the surface such as buildings and trees.

This is known as land cover or in radio engineering; clutter data.

Clutter data

In October 2021, the European Space Agency released a global 10m land cover data set called WorldCover with 9 clutter bands.

In our opinion, the ESA data is a sharp improvement on a similar ESRI/Microsoft 10m land cover data set also published this year.

The land cover can be used to enhance coarse 30m data sets to distinguish between homes and gardens, or crops and rivers. It’s space mapped so has every recent substantial building unlike community building datasets which can be patchy outside of Europe.

This data was previously very expensive. A price reflected in the eye watering pricing of legacy Windows^TM planning tools.

The data has 9 bands which have been mapped to 9 land cover codes in Cloud-RF^TM. Combined with our recent 9 custom clutter bands, we have 18 unique bands of clutter which you can use simultaneously.

Read more about the codes in the documentation here.

Explore the data we have on the ESA WorldCover viewer application here:

https://viewer.esa-worldcover.org/worldcover/

Custom clutter enrichment

We have integrated the 10m data into our SLEIPNIR^TM propagation engine which as of version 1.5, can work with third party and custom clutter tiles simultaneously, in different resolutions.

This is significant as it means you can have a 30m DSM base layer, enhanced with a 10m land cover layer, enriched further with a 2m building which you created yourself. Effectively this gives you a 10m global base accuracy with potential for 2m accuracy if you add custom obstacles. The interface will let you upload multiple items as a GeoJSON or KML file.

Demo 1 – The Jungle

Always a tricky environment to communicate in, and model accurately due to dense tree canopies. In this demo, a remote region of the Congo has been selected at random for a portable VHF radio on 75MHz with a 3km planning radius.

This area has 30m DSM which out of the box produces an unrealistic plot resembling undulating flat terrain. This is because the thick tree canopy is represented as hard ground and the signal is diffracting along as if it were bare earth. The result therefore is that 3km is possible in all directions.

By adding our “Tropical.clt” clutter profile, calibrated for medium height, dense trees, we get a very different view which shows the effective range through the trees to be closer to 1km, or less, with much better coverage down the river basin, due to the lack of obstructions.

Demo 2 – A region without LiDAR

Scotland has very poor public LiDAR compared with England which has good coverage at 1m and 2m.

For this demo, Stirling was chosen which has 30m DSM only. A cell tower on a hill serving the town produces an optimistic view of coverage by default but when enhanced with a “Temperate.clt” clutter profile, calibrated for solid and tall town houses and pine forests (eg. > 50N, Northern Europe, Northern USA) we get a much more conservative prediction. As a bonus, the base resolution has improved three fold to 10m.

Demo 3 – A region with 2m LiDAR

You might think if you’ve got high resolution LiDAR data that’s enough. Wrong. Soft obstacles like trees especially will produce excessive diffraction as if they were spiky terrain. This manifests itself as optimistic ‘great’ coverage due to the diffraction coverage. By adding our “Temperate.clt” profile again we make trees absorb power and see where there are nulls in our coverage – beyond the houses and woods.

Despite our land cover being only 10m resolution, we are able to benefit from the full LiDAR resolution with 2m accuracy.

Inspecting a profile

The path profile tool will now show you colour coded land cover as well as custom clutter and 3D buildings. Crops are yellow, grass is green(!), Trees are dark green, built-up areas are red, 3D buildings are grey, water is blue…

The most significant feature in this image isn’t the coloured land cover, or the custom building (as both are features we’ve done before), or the fact we know the tidal river Severn sits lower than the man-made Canal beside it, It’s the fact that both are being used in the same model at the same time. They are different sources, different resolutions, different densities…

Path profile for 860m link showing 2m LiDAR, 10m Land cover and 2m custom building

Using and editing a profile

Once you’ve got the hang of switching profiles you may find it needs optimising for your region. With the clutter manager in the web interface, premium customers can create their own profile based on field measurements for highly accurate predictions. After all no two forests or neighbourhoods are the same density.

Create your perfect profile and save it to your account. The system has 5 regional profiles ready for all users and you can add your own.

To use them, pick from the Clutter > Profile menu and ensure “Landcover” is set to “Enabled”.

If you have created custom clutter and want to use that, set Custom clutter to “Enabled” to blend it in.

For more see the web interface clutter section in the documentation.

Using clutter from the API

We played with a few designs before settling on this very simple template method where you set a profile within the environment menu as follows. This is a new value “clt” and you can still use the existing “cll” and “clm” values to manage the system clutter and custom clutter layers.

JSON request excerpt for a temperate “European” profile, with custom clutter, with 3D buildings and a 3D building density of 0.25dB/m

  "environment": {
        "clt": "Temperate.clt",
        "clm": 1,
        "cll": 2,
        "mat": 0.25
    },

Example for Jungle profile, without custom clutter, without 3D buildings.

  "environment": {
        "clt": "Jungle.clt",
        "clm": 0,
        "cll": 1,
        "mat": 0
    },

What’s next?

Now that we have highly configurable environment profiles. it’s time to tune them with field testing. We’ve bought a heap of comms equipment and will be using it to optimise these profiles with real world measurements.