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3D simulation roadmap

The problem with tunnels and stairs

Whenever there’s been a major incident involving emergency services in complex urban environments the inquiry report has consistently highlighted radio communications failure despite significant developments in radio communications and 3D technology since the infamous 1988 Kings Cross Fire on the London Underground. The following tragic incidents all featured tunnels, stairs and communications failure:

Limitations of (2D) radio planning tools

Radio planning tools are not used in emergencies. They’re complicated, slow and require a lot of knowledge to produce an accurate output. Even if a skilled operator were able to model a site before the event, currently they would be expected to model each floor of a multi-story building in isolation due to the “floorplan” design of current software.

The problem is indoor planning tools are built for corporate clients to achieve seamless Wi-Fi in every corner of the office, not to help a fire chief deploy a mesh radio network down stairs and then along a tunnel. The top end tools can do limited multipath, slowly, but not as an API which can be consumed by a third party viewer…

Most radio planning tools on the market, ourselves included, have the following limitations when it comes to complex urban modelling which we will explore in detail:

Using LiDAR as a 2.5D surface model

The abundance of free LiDAR data has made this high resolution data the standard for accurate outdoor RF planning and for several Fixed Wireless Access (FWA) tools, including free LiDAR based path tools, it is their core feature. We started using LiDAR in 2015 and know its limitations well; for example when point cloud LiDAR has been rasterised into GeoTIFF then it’s no longer 3D, it’s a 2.5D surface model which is useful for building heights and unsuitable for bridges, arches and tunnels.

A bridge or arch in a rasterised LiDAR model extends to the ground like a wall. In the screenshot below, a large ferris wheel is blocking line of sight through it as well as the elevated rail bridge across the river which is casting a shadow much larger than it would in reality.

London eye and bridges in LiDAR

Using a floor plan to model a building

Expect us

For indoor Wi-Fi planning tools, the start point is typically a floor plan. This does not scale well with multi-story buildings or support vertical planning as it produces a 2D image of a 2D plan.

Many tools present 2D images in a 3D viewer, as we do, but the output remains 2.5D, as with rasterised LiDAR. The significant Wi-Fi attenuation presented by solid floors makes this simplified 2D floor-by-floor planning viable for corporate clients in offices but not in challenging environments or where a floor plan does not exist.

Direct ray only

Attenuation is good, reflections are better

Modelling multipath, or fast fading, is much more complex than the direct ray. For this reason, most tools only do the more powerful direct ray and even then some cannot do diffraction or obstacle attenuation as we do already. For the previously mentioned Wi-Fi planning tools, the current standard is to model obstacle attenuation only. By doing this a tool is able to simulate most of the coverage quickly for a given floor but for complete accuracy it must be augmented by a walk survey, which isn’t so quick. For some customers, a walk survey is just not possible.

Multipath effects will increase coverage beyond a direct ray simulation and cause phase issues like signal dead-spots and doppler spread where reflections increase bandwidth and overall noise. This effect can be observed indirectly via customer reviews for urban WISPs where people state their once good link quality reduced as more neighbours subscribed.

A 3D multipath API for 2024

We’ve been working on this full 3D capability since the 2022 Grenfell inquiry with valuable input from firefighters, mining experts and MANET radio OEMs. The first version of the engine is done and we’re onto API integration now.

Our GPU based design takes a 3D model, simulates propagation in all directions irrespective of floors including configurable reflections, surface refractivity, material attenuation and crucially it outputs to the open 3D standard glTF. It scales from small rooms to suburbs and everything in between so will be used for tunnels, multi-story buildings and outdoor multipath.

It will be integrated into our API first so other standards compliant viewers can visualise it and will then be integrated into our own 3D user interface. We can’t say what interfaces people will be using in the future but are confident that by aiming for open standards APIs we will ensure compatibility with phones, glasses and holograms.


Read LiDAR into a 3D volume

Prepare a volume from a LAS/LAZ LiDAR scan.


Direct ray with attenuation

Model direct ray with configurable attenuation in dB/m for obstacles



Model reflections accurately based on the wavelength and angle of incidence


Phase tracking

Track the phase to show constructive and destructive interference (fast fading) eg. dead spots, cured by a little movement 😉


BIM / glTF support

Read and write BIM models as the open standard glTF “3d tiles” format.

Under development

API integration

Integrate engine into the CloudRF API so a BIM/LAS model can be uploaded and used via our standard JSON requests.

Under development

3D tiles web interface integration

Add 3D tiles output to 3D web interface. Some interfaces already supported 🙂

To do

Multisite support

Model many sites at once

To do

Antenna pattern integration

Add 3D antenna pattern loss

Commercial plan

The 3D engine API will be a new feature within CloudRF Gold plans and our SOOTHSAYER server at no additional cost. It requires a GPU. We’re aiming to get a beta up on CloudRF in May/June and to ship this with the next major SOOTHSAYER release, currently scheduled for September.

Users will be allowed to upload models within their storage limits and execution time / accuracy will be scaled to fit within a reasonable time. Limits will be relaxed on SOOTHSAYER.

We are partnering with open standards based companies to integrate this into different viewers. One exciting partner we are working with now is Avalon Holographics. Their revolutionary display is able to display our rich engine output in a hologram format so it can be explored in three dimensions for maximum spatial awareness without additional hardware for viewers.

If you would like to get our open standard glTF models into your viewer, get in touch. If you can bring challenging BIM models or LiDAR scans of real tunnels and large buildings we would really like to talk to you.

Demo video

3D simulation engine demo video

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Field testing diffraction

Spectrum analyser up mountain

Recently, we added advanced diffraction models to CloudRF to complement our existing models. To validate the performance of the new Bullington and Deygout models, we took a field trip to the Highlands of Scotland to collect UHF measurements over rugged mountain terrain and through forests.

With these measurements we have validated and optimised our new models for this environment. We already had single-knife-edge diffraction, based on Huygen’s formula, and the Irregular Terrain Model (ITM) which uses Vogler diffraction. The Vogler model is known to be good but single knife edge has its limits which we have pushed.


The testing validated our investment into the complex multi-obstacle models we have added.

Both new models offer a significant improvement in accuracy, with no loss in performance for Bullington. We were able to model diffraction with higher accuracy over multiple challenging obstacles such as gradual convex slopes, ridges and valleys. Modifications have been made to the CPU and GPU engines which will be updated on CloudRF and SOOTHSAYER in due course.

Our key findings include:

  • Single-knife-edge was optimistic
  • Deygout was the most accurate, but slower
  • Bullington provided the best overall performance
  • 7.6dB accuracy achieved, including receiver error
  • 2.4dB improvement on single knife edge model

Test environment

We selected a famously cold and remote valley in the Cairngorms national park for our test which has cell towers in the valley and a variety of local repeaters for TETRA, VHF and UHF PTT services. The challenging terrain is notoriously difficult for radio communications making it ideal for our purposes.

Using a test phone with 3dB of measurement error attached to the Vodafone 4G network and a portable Rohde and Schwarz spectrum analyser, we collected a variety of VHF and UHF measurements along a 22km circular mountain route covering a wide variety of terrain. From the data collected, the 800MHz LTE measurements proved the best examples of signal failure so we focused our post-analysis on these.

Throughout the LTE testing the phone attached to multiple local cells and experienced prolonged signal failure as expected in a remote mountain valley.

We filtered the results to isolate 634 RSRP readings from a single physical LTE cell, PCI 460, from which we would calibrate modelling. This cell was located at the start of our test route and was a high power LTE band 20 (800MHz) base station with 10MHz of bandwidth.

Trees and attenuation

The first, and last, few miles of the circular route was a mature Scots pine forest. Unlike dense Scandinavian pine forests, this was sparse with a relatively high tree canopy. A lighter tree clutter profile was used to represent the attenuation from these trees which impact UHF propagation.

Convex hill and a loss of signal

Beyond the forest, the route gained altitude into a mountain plateau where line of sight was lost. The shape of the hill meant any diffraction formula would have to model a gradual convex shape versus a simpler knife-edge obstacle.

The ascent and re-acquisition

As the route ascended a spur leading toward the ridge, the signal was reacquired beyond the snowline. This signal gain was gradual, starting as a diffracted signal from the lower convex hill which eventually became a direct signal at the summit, 7km away from the cell.

Summit switcheroo

The route traversed a high ridge which featured many gaps in our cell coverage in the test data. These gaps were because the LTE modem performed a handover to stronger cells which appeared as soon as they were “visible”. Depending upon the position along the ridge, it occasionally reverted to the original “460” cell at over 7km.

Descent into darkness

The steep descent from the ridge entered a obscured valley not visible from the cell.

This resulted in a prolonged loss of signal for several miles until the signal was reacquired toward the trees at the foot of the valley.

Results analysis

The LTE survey data was prepared as CSV and loaded into the CloudRF web interface for use with the coverage analysis tool. This provided live feedback on accuracy with user generated heatmap layers so the correct settings could be identified first visually using a fine colour schema and then numerically by the reported average error in decibels.

Whilst the site location and frequency was known, the power output was not so the first task was to match line of sight positions, such as on the ridge-line, to establish the power without any obstacles. From there, a tree clutter profile was created to match the tree measurements and finally the best model and context were selected. For this task, the generic Egli VHF/UHF model was chosen as a basic model on which to base the diffraction comparison.

As settings matured, the reported Root Mean Square (RMS) error reduced accordingly until it was below 8dB (including 3dB of receiver error). This was slightly better than the 8dB we achieved on our last field test with LTE800 previously and given the extreme context, spanning a diverse mountain range, this was an excellent improvement.

Subtracting receiver error gives modelling error in the range of 4.6 to 7dB; an excellent result for difficult terrain.

Diffraction modelMean error dBRMSE error dBModelling error dBComment
Single knife edge5.2107Optimistic. May show false positive coverage.
Deygout- Can be conservative and is 50% slower but gives high assurance.
Bullington1.48.95.9Good. Can be optimistic but is as fast as KED and relatively accurate.
Calibration results from comparing area coverage with survey data

Coverage results

The scatter plot for the ascent to the ridgeline shows measured and simulated values. The steep drop at 2.5km and gap in results after 3.3km matches closely for the critical beyond line of sight region. The results start again once we ascended toward the ridge where the new models were conservative by 10dB whilst the simple knife edge model tracked the path loss curve – which was to be expected. All models aligned once line of sight was achieved at 6.3km.


The outcome of this testing has improved the accuracy of our diffraction models, identified optimisations for our clutter profiles and proved a simple path loss model can be very accurate beyond line of sight with the right diffraction model.

The API settings we used for the LTE800 cell and RSRP output are here. Note the custom clutter profile and fine colour schema.

    "version": "CloudRF-API-v3.9.5",
    "reference": "",
    "template": {
        "name": "Lochnagar LTE800",
        "service": "CloudRF",
        "created_at": "2024-01-16T13:15:02+00:00",
        "owner": 1,
        "bom_value": 0
    "site": "Site",
    "network": "LOGNAGAR",
    "engine": 2,
    "coordinates": 1,
    "transmitter": {
        "lat": 57.003155,
        "lon": -3.327424,
        "alt": 15,
        "frq": 806,
        "txw": 15,
        "bwi": 10,
        "powerUnit": "W"
    "receiver": {
        "lat": 0,
        "lon": 0,
        "alt": 2,
        "rxg": 0,
        "rxs": -129
    "antenna": {
        "mode": "custom",
        "txg": 19,
        "txl": 0,
        "ant": 0,
        "azi": 180,
        "tlt": 0,
        "hbw": 120,
        "vbw": 20,
        "fbr": 19,
        "pol": "v"
    "model": {
        "pm": 11,
        "pe": 2,
        "ked": 2,
        "rel": 60
    "environment": {
        "obstacles": 0,
        "buildings": 0,
        "landcover": 1,
        "clt": "SCOT4.clt"
    "output": {
        "units": "m",
        "col": "PLASMA130.dBm",
        "out": 6,
        "ber": 0,
        "mod": 0,
        "nf": -120,
        "res": 10,
        "rad": 8


Climbing mountains in winter to test radio networks is dangerous, hard work which requires fitness, experience, skill and dedication to RF engineering. Only do this if you are serious about improving accuracy!

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HF Near Vertical Incidence Skywave (NVIS)

HF NVIS coverage

Today we launched a new model for ionospheric communication planning with High Frequency Near Vertical Incidence Skywave (NVIS).

It’s available in the interface and directly via the area, path, points or multisite API calls. The powerful GPU accelerated capability offers a modern way of visualising and teaching NVIS propagation. It does not, in it’s present form, do frequency selection so this must be performed prior to using this tool to visualise the coverage.


This form of basic ionospheric propagation is popular with Military, Maritime and rural customers. With a simple horizontally polarised antenna and the right frequency, an operator can establish a link of up to 500km making this a quick and economical method for communicating long distances.

HF is undergoing a renaissance driven by uncertainty of the availability of space systems and the need for secondary communications in emergency PACE planning. Despite the choice available now with consumer grade space based communications, HF is a low cost method which requires no third parties making it immune to business and geo-political changes.

As HF bandwidth is very limited, historically only CW and voice channels were viable although developments in compression, cognitive radio and now MIMO are changing this. Improvements in software especially mean that reliable data channels with improved throughput are possible which makes HF data links a popular low cost, low bandwidth, alternative to satellite communications.

Ionospheric propagation

The ionosphere describes layers of ionised gas between earth and space which vary in height between around 100 and 300km. These layers reflect (HF) radio waves and attenuate others. As the layers are stimulated by sunlight, propagation changes significantly between day and night. Seasons affect propagation also, so a frequency which is good in the day may become unworkable after sunset.

The D Layer is the lowest layer at around 100km and absorbs low frequencies (2-4MHz). This weakens at night so these frequencies become viable. This determines the Lowest Usable Frequency (LUF).

The F layer is the highest layer at around 300km and reflects higher frequencies between 4 and 8MHz. The critical frequency is the Maximum Usable Frequency (MUF) which changes throughout the day, determined by sunlight.

A useful analogy for considering the change in the layers is a car engine; It warms up quickly in the morning and cools gradually at the end of a day driving. HF layers change quickly at dawn and slowly after sunset.

Higher frequencies beyond 8MHz experience less refraction so pass through the layers out into space. Depending on conditions a higher frequency may be possible but the most reliable (for NVIS) are found between 2 and 8MHz.

Using the NVIS model

The HF NVIS model can be selected in the model menu or in the API as code 12. Like other models it has a configurable reliability (aka fade margin) and a “context”. The context here refers to the refraction altitude and not an environmental eg. urban/rural choice with other terrestrial models.

  • Context 1 is the D layer at 100km – (Day)
  • Context 2 is the E layer at 200km
  • Context 3 is the F layer at 300km – (Night)

In the day you should use the D layer and your frequency should be between 4 and 8 MHz.

At night, you will use the F layer and need a lower frequency between 2 and 4MHz.

This HF model is only for use with a pre-determined frequency. It does not do forecasting or LUF/MUF frequency selection. This functionality will follow.

The reliability option provides a 10dB fade margin to tune modelling to match the real world. This was set with 50% reliability aligning to summer predictions with a 5MHz frequency.

HF dipole antenna

The antenna pattern will be a special horizontal dipole. You may set the gain and azimuth only but cannot change the pattern as it has high angle nulls for the skip distance before the reflection hits the earth. This will manifest itself as a cold zone at either end of the dipole where the pattern gain is lowest.

This animation shows a dipole orientated north west. The angle of orientation is measured perpendicular (at a right angle) to the wire so the tips of the antenna will generate the worst coverage, in this case to the north east and south west.

HF coverage animation

Radius and resolution

The recommended resolution for NVIS is 180m due to the immense size of the problem. Land cover is irrelevant with this mode of propagation. The radius has been limited to 500km in line with API limits. You can go further with NVIS but would run a risk of straying into multi-hop HF Skywave and this capability is focused on one hop only.

Most NVIS communication takes place between 50 and 300km where groundwave ends and the signal fades into the noise floor.

Using the GPU engine we can model a 500km radius with NVIS and terrain in under 3s. Terrain is a small concern to NVIS unless it’s a large mountain several hundred km away. In this case you will experience shadows due to to low angle of incidence but compared with shadows from terrestrial communications, it will be small.

Environment layers such as land cover and buildings should be off. They will be ignored at 180m resolution.

The colour schema can be whatever you like but if you want to align with the ‘S’ meter scale, popular with HF, where a barely workable signal is S1 and the best is S9 (-73dBm) use a max value of -73dBm with 6dB bands for S9 to S1.

Accuracy verification

We have calibrated our NVIS model to align within 10dB of measurements taken from a 2012 research paper by Marcus Walden using a 5MHz NATO frequency in the UK. From this paper we selected one of the longer links at 210km where we used the median measurement value which for August 2009 was lower during the day than VOACAP, a popular open source application for HF forecasting. The median dBW measurement at noon was -120dBW (-90dBm).

Noting that the RMS error between the VOACAP predictions and the measured values was concluded to be 7 to 12dB at 12 noon (Ref table 7 on page 8), and more at night, we have tuned our model so an “optimistic” prediction is 3dB from the noon measurement. The context and reliability options provide sufficient control to allow predictions to align with current and local ionospheric conditions.

The screenshot below shows both the path and the area coverage aligning with a 1dB calibration schema. The link has over 900m of curvature height gain which explains why a flat region of England appears as a mountain!

HF NVIS calibration
HF NVIS calibration to 3dB

Ionospheric modelling is less predictable than terrestrial modelling due to unpredictable solar radiation. Predictions generated with this model are useful for training, situational awareness and antenna alignment but cannot provide an accuracy greater than 10dB, assuming the inputs are correct.

Look forward: Space weather and long range HF

HF forecasting tools use lookup tables to set refractivity during both seasons and times of day. Using quality, and current data, improves accuracy but like weather forecasting it cannot offer accurate predictions without live data, in this case space weather which has seen a lot of renewed research recently. Our implementation does not use forecasting data presently so users should not be using it to pick their frequencies, but it will help visualise the coverage and align antennas – which at 500km is important.

For the next phase of HF, long range skywave, we will use a space weather feed to offer high resolution HF predictions. Long range HF uses multiple hops at lower angles so the space weather and time of day must be considered along the route which may be thousands of kilometers….

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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…


API reference:

Hosted noise client:

GPU multisite racetrack demo:

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Live noise floor modelling

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


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


RFeye python library

CloudRF python script

CloudRF API reference

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Calibrating beyond line of sight RF modelling with field testing


We field tested our software to improve it for beyond line of sight planning. From analysis of data we have improved diffraction accuracy, clutter profiles and crucially have proven that high resolution LiDAR is not the best choice for beyond line of sight or sub GHz modelling. An RMSE modelling error of 5.2dB was achieved as a result.

Modelling can only be as accurate as the inputs.

Given accurate reference data and accurate RF parameters it can be very accurate but achieving both conditions requires careful and delicate calibration of dozens of variables. Thankfully this time intensive process is only necessary when changing hardware which for most organisations is a cycle measured in years.

The reference data used could be a digital terrain model like SRTM, a digital surface model like ALOS30, high fidelity LiDAR or landcover like ESA Worldcover. As we demonstrate, high resolution does not always translate to high accuracy in beyond line of sight RF.

Calibrating modelling with LiDAR data to match field measurements

LiDAR is great, but it’s not a silver bullet

You can have the most expensive 50cm LiDAR money can buy and still not achieve real world accuracy or a notable gain over 1m or 2m data (unless you’re planning for a model village). LiDAR on its own cannot model beyond line of sight, essential for sub GHz planning, which is a risk we’ll explore when planning tool design is focused on sales and marketing, not actual RF Engineering.

Controversially, you can get better BLOS modelling accuracy with basic terrain data enhanced with calibrated clutter profiles which we’ll demonstrate below.

The best data to use depends on the technology and requirements. LiDAR is unbeatable for line of sight planning, but won’t help you in the woods, or beyond line of sight without a proper physics propagation engine.

Unless your network is composed of static masts eg. Fixed wireless access (FWA), then chances are you are working non line of sight between radios so LiDAR should be used carefully.

Public 1m LiDAR data showing cars, trees and houses

“Line of sight” field testing Feb 2022

Last year we field tested LTE 800MHz in the Peak district and achieved excellent calibration figures for distant hilltop towers looking onto open moorland. This was predictable given the legacy cellular models we used were developed from similar measurements. As the blog described, the harder calibration was inside a wood where the LiDAR data proved unsuitable. Due to the simplistic nature of first return LiDAR, a tree canopy appears as a solid immutable obstacle. You can model the RF as it hits the tree canopy but not where it matters, on the ground inside the trees. This key finding accelerated and matured our developments with tooling to support calibration with survey data in CSV format and user configurable environment profiles.

CSV import utility – developed for analysing field test data
Clutter manager

“Non Line of sight” field testing, Feb 2023

This year we field tested LTE 800MHz again but this time in a old Gloucestershire village, Frampton on Severn, where the tower was deliberately obstructed and the solid stone buildings in the village meant we were measuring diffraction, coming from rooftops of single, double and triple storey buildings. The test data was collected from 2 handheld LTE test devices using a combination of Network Signal Guru (NSG) and CellMapper for Android. This app reports signal values and logs cell metadata with locations to a CSV file which we can analyse.

Some variables were unknown such as RF power, which required us to take measurements on the green in full line of sight. These “power readings” allowed us to reverse engineer the cell power as approximately 40dBm (10W) which would be appropriate for a cell serving a village.

Frampton on Severn. The cell tower is to the far right behind the pub.

Received Signal Received Power (RSRP)

The measured power value is Received Signal Received Power (RSRP) which is a LTE dBm value determined by the bandwidth, in this case 10MHz like most LTE Band 20 signals in Europe.

RSRP is lower than the carrier signal (Received Power) which is agnostic to bandwidth, but also measured in dBm.

Be careful not to confuse the two units of measurement as they can vary by more than 27dB!! A carrier signal of -80dBm might have a RSRP of -108dBm or lower depending on bandwidth. RSRP is usable down to -120dBm.

Received power dBmBandwidth MHzRSRP dBm
Relationship between power, bandwidth and RSRP at 10MHz


Diffraction is the effect that occurs when radiation hits an edge like a rooftop or a hilltop. The wavefront radiates from that edge with resulting power determined by several factors like height and wavelength. Much like a game of pool, the angle of incidence determines the angle of reflection so a tall building will cast a long RF shadow before the diffracted signal is available again beyond the shadow. A proper diffraction shadow has soft edges as the RF scatters in all directions. LiDAR data creates sharp shadows, even when trees have no leaves.

The CloudRF service has two diffraction capable CPU and GPU engines which use a proprietary algorithm based upon Huygen’s formula which considers obstacle dimensions and wavelength.

Exaggerated diffraction caused by solid LiDAR

Which propagation model is best for 800MHz?

Most propagation model curves follow similar trajectories but differ by only a modest amount of dB in relation to the impact of an obstacle. The choice of model is therefore less important, in our experience, than getting the obstacle data right so for a cellular base station, you could choose to calibrate against any empirical or deterministic model which supports that frequency. Each model has a reliability margin to help align and tune it. For UHF the advanced (and default) ITM model is preferable as it was designed for NLOS broadcasting with complex diffraction routines. For this test we picked the simpler Egli VHF/UHF model with basic knife edge diffraction since this features in both our CPU and GPU engines, and we want to calibrate both.

Path loss curves for propagation models

What is “accurate”?

The cellular modem used to record power levels has a measurement error of -/+ 3dB so any reading cannot be more accurate than this. Therefore, if calibration of field measurements returns a Root Mean Square (RMSE) value of 8dB, this can be considered to be composed of measurement error and (5dB of) modelling error.

For Line of sight, a modelling error level of < 10dB is ok, < 5dB is good, and < 3dB is excellent. This is the easy part which for some basic tools is enough.

Line of Sight coverage: Good for above UHF only

For non line of sight (which covers much more complex scenarios), the error doubles so an error level of < 20dB is ok, < 10dB is good and < 6dB is excellent.

For our field testing, we achieved a non line of sight calibration with 5.2dB of modelling error which we were content with. We are confident we can improve upon this with richer clutter data which we are developing presently.


1m LiDAR – It isn’t as useful as it looks

Using 1m LiDAR for the village we generated a sharp heatmap sensitive to chimney stacks and even parked vehicles which made for a very crisp result visually but the first-pass correlation with the field measurements showed it was conservative, which arguably is a safe default if you’re unsure.

The reason was a combination of trees and buildings. The village had trees on the green but due to the season, none were in leaf so signals would travel through them with relatively reduced attenuation. The LiDAR data however, regards a tree as a solid obstacle so results in an overly conservative prediction for measurements beyond the trees. Attenuation through buildings is a weakness of LiDAR in 2.5D RF modelling using this raster data.

You can show RF on the roof and if diffraction is calibrated, beyond the diffraction shadow as the signal hits the ground but not within the shadow itself where through-building signals reside.

LiDAR calibration showing a mean error of -1dB and a total RMSE error of 10dB.

The LiDAR result was improved with positive adjustments to the diffraction routine in SLEIPNIR, our CPU engine. As a result, diffraction is slightly more optimistic and the correlation with field measurements was improved.

The best LiDAR score, subtracting 3dB of receiver error was a modelling RMSE of 7.28dB.

DTM and Landcover – Better than LiDAR?

Using 30m DTM with layered 10m Landcover and 2m buildings, sampled at 5m resolution, higher calibration was achieved despite the loss of resolution. The reason is the Landcover offers through-material attenuation which can be adjusted to match field measurements. In this case, the “trees” and “urban” height and attenuation values were manipulated until coverage matched the results with high accuracy.

The best Landcover score, subtracting 3dB of receiver error was a modelling RMSE of 5.22dB.

Landcover calibration produced a better result – without breaking the bank

A / B comparison – LiDAR and Landcover

Using our calibrated settings, we extrapolated coverage out to 3km radius to model the whole cell. Here you can clearly see differences in coverage between the two data sets. With LiDAR, coverage is bouncing off hard tree canopies and casting sharp shadows on obstacles like hedgerows. With Landcover, we still have diffraction but more attenuation from obstacles which creates major nulls and also softer diffraction shadows, set by our clutter profile.

A look forward

Findings from this field testing will be worked back into the CloudRF service in coming days, followed by SOOTHSAYER in due course, as new releases for our SLEIPNIR CPU engine, GPU engine and better default clutter values. We are developing sharper, and economically viable, global clutter data to improve on these scores, but won’t say how just yet 😉

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Bandwidth and noise

As communications systems have grown in capacity, they have expanded in physical bandwidth in an increasingly congested RF spectrum.

Effective digital communications planning requires more than knowledge of antennas and propagation fundamentals. It now needs an intimate understanding of bandwidth and noise to co-exist and communicate efficiently.

Unfortunately, this key aspect of modern RF systems is often taught badly, or in some cases not at all, leading to often unwelcome surprises for equipment operators in the field. As a technology and market agnostic platform we’ve observed poor bandwidth knowledge in many markets, but notably MANET and 5G, both of which are accelerating the deployment of wideband systems, often with little to no planning beyond a topographic map study.

Both radio markets are evolving from narrower channel technologies, which in the case of MANET and the VHF Combat-Net-Radio (CNR) it replaces was measured in KHz so they need to update their theory training content and associated software to convey these potentially complex topics to novice students in a digestible manner.

Increasing bandwidth increases noise, which reduces coverage

Teaching noise

As bandwidth increases, so does channel noise. This simple concept might seem easy to remember for a student but without visual aides, and since the demise of analog systems; audible aides, it is hard to demonstrate in practice.

A good teacher may show visual aides like noise charts, FFTs, spectograms and a bad one may show some Johnson-Nyquist formulas buried within an all-day powerpoint which is not helpful except for getting paid.

FFT showing a narrow signal and wideband interference

A student can tick the right box on their exam(s) but spend their career wasting bandwidth and struggling to establish communications because they believe that big is better – it isn’t, or worse still, that bandwidth has no effect on the coverage since that’s a function of transmitted power and/or height. Having an intimate understanding of the interplay between bandwidth, receiver sensitivity and thermal noise will make spectrum users more efficient, effective and considerate.

Bandwidth MHzThermal noise (dBm)
Bandwidth thermal noise table based on a temperature of 21C

Which waveform is best?

Comparing digital radios is complicated due to the myriad of features, waveforms and software.

Given a particular waveform it will have characteristics such as a minimum Signal-to-Noise Ratio (SNR) value which it requires to achieve a symbol rate necessary to deliver a fast data link for example. This dB value must sit proud of the noise floor so if the noise floor is high at -90dBm, coverage will be reduced and conversely, by taking it to somewhere quiet eg. -110dBm, the coverage will improve by 20 decibels – a huge difference.

To compare waveforms precisely, the same noise floor should be simulated, initially, with fixed values to eliminate random error in field testing. The sensitivity values will be somewhere between 3 and 20dB depending on what the waveform and target Bit-Error-Rate (BER) is.

Bit Error Rate (BER) describes the ratio of errors in a data stream. An ok value is 10-3 or 1 bad bit in a 1000 or 0.1%. This increases with noise until a signal is unrecoverable. For more on BER see an older blog here.

For ground radios designed for noisy environments, a BER of 10-2 (1 error in 100 or 1%) is used here for extracting our planning thresholds from a chart of SNR curves. For an airborne system without obstacles this could be higher, for example 10-5.

Signal to Noise Ratio for different modulation schemas against error rates.

A narrow waveform eg. QPSK gives better coverage and works better in noisy conditions. This is the fallback telemetry mode used in many data radios.

A wide waveform eg. QAM64 is capable of better throughput and delivering high bandwidth streams such as HD video.

The best radio is one which can use different waveforms to satisfy both coverage and capacity.

Modelling bandwidth: A tutorial

Modelling RF Bandwidth and noise

Quick reference guide

A quick reference guide for using bandwidth and noise is available here. For other guides see here.


Bandwidth and noise is essential knowledge for anyone deploying wideband systems or comparing waveforms.

RF theory training can be enhanced (and needs to be) with visual tooling to let students quickly observe the impact of different inputs in a controlled environment with templates to minimise user error.

For information on how SOOTHSAYER can help with signals training see here.

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GPU propagation engine

5G cell

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 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 “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.


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:



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.

CloudRF is a member of the NVIDIA inception program

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Mapping mesh networks

Mesh network

Mobile ad-hoc networks (MANET) are an increasingly popular architecture in emergency services and Defence communications. Unlike classic repeater based networks, MANET radio network communications do not have fixed infrastructure so must form self-healing, self-routing networks.

MANET radio modules are well suited to working either off-grid away in remote areas or for providing resilience and independence in well served cities which may be suffering from power and/or network failure.

The bandwidth requirements and throughput of MANET networks varies substantially by waveform. Some are designed for range, others maximum throughput. For this reason, manufacturers offer a range of frequency modules.

Why RF planning tools don’t get used for emergency networks

RF planning software has evolved substantially in the 30 years it’s been used to build out fixed infrastructure networks. Time sensitive customers such as the emergency services have a difficult relationship with these tools. They need them, and often buy them, but don’t have the time to use them to their full capabilities. As a result they rarely get used on anything except training and exercises. Even then the numbers of staff directly interfacing with them will be very small, even in very large organisations of radio users.

The focus for most RF tools is planning with static sites. Whether that’s clicking on a map or uploading a spreadsheet of hundreds of locations it’s still static. MANET requires dynamic inputs and continuous computation which is where APIs come to the fore…


Cloud-RF’s latest API has a function designed for ad-hoc networks called ‘points’. The points API functions like a point-to-point profile in terms of it’s input and output except it accepts an array of transmitters. This means you can test 10,50 or 500 transmitter nodes back to a single receiver in a single API call. It’s also fast as you’d expect and can model a link every millisecond so the 870 distinct links demonstrated in the video were processed in under a second, every second.

For more information on the points API see our documentation here:

Radio mapping planning

In this video, we demonstrate the Cloud-RF points API to model a MANET network (Mobile Ad-hoc Network). For this demo 30 nodes were moved around a 16km track covering a variety of terrain. Each node was tested against 29 siblings for a total of 870 links per second.

Coloured links denote good (green) average (amber) and poor (red) links between the nodes and map to 5dB, 10dB and 20dB signal-to-noise ratios. Only links exceeding 5dB SNR are shown or it looks like a bad game of kerplunk!

The radio settings used were L band (1-2GHz) with only 1 watt of power. This conservative start setting was chosen to show a dynamic range of links. Later in the video the template is switched at the database to demonstrate the impact or gain of using different bands such as 2.4GHz and 500MHz.

Integrating your data

The demo video used mock data and an unpublished script to present the results as a KML. The source of the data is irrelevant so long as it’s accurate and time sensitive. This could be a radio vendor’s dashboard or database. Many of the leading vendors such as DTC, Harris, Persistent Systems, Silvus and Trellisware have location aware GPS modules and software interfaces to display reported radio positions.

The required format for a point is WGS84 decimal degrees. The height is taken from the template which is defined within the body of the points request. The new APIv2 makes defining a template easy as a JSON object so you can have a local archive of template .json files.

A suggested workflow for API integration for dynamic points is as follows:

  1. Fetch a list of all radio locations as decimal degrees
  2. Choose a template as a JSON object
  3. Make an API request using the data and a client script to
  4. Parse the JSON response to extract the results for each node
  5. Put the results on a map as lines
  6. Style the lines based upon your own local rules for your equipment, QoS and waveform eg. < 5dB is red

Download example client scripts from our Github site:

For assistance with integration and hosting options email

Autonomous vehicles

Where this points API will really add value is in mapping and assisting autonomous vehicles who are invariably fitted with MANET radio modules. Whether it’s a drone or a UGV, this API can be used to rapidly exercise multiple routes to help make better decisions.

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RF penetration demonstration

During infantry training, soldiers are shown firsthand the impact of different weapons upon different materials to help them make better decisions about good cover versus bad cover. Spoiler: The railway sleeper doesn’t make it 🙁

As tactical radios have moved several hundred megahertz up the spectrum from their cold-war VHF roots, material attenuation is a serious issue which needs demonstrating to enable better route selection and siting. Unlike shooting at building materials it’s hard to visualise invisible radio signals, and therefore teach good siting, but equally important as ground based above-VHF signals are easily blocked in urban environments.

This blog provides a visual demonstration of the physical relationship between different wavelengths and attenuating obstacles only. It does not compare modulation schemas, multi-path, radios or technologies.

Bricks and wavelengths

Clutter data refers to obstacles above the ground such as trees and buildings. Cloud-RF has 9 classes of clutter data within the service which you can use and build with. Each class (Bricks +) has a different attenuation rate measured in decibels per metre (dB/m). This rate is a nominal value based upon the material density and derived from the ITU-R P.833-7 standard and empirical testing with broadcast signals in European homes.

A signal can only endure a limited amount of attenuation before it is lost into the noise floor. In free space attenuation is minimal but with obstacles it can be substantial. This is why a Wi-Fi router in a window can be hard to use within another room in the house but the same router is detectable from a hill a mile away.

The attenuation rate is an average based upon a hollow building with solid walls.

Common building materials attenuate signals to different amounts based on their density and the signals wavelength.

A higher wavelength signal such as L band (1-2GHz) will be attenuated more than VHF (30-300MHz) for example.

A long wavelength signal like HF will suffer minimal attenuation making it better suited to communicating through multiple brick walls.

The layer cake house

A brick house is not just brick. It’s bricks, concrete blocks, glass, insulation, stud walls, furniture and surfaces of varying absorption and reflection characteristics. Modelling every building material and multi-path precisely, is possible, given enough data and time due to the exponential complexity of multi-path but wholly impractical.

A trade-off for accurate urban modelling is to assign a local attenuation value. It’s local since building regulations vary by country and era so a 1930s brick house in the UK has different characteristics to a 1960s timber house in Germany. Taking the brick house we can identify the nominal value by adding up the materials and dividing it by the size.

For example, 2 x solid 10dB brick walls plus a 5 dB margin for interior walls and furniture would be 25dB. Divide this by a 10m size and you have 2.5dB/m. Using some local empirical testing you can quickly refine this for useful value for an entire city (assuming consistent architecture) but in reality the *precise* value will vary by each property, even on a street of the same design, due to interior layouts and furniture.

Range setup

We created nine 4 metre tall targets using each of the 9 clutter classes in attenuation order from left-to-right, measuring 10x10m and fired radio-bulletsTM at them from a distance of 300m using the same RF power of 1W.

The following bands were compared: HF 20MHz, VHF 70MHz, UHF 700MHz, UHF 1200MHz, UHF 2.4GHz. SHF 5.8GHz.

The ITU-R P.525 model was used to provide a consistent reference.

Only the stronger direct-ray is modelled. Multipath effects mean that reflections will reach into some of the displayed null zones, with an inherent reflection loss for each bounce, but these are nearly impossible to model accurately and in a practical time.

Here are the results.

HF 20MHz


UHF 700MHz

UHF 1200MHz

UHF 2.4GHz

SHF 5.8GHz


  • Dense materials, especially concrete, attenuates higher frequency signals more than natural materials like trees
  • Lower UHF signals perform much better than SHF with the same power
  • Higher frequencies with low power can be blocked by a single house, even after only 300m
  • HF eats bricks for breakfast!


Modern tactical UHF radios, and their software eco-systems, are unrecognisable from their cold-war VHF ‘voice only’ ancestors in terms of capabilities but have an Achilles heel in the form of material penetration. To get the best coverage the network density must be flexed to match the neighbourhood.

This is obvious when comparing rolling terrain with a urban environment but the building materials and street sizes in the urban environment will make a significant difference too. Ground units which communicated effectively in a city in one country may find the same tactics and working ranges ineffective in another city with the same radios and settings. Understanding the impact of material penetration will help planning and communication.