“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
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.
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.
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.
“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.
“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.
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 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.
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.
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.
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.
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.
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 😉
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.
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.
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.
Thermal 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.
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
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.
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.
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.
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.
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.
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.
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…
An API for MANET
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.
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:
Fetch a list of all radio locations as decimal degrees
Choose a template as a JSON object
Make an API request using the data and a client script to https://api.cloudrf.com/points
Parse the JSON response to extract the results for each node
Put the results on a map as lines
Style the lines based upon your own local rules for your equipment, QoS and waveform eg. < 5dB is red
For assistance with integration and hosting options email firstname.lastname@example.org
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.
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.
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.
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.
A popular question when modelling GSM / UMTS / TETRA / LTE networks is how can I show the coverage from the mobile subscribers (Uplink)? Showing a tower’s coverage (Downlink) is easy but how do you go the other way back to the tower? If you know your equipment capabilities (tower and subscribers) you can calculate link budgets for both the uplink and downlink and then use those values to perform an area prediction. Here’s a simple example with the GSM900 band and without some of the other gains and losses which can complicate this for the benefit of novices. You can always add in your own gains and losses where you like to suit your needs.
1. Calculate the total effective radiated power for the BTS tower by adding the power and antenna gain (Limited to 33dBm in the UK)
2. Repeat for handset (Limited to 23dBm in the UK)
3. Calculate the minimum receive level for the BTS by subtracting the receive antenna gain from the receiver sensitivity eg. -110 – 10 = -120
4. Repeat for handset
5. Calculate the maximum allowed path loss (MAPL) by subtracting the minimum receive level from the ERP.
6. Repeat for handset
A balanced network will have similar values. If your base station can radiate for miles but your handsets cannot you have an unbalanced and inefficient network.
Finally, to see this on a map, use the ‘Path loss (dB)’ output mode in CloudRF along with the ‘Custom RGB’ colour schema. Enter the uplink value into the green box and the downlink value into the blue box and run the calculation. A typical cell site will have a greater reach (blue) than it’s subscribers (green). The system will automatically factor in the effect of terrain, ground absorption, antenna heights to give you an accurate prediction.
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