This table captures the growth of 5G cellular sites across Australia, as reported by Australia Cellular Services:
Two months ago, we documented the first significant improvement to Canadian 5G prices — only four times that of Australia, a country with a similar culture, economy, land mass, population and population distribution. The table to the right compares SIM-only 5G plan prices for Canada (bundled and unbundled) with Australia. (The bundled price requires you to also purchase home internet, TV, etc.)
Canadian wireless providers offer secret detals if you know who to speak to and what to say, averaging $0.35 / GB, which is still double Australian prices. Canadian retail 5G prices can drop further — trickery free — with substantive improvements to federal government policy.
|Country||Carrier||Plan||$ / GB||Plan||$ / GB|
|AUS||Vodafone||$57 / 600GB||$0.09||$57 / 600GB||$0.09|
|AUS||Optus||$60 / 500GB||$0.12||$60 / 500GB||$0.12|
|AUS||Telstra||$83 / 300GB||$0.28||$66 / 300GB||$0.22|
|CAN||Rogers||$105 / 150GB||$0.70||$105 / 150GB||$0.70|
|CAN||Telus||$105 / 150GB||$0.70||$105 / 150GB||$0.70|
|CAN||Bell||$105 / 150GB||$0.70||$85 / 120GB||$0.71|
|CAN||Rogers Bundle||$55 / 120GB||$0.46|
|CAN||Telus Bundle||$90 / 150GB||$0.60|
|CAN||Bell Bundle||$55 / 120GB||$0.46|
Two years ago, Canadian cellular plans were 10 times more expensive than in Australia, making its nascent 5G network completely useless. Recently, this gap shrunk to only 4 times more expensive (see table at right). Canadian plans that were $1.75 - $2.50 / GB are now only $0.70 / GB.
Canada's high prices are not due to its sparse population or huge landmass, as Australia faces these challenges too. Instead, they're due to bad government policy and regulatory capture, which is also why Canada has less than half the number of critical 3,500 MHz sites as Australia: 4,501 vs 9,809.
|Country||Carrier||Plan||$ / GB||Plan||$ / GB|
|AUS||Vodafone||n/a||$57 / 600GB||$0.09|
|AUS||Optus||$64 / 500 GB||$0.13||$60 / 500GB||$0.12|
|AUS||Telstra||$64 / 180 GB||$0.36||$83 / 300GB||$0.28|
|CAN||Rogers||$175 / 100 GB||$1.75||$105 / 150GB||$0.70|
|CAN||Telus||$100 / 50 GB||$2.00||$105 / 150GB||$0.70|
|CAN||Bell||$125 / 50 GB||$2.50||$105 / 150GB||$0.70|
The following terms & conditions apply to 3-D Fresnel Zone:
Subscribers who cannot sign in or otherwise use Cellular Services or 3-D Fresnel Zone, for reasons technical or otherwise, can receive a subscription period extention equal to the duration of time when the subscription was unavailable. This extension is the only remedy available to you; there are no full or partial monetary refunds.
Your purchase of a subscription to any service mentioned above indicates that you understand and agree to all terms listed above.
The links above contain helpful information about service features and capabilities. Please contact us if you have any other questions.
Two years ago, Australia licensed 26 GHz spectrum (aka millimeter wave) to Telstra, Optus and Vodafone for 5G service.
Australia Cellular Services has a new 26 GHz filter that lets you explore all 1,045 millimeter wave sites from these licensees.
Channel details show a transmit frequency of 26,200 MHz and a huge bandwidth of 1,000 MHz. Its Massive MIMO antennas provide an impressive 30.6 dBi of gain, to help offset huge signal attenuation.
Media exaggerates the benefits of millimeter (mm) wave. Mm supports 1+ gigabit throughput, which few need. Mm supports more subscribers — but only if the backhaul has the extra capacity. As well, a mm site can reach only a few hundred meters, requiring site planners to identify exactly where subscriber activity will be the greatest.
Ask a friend to aim a flashlight at your face. Step to one side and the brightness dims. This is how a smartphone used to experience signal strength as it moved around.
Imagine a flashlight that focuses its beam tightly on your face and your friend actively steering this beam to follow your face as you move. This is what Massive MIMO (mMIMO) does for cellular communications. This focusing and steering uses spectrum more efficiently, increasing subscriber capacity in congested or noisy areas.
This introductory video explains how Massive MIMO improves spectrum efficiency.
Cellular Antennas: Passive (left) vs Active Massive MIMO (right)
New Zealand Cellular Services uses a database of wireless sites published by New Zealand Radio Spectrum Management (NZRSM), a government agency. This database contains location and technical information for all cellular and fixed wireless (WISP) sites across the country. For many years, NZRSM updated this database weekly — until 2022-Dec-05.
We asked NZRSM in 2023-Jan why their database had not been updated. NZRSM replied:
We registered for their API platform. After dozens of emails and hours over the next three months we discovered that the API would not support our requirements, contrary to their advice.
New Zealand Cellular Services remains available to subscribers but remember the INFORMATION IS FROM DECEMBER 2022.
We plan to update our New Zealand database but need to find new sources of information. If you have contacts with any New Zealand wireless operators (cellular or fixed wireless) please contact us to provide an introduction.
How does this affect you, as a subscriber to New Zealand Cellular Services: The pace of change is slow for cellular networks and even slower for most fixed wireless networks. As such, the information provided by New Zealand Cellular Services should remain very relevant to the end of 2023 and somewhat relevant afterwards.
SRTM is good, but Copernicus DEM is usually better. For terrain applications, FABDEM might be even better.
We use Digital Elevation (DEM) and Land Cover Models to identify obstructions to wireless communications over long distances. DEM quality is important to our business and the success of our customers. SRTM has been the go-to DEM for most industry applications. SRTM has served us well. But, it now has competition. Should we replace SRTM with a competitor?
How can we know if a competitor is better than SRTM? Here are actual quality claims from DEM marketing and scientific material:
Absolute nonsense! It's like saying a basketball team with taller players is better than a team with shorter players. Yes, height is important, but at best secondary to the players' many quirks & features. DEM quality is similar, and its many quirks & features elude the simplistic statistics shown above.
This article introduces DEM Explorer, a graphing tool that lets you explore DEM quality and see for yourself that DEM quality cannot be captured by a single number.
DEM Explorer graphs the behavior of the 14 DEMs below, across 3 land covers and 4 slopes. Each curve plots the distribution of error [ elevDEM - elevLiDAR ] between a DEM and highly accurate LiDAR ground truths. Pan, zoom, pinch & swipe the graph to discover more quirks & features.
|ASTER||v003||2019-06||Put it out to pasture.|
|AW3D30||v3.2 (Feb 2022)||2022-02|
|COPernicus DEM 30||DGED 2022_1||2023-01||Pixels 32 bit float|
|COPernicus DEM 30 0.5m||DGED 2022_1||2023-01||Pixels rounded to 0.5m|
|COPernicus DEM 30 1m||DGED 2022_1||2023-01||Pixels rounded to 1m|
|COPernicus DEM 90||DGED 2022_1||2023-01||Pixels 32 bit float|
|COPernicus DEM 90 1m||DGED 2022_1||2023-01||Pixels rounded to 1m|
|FABDEM||V1-2||2023-01||COP30 with reduced forest & building bias|
|ICESat-2_2||v005||2023-02||h_te_uncertainty < 2|
|ICESat-2_10||v005||2023-02||h_te_uncertainty < 10|
|MERIT||v1.0.3||2018-10||SRTM, with less forest bias|
|NASADEM||HGT v001||2020||Reprocessed SRTM|
|SRTM||SRTMGL1 v003||2016||Very popular|
|TDX90||v3||2016||Foundation of COP30 and COP90|
Click any graph on the right to launch DEM Explorer and see
We compare each DEM listed above to billions of LiDAR ground truths with a point density > 6 / m2. Each colored curve on the graph is a distribution of error, created by comparing one DEM to all LiDAR ground truths with the same land cover and slope; eg. forest with moderate slope. Each curve stresses the DEM in a different way, teasing out biases. A smooth curve requires at least one million LiDAR ground truths; most curves use many more (billions in some cases) producing the smooth curves you see on the right.
These LiDAR ground truths have a vertical accuracy better than the height of a chipmunk (5 to 10 cm). We quote RMSE, MAE and other statistics to 0.1m precision; a higher precision captures only terrain noise, like chipmunks, acorns and other ephemeral clutter.
ESA WorldCover 10m 2021 V200 identifies a land cover for each LiDAR elevation: Grass / Crop, Forest or Developed. (We combine Grassland and Cropland, as they present remote sensing with a similar, short and easily permeable surface.)
Slope is calculated from a high-resolution 0.5m elevation grid created from the LiDAR ground truths, providing the most accurate ground slope possible:
|Gentle||1 to 4|
|Moderate||4 to 12|
|Steep||12 to 100|
LiDAR and DEM surveys capture ellipsoidal elevation which are later converted to geoidal (eg. EGM96, EGM2008, NAVD88, CGG2013) for public use. Our analysis require all LiDAR and DEM elevations normalized to the WGS84 ellipsoid. Normalization applies an interpolation method (eg. bilinear, bicubic) to a geoid grid; each interpolation & grid size combination produces slightly different results. Normalization error occurs if the combination we use does not match what was used when the elevation data was packaged for public use. This error can vary from centimeters to meters.
A geoid's continuous surface is defined by spherical harmonic coefficients. These coefficients are too computationally expensive to work with directly, so they are digitized once into a grid of pixels, which approximate the geoid's surface, and interpolated, on demand, to obtain geoid offsets.
Our normalization (from geoid to WGS84) should use the same grid size and interpolation as when the DEM was created. However, only NASADEM publishes these details (ie. 15 arcsecond grid with linear interpolation). We applied various geoid grid sizes and interpolation methods to COP90 to discover how it was derived from TanDEM-X 90m (ie. 60 arcsecond grid with linear interpolation); we assume the same for COP30 but cannot confirm because TanDEM-X does not publish a 1 arcsecond spacing DEM. We used a 60 arcsecond grid and spline interpolation for other DEMs, as that is the interpolation method used by the US National Geospatial Intelligence Agency (masters of the geoid) in their calculations.
These details are important wherever the geoid undulates strongly, such as Hawaii, a place we are currently studying.
DEM quality is not a constant and depends on use-case, nature-of-bias, budget, license terms, file size and coverage area.
Marketing and scientific literature often use RMSE (root mean square error) as a proxy for DEM quality. RMSE must be used with caution, because a few bad apples can spoil the results. To that end, DEM Explorer provides >10m and >20m threshold statistics, measuring the percentage of bad apples (ie. percentage of error above 10m and 20m) which sends RMSE soaring. DEM Explorer also provides MAE (mean absolute error) which is less sensitive to extreme outliers. But, RMSE or MAE — alone — are as much a sign of DEM quality as player height is to basketball team quality.
What's the answer to replacing SRTM? Switching DEMs is not a simple exercise. A new DEM brings its own quirks & features that will improve some things and worsen others. Will the mix of quirks & features net a positive outcome?
Copernicus DEM provides much more accurate surface elevations, useful for our work in wireless propagation analysis. FABDEM is a derivative of Copernicus DEM that reduces this surface bias, which you need for flood analysis. FABDEM performs this task well, at a cost of some negative bias. As well, FABDEM has restrictive terms of license which a commercial application must consider.
We use DEMs for wireless propagation analysis, which favors a DEM that captures all surface clutter (forests, shrubs, but not chipmunks or acorns). Other use-cases, like floodplain analysis, need a no-clutter DEM. These and other quirks & features are what DEM Explore can help you discover, on your search for a better DEM.
Figure 1: One RMSE value cannot capture DEM quality
This range of RMSE values — for one DEM — shows the folly of representing DEM quality with a single RMSE value. Yet scientific and marketing literature does this.
Figure 2: Copernicus DEM quality eclipses NASADEM in low-clutter terrain
NASADEM's shallow curve isn't a sign of how bad it is (NASADEM is a good DEM). Instead, it's a sign of how good Copernicus DEM is in certain situations.
Figure 3: Copernicus DEM can't see the forest floor
COP30's yellow tail to the right of the y-axis is forest clutter; a benefit to radio propagation analysis but a detriment to floodplain analysis. FABDEM's green curve reduces clutter bias, at a cost of bias left of the y-axis.
Figure 4: Copernicus DEM does better in Newfoundland, Canada.
Figures 3 & 4 show different RMSE & MAE values for the same DEM in forests with moderate slope. The only difference is place, expanding on observations in Figure 1.
Figure 5: Effects of rounding DEM pixels (no effect)
Rounding Copernicus DEM pixels from float to integer significantly improves compression ratios, benefiting resource constrained devices. Quality is not compromised when rounding pixels in forests with level terrain.
Figure 6: Effects of rounding DEM pixels (some effect)
The effects of rounding are slightly worse in Grass / Crop surfaces with level terrain.
Figure 7: ICESat-2, as a sparse-DEM, in forested, moderate slope areas
ICESat-2 ATL08 segments are used in other studies as ground truths to assess DEM quality. We turned the tables, creating two sparse-DEMs from 68,600 km2 of ATL08 segments and compared them to our higher accuracy LiDAR. DEM Explorer shows this sparse-DEM to be more far accurate than any global DEM.
The graphs below plot the most recent 18 months of ISED SMS snapshots, by channel count (top), occupied spectrum (middle) and site count (bottom), for the three national (left) and four regional (right) carriers.
Rogers' graphs (red) naturally trend upwards, capturing the growth of its network. Telus' (green) and Bell's (blue) graphs see-saw up and down, hilighting the inconsistencies of these snapshots.
|700 MHz B12||5,183||14,278||9,095|
|700 MHz B29||4,813||5,012||199|