Mobile Coverage Maps: the Good, the Bad and the Ugly

“How can I check my mobile signal?” and “how can I find the operator with the best mobile coverage in my area?” sound like straightforward questions. And the straightforward answer is “check your operator’s coverage map”. But that is rarely satisfactory. In this blog post we will explore why. We will identify five underlying reasons why traditional mobile coverage maps (as provide by operators and regulators) are so unsatisfactory. And we will outline an alternative approach to address these issues. The five reasons why traditional mobile coverage maps fail to hit the spot are:

• Mobile coverage maps are marketing instruments
• Mobile coverage maps are created top-down
• Mobile coverage maps lack numeric granularity
• Mobile coverage maps lack visual granularity
• Independent providers are not independent

So let’s unpack each one.

Mobile coverage maps are marketing instruments

People don’t design mobile coverage maps as operational tools They are marketing instruments. Their primary use is to persuade current and potential customers of the superiority of the provider’s network. By their very nature they will present their own network in the best light possible. And when it doubt, these maps will err on the side of optimism.

Mobile coverage maps are created top-down

The laws of physics, which are well understood, govern mobile signal propagation. How far a signal will carry will depend on the frequency, the power and the medium through which the waves travel (air vs leaves or a brick wall). Mobile coverage maps are sophisticated physics models that combine the laws of physics with mapping data on the location and nature of various objects (houses, trees, buildings, etc…) .

The main issue with this approach is two-fold: the quality of the calculation is only as good as the quality of the data that went into it. The more granular the geographical data, the better the results, but the heavier the calculation. So operators will look to strike a balance.

Secondly the terrain and geographic data changes: trees grow, buildings go up or are torn down. And these changes impact the coverage. Again, ideally this data should be updated and calculations redone, but this is rarely the case

And finally and most fundamentally, top-down calculations can only take you so far. Reality on the ground will always be a bit messier than what the theory says. And while operators have access to bottom-up data through crowdsourcing and drive testing, they do not incorporate this data in their network coverage maps.

Mobile coverage maps lack numerical granularity

Mobile signal strength is expressed in “dBm” (“decibel milliwatts”) (this article has a good, digestible wider explanation on dBm and singnal strength: https://www.wilsonpro.com/blog/whats-the-difference-between-db-and-dbm). This numerical value is typically in the range between -80 (very good) and -120 (unusable). And while this signal strength can be modelled quantitatively, it takes up a lot of computing power, especially if one wants to map out a whole country. So instead of providing hard numbers, mobile coverage maps typically limit themselves to some qualitative assessment (“good outdoor coverage” or “good indoor coverage”). While this has some merit in a marketing context, operationally speaking it is not very useful.

Mobile coverage maps lack visual granularity

From a presentation perspective, mobile coverage maps overlay images on a map, optimizing for a macro view (country or region). However, when a user zooms into a specific location, such as a house or street, the unchanged resolution presents the area as an indistinct color blob, providing no opportunity to extract meaningful data.

Independent providers are not independent

We know that mobile operators are both judge and jury when it comes to presenting their mobile coverage maps. And they will, by definition, present an as positive picture as possible. So what about third party providers? There are the local regulators as well as commercial providers. Should they not be in a position to provide a more neutral and truthful perspective?

Let’s start with the commercial providers. There are a number of notionally independent providers of mobile coverage information. These will combine top-down and bottom-up (through crowdsourcing and drive testing) information to create detailed mobile coverage maps. However the main customers for their products are the mobile operators. And this makes them dependent on the big operators. These vendors will not provide information that puts their customers in a bad light and they will have scant interest in providing piecemeal data to users that are not looking to spend large sums of money.

Finally, what about the regulators? Regulators have to walk tightrope between the interests of consumers and the interests of the providers. They need to be seen to be a fair and neutral arbiter and the mobile operators will hold them to that. This means that the regulators are extremely careful not to publish any information which can be deemed to be negative or disparaging for any operator without at least triple-checking. Creating a truthful, comprehensive and defendable coverage map through traditional means would take significant cost and effort. And the resources available to these organisations are constrained. So ,more often than not ,they opt for the next best thing: collect the self-reported coverage data from the operators and re-publish, thereby perpetuating the issues we highlighted earlier.

Teragence offers an alternative

We as Teragence believe that good mobile coverage information is more than just a marketing gimmick. Many businesses depend on good mobile coverage, but they cannot get any clear, actionable data anywhere. Whether it is “normal” cellular coverage data or LPWAN technologies such as LTE-M and NB-IOT. That’s why we created our mobile signal strength checker Teragence Signal Checker the “un-coverage map”. We do not provide nation-wide coverage maps. We do not compare operators and hand out trophies. Instead we provide our customers with specific, quantitative signal strength information for specific locations. We do this by combining crowdsourced data with advanced geospatial analytics and machine learning techniques. This bottom-up approach is the antithesis of the traditional mobile coverage map, but it provides our customers with the actionable information they need to optimise their connectivity-dependent operations. So if you are looking for accurate, granular and location specific signal strength data, get in touch.