These exemplary percentages represent the relative numbers of previously received indication messages in said time windows, and they are used as a basis for determining said relationship strength.

The next step is to convert the relative number of previously received indication messages in each time window (i.e. each percentage) into a set of descriptor values. Each of said descriptor values is a quantitative descriptor of how well the relative number of previously received indication messages matches a particular quantity class. Each set has as many descriptor values as there are quantity classes. In the terminology of fuzzy logic the quantity classes are also referred to as input membership functions.

FIG. 4 illustrates an example of three quantity classes or input membership functions that can be used to convert the relative number of previously received indication messages in each time window into a set of descriptor values. The first input membership function is called “FEW”: it has a constant value 1.0 between 0% and 10%, from which it decreases linearly to 0.0 at 30%. The second input membership function is called “SOME”: it increases linearly from 0.0 to 1.0 between 10% and 30% and decreases linearly back to 0.0 at 50%. The third input membership function is called “MANY”: it increases linearly from 0.0 to 1.0 between 30% and 50% and stays constant at 1.0 up to 100%.