TEST & MEASUREMENT NETWORK TESTING
Call stability score
The call stability score is a new
assessment metric for reliable
communications. A suddenly dropped
phone call is an annoying experience,
so that is why mobile network
operators have been testing voice
quality and connection stability for
many years.
The most popular statistic is the call
drop rate (CDR). But since the number
of dropped calls is very low in mature
networks, it is necessary to make
a large number of calls in order to
obtain a statistically significant value.
Consequently, drive test campaigns are
long and expensive.
Therefore, Rohde & Schwarz uses
a method to replace the binary call
status (either successfully completed
or dropped) by a finely graduated
analogue value. This is done by
creating a statistical AI-generated
model that links the transmission
conditions with the call status.
The CSS derived from the model
allows the reliability of the mobile
connection to be measured over the
entire call duration and classified
based on quality.
The diagnostic also includes
unstable calls that were successfully
completed but the data proves they
were not far away from being dropped.
In conventional CDR statistics, those
unstable calls would be assessed
positively as successful calls, distorting
the network quality assessment.
The CSS value is based on
information gathered from millions
of test calls and incorporated in the
model during the learning process. The
assessment is conclusive right from
the first call. The network call quality
is registered more accurately and with
less test effort.
In practice, every nine seconds of a
call, measurement data is sent to the
statistical model as a time series. The
model assesses the data based on the
learned rules and outputs a number
between 0 and 1.
The higher the number is, so the
lower the likelihood of a drop occurring
in that nine-second interval.
The CSS measurement is part
of the R&S SmartAnalytics analysis
platform.
Another AI-driven function in this
software suite is anomaly detection
using unsupervised learning. In both
cases, the use of artificial intelligence
leads to results that are not possible
with conventional means.
AI methods will be used more
and more in the future to maximize
exploitation of the information content
of measurement data.
Below: Display of a
network optimisation
scenario using the
R&S SmartAnalytics
analysis software’s
call stability score
patterns (edges,
coloured areas,
etc.).
For training, the
learning software is
presented with images
labelled by humans and
works out characteristics
that allow decisions to
be made. These rules are
concealed in the neural network
of the AI system rather than
being formulated in algorithms.
An example of nonvisual
pattern recognition is the
determination of the call stability
score (CSS) for network tests
(described below).
Unsupervised learning works
without labels. The algorithms have
to independently recognise patterns
or multidimensional data aggregates
in order to derive usable conclusions
from them, for example with the aim
of measuring differences between
new and known data points. A typical
task for unsupervised learning is
anomaly detection, which identifies
unusual data without the support of
experts.
AI methods
In response to the needs of network
operators Rohde & Schwarz uses
AI methods for applications such as
simplifying the optimisation of mobile
networks or improving the assessment
of qualitative differences between
providers.
The Data Intelligence Lab
established in 2018 tackles these
issues and supports Rohde &
Schwarz R&D departments with data
based analysis methods. These
approaches are especially promising
for testing mobile networks where
particularly large amounts of data are
generated, so that manual analysis
and rule formulation are no longer
practical. Machine learning makes
it possible to use the information
hidden in large data sets, for example
to derive new assessment metrics.
An example is the call stability score.
Author details:
Dr. Alexandros
Andre Chaaraoui
is Data Scientist
and Project Leader,
Rohde & Schwarz
www.newelectronics.co.uk 13 October 2020 23
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