Machine learning algorithm for detecting periodic disturbances of microwave signals
Typ
Examensarbete för masterexamen
Program
Publicerad
2022
Författare
Lundmark, Oliver
Abelson, Julius
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
As digitalisation is expanding into new fields the demand for secure and stable connections
between devices are ever-increasing. This can be done via microwave link
networks, that comes at a fairly cheap price in comparison with fiber optic cables,
but with the con of being more exposed to external disturbances. These disturbances
could be caused by several different phenomena, including rain, wind and
construction cranes. The scope of this thesis was to expand an already existing tool
for detecting disturbances in Ericsson’s customer’s microwave link network, adding
the possibility of detecting and classifying one more disturbance. This disturbance
is caused by sunrays, which leads to a thermal expansion on one side of the mast
where the node of a link is attached to, causing a miss-alignment between the antennas.
The disturbance is called periodic sway, due to its characteristic 24 hours
periodicity, correlating to the sun’s periodicity.
The tool uses convolutional neural networks (CNN) to detect and classify disturbances.
The CNN model needs features to train on to properly classify the disturbances.
Today Ericsson’s tool uses the features like received signal power and
attenuation. When expanding the tool and adding the periodic sway disturbance,
further features had to be added, to capture the periodic nature of this particular
disturbance. This resulted in adding historical data as a feature.
The conclusions drawn from this thesis are that adding three days of historical
data is sufficient for detecting and classifying this disturbance. Furthermore, the
results imply that a sparse sampling period of 30-60 minutes is enough for the CNN
to detect the periodicity.
Beskrivning
Ämne/nyckelord
microwaves, microwave links, microwave disturbances, machine learning, neural networks