Machine learning algorithm for detecting periodic disturbances of microwave signals

dc.contributor.authorLundmark, Oliver
dc.contributor.authorAbelson, Julius
dc.contributor.departmentChalmers tekniska högskola / Institutionen för matematiska vetenskapersv
dc.contributor.examinerAndersson, Adam
dc.contributor.supervisorSjödin, Martin
dc.contributor.supervisorTavara, Shirin
dc.date.accessioned2022-06-22T06:07:01Z
dc.date.available2022-06-22T06:07:01Z
dc.date.issued2022sv
dc.date.submitted2020
dc.description.abstractAs 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.sv
dc.identifier.coursecodeMVEX03sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/304859
dc.language.isoengsv
dc.setspec.uppsokPhysicsChemistryMaths
dc.subjectmicrowaves, microwave links, microwave disturbances, machine learning, neural networkssv
dc.titleMachine learning algorithm for detecting periodic disturbances of microwave signalssv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
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