Signal Bandwidth Localization Using Artificial Intelligence

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Nkom are working towards a fully end to end learning solution to detect signal bandwidth, center frequency and signal transmission. Currently Nkom has deployed an automated jammer classification system to gain machine learning (ML) experience and are currently in use to actively detect signal disruptions in an automated manner for GNSS jammers.

To expand the scope of frequency interference detection using ML, Nkom is moving towards a more general solution than already employed, to detect signal bandwidth (BW) and signal localization within the spectrum for the full frequency range used for electromagnetic communication. The final goal is to solve this in a signal-agnostic manner for the total signal bandwidth, without classifying the communication protocol or signal type.

A proposed progression for this proposal would be to start with detection of the BW of jammers as a first step towards an end-to-end solution in the pre-project face, and then further widen the scope to manage “any” signal type in the full master thesis.

Oppdragsgiver

Nkom

Nkom arbeider for å sikre et robust, likeverdig, rimelig og fremtidsrettet tilbud av posttjenester og elektronisk kommunikasjon (ekom) i hele landet. 

Oppgaveforslag

Type: Fra virksomhet
Publisert: 2022-07-28
Status: Ledig
Grad: Master

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