Andrea Di Domenico
Machine learning methodologies for QoE prediction in satellite networks.
Rel. Marco Mellia, Danilo Giordano. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2023
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Abstract
Satellite Communication (SatCom) enables Internet access in remote locations where conventional infrastructure is unavailable or too expensive to be deployed. In a SatCom connection, a customer uses a parabola to connect to a satellite, which sends all of the customer’s traffic to a ground station, which relays the traffic to the Internet. Unlike traditional infrastructures, where the latency to retrieve content is on the order of tens of ms, SatCom’s latency is much higher: ∼550ms for the satellite link to reach the ground station, plus the time it takes for the ground link to reach the content. In this scenario, the Quality of Experience (QoE) of customers is significantly impacted by the SatCom connection, as slowdowns in the satellite or ground link can severely impair the QoE of customers.
In order to identify and investigate such impairments, it is of utmost importance for the SatCom operator to build models to estimate the customers’ QoE leveraging only network flows generated by the customers
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