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- W3049382014 abstract "This paper details the architecture and employment of a link-layer user-uplink simulator and a methodology that enables prediction of the spectrum demand of various connection service mixtures through the use of random forest machine learning. The results show that a model can be produced using this methodology. Predictions are used as an input to a channel load balancing algorithm, with the goal of learning the behaviour of the scheduling and framing algorithms." @default.
- W3049382014 created "2020-08-21" @default.
- W3049382014 creator A5021572278 @default.
- W3049382014 date "2020-07-01" @default.
- W3049382014 modified "2023-10-16" @default.
- W3049382014 title "Machine Learning-based Frequency Resource Demand Prediction for a Mobile Satellite Network" @default.
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- W3049382014 doi "https://doi.org/10.1109/tsp49548.2020.9163578" @default.
- W3049382014 hasPublicationYear "2020" @default.
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