{"ID":2871573,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10917","arxiv_id":"2509.10917","title":"Forecasting Self-Similar User Traffic Demand Using Transformers in LEO Satellite Networks","abstract":"In this paper, we propose the use of a transformer-based model to address the need for forecasting user traffic demand in the next generation Low Earth Orbit (LEO) satellite networks. Considering a LEO satellite constellation, we present the need to forecast the demand for the satellites in-orbit to utilize dynamic beam-hopping in high granularity. We adopt a traffic dataset with second-order self-similar characteristics. Given this traffic dataset, the Fractional Auto-regressive Integrated Moving Average (FARIMA) model is considered a benchmark forecasting solution. However, the constrained on-board processing capabilities of LEO satellites, combined with the need to fit a new model for each input sequence due to the nature of FARIMA, motivate the investigation of alternative solutions. As an alternative, a pretrained probabilistic time series model that utilizes transformers with a Prob-Sparse self-attention mechanism is considered. The considered solution is investigated under different time granularities with varying sequence and prediction lengths. Concluding this paper, we provide extensive simulation results where the transformer-based solution achieved up to six percent better forecasting accuracy on certain traffic conditions using mean squared error as the performance indicator.","short_abstract":"In this paper, we propose the use of a transformer-based model to address the need for forecasting user traffic demand in the next generation Low Earth Orbit (LEO) satellite networks. Considering a LEO satellite constellation, we present the need to forecast the demand for the satellites in-orbit to utilize dynamic bea...","url_abs":"https://arxiv.org/abs/2509.10917","url_pdf":"https://arxiv.org/pdf/2509.10917v2","authors":"[\"Yekta Demirci\",\"Guillaume Mantelet\",\"Stéphane Martel\",\"Jean-François Frigon\",\"Gunes Karabulut Kurt\"]","published":"2025-09-13T17:33:53Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Transformer\"]","has_code":false}