Is Forecasting Accuracy Enough? A Comparative Study of Traffic Forecasters for Beam-Hopping LEO Satellite Networks

Abstract

We evaluate diverse models for user traffic demand forecasting in Low Earth Orbit (LEO) satellite networks with Beam Hopping (BH), questioning whether predictive accuracy is the right objective for this task. To capture the complex nature of the user traffic demand, we employ a second-order self-similar traffic model, supplemented by a publicly available Wi-Fi dataset to validate the self-similar model against the empirical traffic patterns. We compare forecasters ranging from classical statistical approaches, such as the optimal forecaster for self-similar data and the optimal linear predictor on the discrete sampling grid, to Fractional Auto-Regressive Integrated Moving Average (FARIMA) models, as well as emerging deep learning architectures. The latter category encompasses foundation and domain-specific transformer models, alongside a lightweight neural network consisting solely of linear layers. We assess these models at two levels: in isolation, through the Mean Absolute Scaled Error (MASE), and in context, through a BH simulator in which the forecast drives the illumination plan. On purely self-similar traffic the three self-similarity aware forecasters perform on par with one another and dominate the learned models, whereas on the raw Wi-Fi trace this ordering nearly reverses. Seasonality violates their stationary increment assumption; removing the periodic component restores their comparative accuracy. Crucially, these accuracy differences barely propagate to the system level. Loss ratio and buffer backlog are affected more by system utilization and the planning period than by the choice of forecaster, with the performance gap between forecasters vanishing entirely below 0.90 utilization. This suggests design efforts are better spent optimizing utilization margins and planning periods rather than chasing marginal gains in raw accuracy.