Burst Aware Forecasting of User Traffic Demand in LEO Satellite Networks

TL;DR

This paper presents a burst aware traffic forecasting model for LEO satellite networks that significantly improves prediction accuracy during high-demand bursts, enhancing resource planning and reducing packet loss.

Contribution

It introduces a novel transformer-based forecasting approach with three key enhancements tailored for bursty traffic patterns, applicable to various wireless networks.

Findings

Reduces prediction error by up to 94% at one-step horizon

Accurately captures burst events near the end of longer horizons

Demonstrates robustness in high-traffic scenarios

Abstract

In Low Earth Orbit (LEO) satellite networks, Beam Hopping (BH) technology enables the efficient utilization of limited radio resources by adapting to varying user demands and link conditions. Effective BH planning requires prior knowledge of upcoming traffic at the time of scheduling, making forecasting an important sub-task. Forecasting becomes particularly critical under heavy load conditions where an unexpected demand burst combined with link degradation may cause buffer overflows and packet loss. To address this challenge, we propose a burst aware forecasting solution. This challenge may arise in a wide range of wireless networks; therefore, the proposed solution is broadly applicable to settings characterized by bursty traffic patterns where accurate demand forecasting is essential. Our approach introduces three key enhancements to a transformer architecture: (i) a distance from the last burst embedding to capture burst proximity, (ii) two additional linear layers in the decoder to forecast both upcoming bursts and their relative impact, and (iii) use of an asymmetric cost function during model training to better capture burst dynamics. Empirical evaluations in an Earth-fixed cell under high-traffic demand scenario demonstrate that the proposed model reduces prediction error by up to 94% at a one-step horizon and maintains the ability to accurately capture bursts even near the end of longer prediction horizons following Mean Square Error (MSE) metric.