# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 1 Introduction  
LEO mega-constellation networks demonstrate excellent communication capabilities, including wide geographic coverage, low delay, large bandwidth and uninterrupted service provision, highlighting their significant role in space-air-ground integrated networks (SAGINs) [1]. In recent years, with the construction of LEO mega-constellations such as Starlink, OneWeb and Kuiper [2], the explosive growth of Internet-connected devices and traffic demand have increased drastically. However, on-board computing and storage resources are limited by power consumption and the size of satellites. Thus, LEO mega-constellations face challenges in capacity and quality of service (QoS) [3]. Due to continuous inter-satellite links (ISLs) switching between adjacent satellites, the network topology changes drastically over time. Meanwhile, satellite network traffic shows temporal and spatial heterogeneity, i.e., traffic demands may vary greatly in different geographical locations or in different time slots [4]. In the case of expanding user scale and traffic demand, the above challenges result in increased congestion in LEO mega-constellations. Through traffic forecasting, the change in network traffic can be analyzed in advance. The control of network traffic can be changed from reactive response to proactive perception. On the one hand, by forecasting the traffic demand for the next time slot, traffic transmission paths can be planned in advance to proactively avoid congestion. On the otherhand, for bandwidth-sensitive services, always reserving peak bandwidth will inevitably reduce resource utilization. On the basis of traffic forecasting, bandwidth allocation can be dynamically adjusted according to service demand to achieve the efficient utilization of bandwidth resources. The priority of satellite network traffic forecasting is to build network traffic models that represent the network properties accurately. Traffic demand at different geographic locations is usually cyclical [5]. Meanwhile, the self-similarity of traffic is prevalent in terrestrial networks [6]. In a satellite network, the traffic is aggregated through terrestrial gateways, which will not change the basic properties, i.e., long-range dependence (LRD) on a large scale and self-similarity on a small scale [7], as shown in Figure 1. The actual traffic demand can be divided into long-range baseline and short-range fluctuations; baseline traffic has LRD properties, whereas fluctuations are short-range dependent. Therefore, forecasting models with high precision and low complexity that can demonstrate self-similarity are essential for network traffic forecasting. Traditional network traffic forecasting models, i.e., Poisson models and Markov models, can only deal with short-range dependence (SRD) traffic. Meanwhile, due to the limited on-board computing resources of satellite networks, the computational complexity of a satellite network's traffic forecasting model needs to be reduced [8]. So, the traffic prediction models of terrestrial networks are not suitable for satellite networks with LRD properties. In order to establish a high-precision and low-complexity traffic forecasting model, it is necessary to first decompose network traffic to extract the long-range correlation traffic and short-range fluctuation traffic. Moreover, the computational complexity of the forecasting models for LRD traffic in terrestrial networks is considerable. Due to the limited on-board computing and storage resources of LEO satellites, it is necessary to reduce the computational complexity. Therefore, the forecasting models of terrestrial networks cannot be adopted to constellation networks directly. It is necessary to establish a high-precision and low-complexity forecasting model for mega-constellation network traffic. To address the above issues, constellation network traffic is decomposed into multi-order intrinsic mode functions (IMFs) with empirical mode decomposition (EMD).