Low Earth Orbit (LEO) satellite constellations enable global low-latency connectivity but face challenges due to weather-dependent link variability, orbital dynamics, and heterogeneous ground infrastructure. In this paper, we propose a hybrid LEO-terrestrial architecture that integrates Software-Defined Networking (SDN) ground station clusters with repeater-assisted reception and a structured fallback mechanism. We develop a stochastic model that captures weather-driven reliability, correlated repeater behavior, and multi-path reception, and formulate an optimization problem to minimize the expected communication delay. To address the intractability of this problem in large-scale, partially observable environments, we design a fully decentralized Multi-Agent Reinforcement Learning (MARL) framework based on Proximal Policy Optimization (PPO), where each satellite makes decisions using only local observations. The reward is aligned with the analytical delay objective, ensuring consistency between the model and learned policies. Simulation results across diverse scenarios demonstrate that the proposed approach reduces the mean delay by 40-60% and significantly decreases fallback usage compared to baseline methods. These results highlight the effectiveness of the proposed framework in enabling adaptive and delay-efficient control in next-generation LEO satellite systems.
Wafa Hasanain, Pablo G. Madoery, H. Yanikomeroglu et al.· IEEE Open Journal of the Com...· 0 citations
In LEO satellite networks utilizing beam hopping (BH), resource allocation plans must be committed well in advance. This inherent operational delay necessitates predicting future user demand during the planning phase. Such predictive agility is particularly crucial for military applications, where unpredictable tactical environments demand low-latency, resilient communication links. However, existing forecasting models are typically evaluated based on standalone accuracy, ignoring their cross-layer impact on overall network performance. To address this gap, we evaluate two distinct demand forecasting solutions within a comprehensive, full-stack LEO satellite simulation compliant with DVB-S2X standards. Beyond prediction accuracy, we examine how incorporating user demand forecasts into BH plan generation impacts key network metrics, particularly delay and jitter. We evaluate these forecasting solutions alongside a static allocation baseline. Our results demonstrate that forecast-based dynamic planning reduces delay by 10-40% across the beams under certain load conditions compared to static allocation methods. Crucially, marginal improvements in predictive accuracy do not translate into proportional network metric gains. While the evaluated forecasting solutions differ by 14-16% in Normalized Mean Square Error (NMSE), this discrepancy yields less than a 1% reduction in delay and produces nearly identical jitter characteristics. These findings suggest that when designing user demand forecasting solutions for practical LEO deployments, prioritizing system scalability may be more valuable than chasing minor accuracy enhancements.
Yekta Demirci, Guillaume Mantelet, Stéphane Martel et al.· 0 citations
The growing demand for adaptive, resilient communication in software-defined networking (SDN)-enabled low Earth orbit (LEO) satellite networks underscores the importance of optimized SDN controller management. In particular, the overhead introduced by setup penalties and constraints on controller capacity significantly impacts key performance metrics, including satellite-to-controller delay, reassignment frequency, and the number of concurrently active controllers. These factors collectively influence the network’s ability to provide low-latency services while maintaining resource utilization in dynamic orbital environments. To investigate these dynamics, we develop a simulation framework based on OMNeT++ and INET, extending the open-source satellite simulator OS3 with enhanced satellite mobility and dynamic routing. The framework integrates SDN functionality into LEO satellite networks using the OpenFlow protocol, enabling centralized, programmable control with real-time adaptability. A central challenge in such networks is not only assigning satellites to appropriate controllers, but also dynamically activating and deactivating SDN controllers to match evolving topologies and traffic demands. To address this, we consider the baseline Dynamic Satellite-to-Controller Assignment (DSCA), the proposed Optimal Dynamic Satellite-to-Controller Assignment (Opt-DSCA), and their scalable heuristic variants (H-DSCA and H-Opt-DSCA). Opt-DSCA jointly minimizes satellite-to-controller delay and the number of active controllers, while incorporating setup penalties and activation costs to discourage unnecessary satellite migrations and redundant controller utilization. Simulation results reveal that both setup penalties and controller activation thresholds are critical determinants of system behavior across both optimization-based and heuristic schemes. Lower penalties enhance adaptability but result in more frequent reassignments and higher variability in controller state transitions. Conversely, higher penalties improve robustness by limiting controller switching, though at the cost of reduced flexibility and increased latency. These findings highlight the necessity of jointly optimizing reassignment policies and controller activation strategies to support robust, low-latency, and resource-aware SDN architectures for large-scale LEO satellite constellations.
Wafa Hasanain, Pablo G. Madoery, H. Yanikomeroglu et al.· IEEE Open Journal of the Com...· 0 citations