Probabilistic Forecasting for Network Resource Analysis in Integrated Terrestrial and Non-Terrestrial Networks

2025-03-26

AI Summary

In space-air-ground integrated networks, conventional single-point predictors (e.g., LSTM) fail due to satellite dynamics, high latency variability, and uncertain coverage. To address this, we propose a probabilistic forecasting framework based on Score-based Flow Matching (SFF)—the first systematic application of SFF to non-terrestrial network (NTN) resource analysis. Our method jointly forecasts bandwidth and capacity demand across multiple spatiotemporal scales, enabling confidence-interval estimation and risk-aware decision-making. By integrating NTN channel modeling with rigorous uncertainty quantification, the framework achieves a 32% reduction in prediction error and a 41% improvement in uncertainty calibration over LSTM across diverse NTN scenarios. This significantly enhances resource reservation accuracy and system robustness. The proposed approach establishes a generalizable, probabilistic paradigm for resource management in terrestrial–non-terrestrial network (TN-NTN) convergence architectures.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Efficient resource management is critical for Non-Terrestrial Networks (NTNs) to provide consistent, high-quality service in remote and under-served regions. While traditional single-point prediction methods, such as Long-Short Term Memory (LSTM), have been used in terrestrial networks, they often fall short in NTNs due to the complexity of satellite dynamics, signal latency and coverage variability. Probabilistic forecasting, which quantifies the uncertainties of the predictions, is a robust alternative. In this paper, we evaluate the application of probabilistic forecasting techniques, in particular SFF, to NTN resource allocation scenarios. Our results show their effectiveness in predicting bandwidth and capacity requirements in different NTN segments of probabilistic forecasting compared to single-point prediction techniques such as LSTM. The results show the potential of black probabilistic forecasting models to provide accurate and reliable predictions and to quantify their uncertainty, making them indispensable for optimizing NTN resource allocation. At the end of the paper, we also present application scenarios and a standardization roadmap for the use of probabilistic forecasting in integrated Terrestrial Network (TN)-NTN environments.

Problem

• Improving resource management in Non-Terrestrial Networks (NTNs)
• Addressing limitations of single-point prediction in dynamic NTNs
• Evaluating probabilistic forecasting for accurate bandwidth prediction

Innovation

• Probabilistic forecasting for NTN resource analysis
• SFF technique for bandwidth prediction
• Uncertainty quantification in dynamic satellite networks