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

arXiv: https://arxiv.org/abs/2503.20658 · HTML: https://mlsanity.com/paper/2503.20658/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 26 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [Open RAN xApps Design and Evaluation: Lessons Learnt and Identified Challenges](https://mlsanity.com/paper/2311.04380/) (2023)
- [On the use of Probabilistic Forecasting for Network Analysis in Open RAN](https://mlsanity.com/paper/2407.14375/) (2024)
- [On the Impact of PRB Load Uncertainty Forecasting for Sustainable Open RAN](https://mlsanity.com/paper/2407.14400/) (2024)

## Built on by (most cited first, 1 of 1)

- [Goal-oriented probabilistic forecasting for dynamic PRB allocation in 5G networks](https://mlsanity.com/paper/2609.17297/) (2026)

## Abstract (arXiv)

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.