Statistically Robust Resource Block Allocation for Satellite Communications

2026-06-01

AI Summary

This study addresses the challenge of accurately estimating resource block (RB) capacity prior to satellite communication system deployment, where unknown spatial fading correlations pose significant overload risks. The work presents the first RB-dimensioning rule tailored for satellite coverage areas, modeling the spatial fading covariance structure via a Gaussian random field. By integrating stochastic user sampling, Monte Carlo simulations, and concentration inequality analysis, the proposed framework delivers a robust RB budget estimate under a prescribed target overload probability. Furthermore, it derives a conservative analytical upper bound on the overload probability for the resulting RB allocation. This approach provides system planners with a reliable foundation that combines simulation-based accuracy with rigorous theoretical guarantees for capacity planning in satellite networks.

Abstract

It is critical to dimension (accurately estimate capacity of) a satellite system prior to deployment, as it is very expensive to reconfigure launched satellite systems that fail to meet demand or that waste capacity. The fundamental requirement is a dimensioning rule for resource blocks (RBs) given a satellite footprint and a target overload probability (target Quality-of-Service). The rule must be robust to the spatial covariance structure of signal attenuation, which is generally unknown both at the time of pre-deployment dimensioning and afterwards. Existing approaches address parts of this problem, but there does not yet exist a footprint-level RB dimensioning rule for the satellite context. We develop such a rule: starting with a Gaussian attenuation field that induces a covariance structure inspired by classical work on spatial covariance of attenuation, we sample users at random along with their field-based attenuation values, and estimate aggregate RB demand for a target overload probability. We do this in two complementary ways: a Monte Carlo route that gives a simulation-derived RB budget for a given target overload probability, and a concentration route that gives a conservative analytic upper bound on the target overload probability for a given RB budget (such as the one obtained through simulation). Taken together, these complementary approaches give a principled way to dimension RBs for a satellite footprint under spatially correlated attenuation.

Problem

• Resource Block Allocation
• Satellite Communications
• Spatial Covariance
• Overload Probability
• System Dimensioning

Innovation

• resource block allocation
• satellite communications
• spatial covariance
• statistical robustness
• dimensioning rule