Statistically Robust Resource Block Allocation for Satellite Communications
2026-06-01 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors address the challenge of estimating the right amount of resources, called resource blocks (RBs), needed for a satellite system before it is launched. Because fixing satellite capacity after launch is costly, they develop a method to predict how many RBs to allocate while considering how signal strength changes across the satellite coverage area. They use two methods: one uses simulations to estimate the needed RBs for a desired quality level, and the other provides a safe upper limit using math. Together, these methods help ensure satellites have enough capacity despite unpredictable signal variations.
resource blockssatellite footprintsignal attenuationspatial covarianceGaussian attenuation fieldMonte Carlo simulationquality of servicedimensioningoverload probability
Authors
Chaitanya Manapragada, Laurent Decreusefond, Philippe Martins
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.