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arXiv2026

SkyShare: Constellation-wide Sky Sharing for LEO-Radio Astronomy Coexistence

Rapidly growing low-Earth-orbit (LEO) constellations increasingly operate in the spectrum shared with radio astronomy services (RAS), creating escalating interference risks for sensitive scientific observations. Existing mitigation mechanisms rely on reactive beam steering, or avoidance near observatories but fail to account for aggregate sidelobe emissions-leading to residual interference and substantial, unnecessary capacity loss. We present SkyShare, a constellation-wide sky-sharing system that enables predictive, interference-aware spot beam scheduling to protect radio astronomy while preserving network coverage. SkyShare integrates high-fidelity orbital prediction with International Telecommunication Union (ITU)-compliant Equivalent Power Flux Density (EPFD) modeling, and real-time observatory data via Operational Data Sharing (ODS) to jointly optimize beam-cell assignments over observation windows. To make constellation-scale coordination tractable, we introduce a concept of EPFD-budgeted Region-of-Interest(RoI) that bounds residual sidelobe interference while confining optimization to a minimal, provably sufficient set of cells. Building on RoI, we formulate LEO-RAS coexistence as a scalable scheduling problem and design SkySched, a flow-based algorithm that is optimal in special cases and yields scalable near-optimal solutions in the general NP-hard setting. SkyShare operates entirely in the control plane and requires no satellite hardware changes. Using real Starlink constellation geometries, we evaluate SkyShare across 25 single-dish, Ku-band RAS sites worldwide. Compared to Starlink boresight avoidance, SkyShare reduces unserved cells by up to 90.68% while remaining within EPFD limits.

cs.NI↗

arXiv2026

OpenTwin: Closed-Loop Digital Twins for Trustworthy Policy Deployment in Open RAN

In open radio access networks (O-RAN), the near-real-time RAN Intelligent Controller (RIC) hosts third-party xApps whose training and validation risk disrupting the operational network. Disaggregation amplifies this risk, as no single party can certify a control action end to end. Indeed, our testbed shows an E2 control request reported as successful while the base station never applies the change. Digital twins (DTs) promise safe policy evaluation, yet existing O-RAN DTs largely rely on hand-crafted models, run without feedback from the deployment, and never say how often their predictions can be trusted. To fill this gap, we present OpenTwin, a closed-loop framework that learns the simulator configuration reproducing an operating deployment streamed measurements, certifies the resulting DT by re-simulation, calibrates it online, and evaluates each xApp action before it executes on the physical network. Every trust decision carries an error rate bounded by an operator-prescribed budget, with a drift detector limiting needless resynchronizations and a conformal fidelity gate admitting an action only when its predicted outcome range lies in the safe region. Extensive experiments across simulation and real-world testbeds confirm single-digit percentage error in reproduced measurements, false approvals an order of magnitude below every budget from 0.05 to 0.30, and a gated energy-saving xApp that retains roughly 40% of the saving achievable with perfect foresight.

cs.NI↗