Author

Ruozhou Yu

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Preprint Aug 2026

STAR-GS: Truthful and Visibility-Aware Resource Scheduling for Ground Station as a Service

The rapid growth of Low Earth Orbit satellite constellations has created increasing demand for efficient and scalable downlink services. Ground Station as a Service (GSaaS) provides an on-demand access model for satellite operators, but commercial GSaaS providers must schedule limited ground-station bandwidth among multiple satellites with heterogeneous data demands, overlapping visibility windows, strict deadlines, and strategic bidding behaviors. This paper studies GSaaS resource scheduling from a ground-station-centric perspective, where the provider jointly determines task admission, ground-station assignment, bandwidth allocation, and payments. Under satellite orbital dynamics, bandwidth constraints, and downlink task deadlines, maximizing the provider's revenue is NP-hard. To address this challenge, we propose STAR-GS, a truthful and feasibility-aware scheduling mechanism that combines bid-aware admission control, best-fit ground-station assignment, Earliest Deadline First (EDF)-based bandwidth scheduling, and critical-payment pricing. By integrating auction theory with schedulability analysis, STAR-GS incentivizes task owners to truthfully report their private valuations while ensuring that admitted tasks can be feasibly completed before their deadlines. Simulations using Ansys Systems Tool Kit (STK) show that STAR-GS consistently achieves higher revenue than heuristic baselines, obtains near-MILP performance with substantially lower runtime, and scales smoothly to workloads containing up to 900 tasks.

Zhiying Wang, Xiaojian Wang, Huayue Gu et al. · 0 citations

Conference Jul 2026

Twin-Guided Meta Learning for Generalizable UAV Trajectory Planning in Low-Altitude Wireless Networks

Ensuring QoS provisioning in low-altitude wireless networks requires UAV positioning and navigation strategies that adapt to dynamic environments and generalizes across heterogeneous network scenarios. This paper proposes a digital twin (DT)-assisted meta reinforcement learning framework for multi-agent UAV trajectory planning. A high-fidelity network DT serves as a supervisory layer to generate key performance indicators (KPIs) and fine-grained channel knowledge, which guides both domain-specific learning and cross-domain validation. Building on the twin-informed UAV landmarks, we then develop a weakness-aware meta learning scheme: in the inner loop, agents are trained cooperatively toward the self-discovered landmarks under dynamic conditions; in the outer loop, navigation policies are evaluated via the DT to identify bottlenecks and generate targeted hard scenarios, enabling robust adaptation across diverse scenarios. Extensive simulations show that our framework achieves up to 4× higher service coverage compared to baselines, while the target-aware outer-loop adaptation further improves cross-scene performance and model generalization.

Jiayuan Huang, E. Tucker, Ruozhou Yu et al. · International Conference on... · 0 citations