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

Auction-theoretic, feasibility-aware scheduling for GSaaS providers under orbital dynamics and strategic bidding

Zhiying Wang; Xiaojian Wang; Huayue Gu; Zhishan Guo; Ruozhou Yu· 2026· DOI 10.48550/arXiv.2608.22222

The core problem

The rapid growth of Low Earth Orbit (LEO) 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. The authors 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

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. The evaluation compares STAR-GS against a mixed-integer linear programming (MILP) optimal solver and several heuristics. Across varying numbers of tasks, ground stations, and bandwidth capacities, STAR-GS attains revenue within a small gap of the MILP optimum while reducing computation time by orders of magnitude. For instance, with 900 tasks, STAR-GS completes scheduling in a fraction of the time required by MILP, which becomes intractable. The mechanism also maintains truthfulness and feasibility: no admitted task misses its deadline, and no task can improve its utility by misreporting its valuation. These results demonstrate that integrating auction theory with schedulability analysis yields a practical and scalable solution for commercial GSaaS providers.

The rapid growth of Low Earth Orbit (LEO) 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. The authors 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.

The GSaaS scheduling problem is formulated as a revenue-maximization problem over a set of downlink tasks T = {1, \dots, n}, a set of ground stations G = {1, \dots, m}, and a discrete scheduling horizon. Each task i is characterized by a data volume d_{i}, a deadline D_{i}, a visibility window [a_{i}, b_{i}] during which it can communicate with a given ground station, and a private valuation v_{i} reported as a bid \overset{v}{^}_{i} . The provider selects an admission set A \subseteq T, assigns each admitted task to a ground station g (i) \in G, and allocates bandwidth x_{i} (t) over time. The optimization is:

The key insight of STAR-GS is the tight coupling of economic incentives with physical-layer scheduling constraints. Traditional auction mechanisms for resource allocation often ignore feasibility, leading to admitted tasks that cannot be completed. STAR-GS avoids this by embedding schedulability analysis into the admission and assignment stages, ensuring that every admitted task is feasible under EDF scheduling. The critical-payment rule guarantees truthfulness, which is essential in settings where satellite operators are strategic and may misreport their valuations to gain advantage. The best-fit assignment heuristic balances load across ground stations while respecting visibility windows, and the EDF-based bandwidth allocation ensures deadline compliance. The approach is ground-station-centric, meaning the provider optimizes its own revenue while maintaining fairness and efficiency. Limitations include the assumption of known visibility windows and deterministic data volumes; future work could address uncertainty in orbital dynamics or task arrivals. Overall, STAR-GS offers a principled and scalable framework for GSaaS resource management, bridging auction theory and real-time scheduling.

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