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arXiv 2510.03438cs.NIcs.AIcs.SYeess.SY
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面向大型LEO星座的可扩展地面站选择方法

Scalable Ground Station Selection for Large LEO Constellations

Stanford University(斯坦福大学)

Grace Ra Kim, Duncan Eddy, Vedant Srinivas, Mykel J. Kochenderfer

AI总结:

针对大型LEO星座地面站选择中传统混合整数规划难以扩展的问题,提出分层分解聚类框架,在保证近最优性能的同时实现可扩展选址,真实场景验证了其有效性。

摘要:

高效的地面站选择对低轨(LEO)卫星星座至关重要,可最小化运营成本、最大化数据下行链路容量并减少接入窗口之间的通信间隙。传统地面站选择通常从地面站即服务(GSaaS)提供商提供的固定位置集合中进行选择,这有助于将问题范围缩小为在现有基础设施上优化选址。然而,当规模扩大时,尤其是考虑多个提供商和大型卫星星座时,使用现有混合整数规划方法求解地面站的全局最优解会迅速变得难以处理。为解决这一问题,我们提出了一种可扩展的分层框架,将全局选择问题分解为单卫星、短时间窗口的子问题。对每个子问题的最优地面站选择结果进行聚类,以识别所有分解案例中始终具有高价值的位置。随后将聚类级集合匹配回最接近的GSaaS候选站点,以生成全局可行解。该方法在保持近似最优性能的同时实现了可扩展的协调。我们在合成Walker-Star测试用例(1-10颗卫星、1-10个地面站)上评估了方法的性能,所有测试用例的解均达到全局整数规划(IP)最优值的95%以上。在Capella Space(5颗卫星)、ICEYE(40颗)和Planet的Flock(96颗)上的真实场景评估表明,当精确IP解无法扩展时,我们的框架仍能提供高质量的站点选择结果。

英文摘要:

Effective ground station selection is critical for low Earth orbiting (LEO) satellite constellations to minimize operational costs, maximize data downlink volume, and reduce communication gaps between access windows. Traditional ground station selection typically begins by choosing from a fixed set of locations offered by Ground Station-as-a-Service (GSaaS) providers, which helps reduce the problem scope to optimizing locations over existing infrastructure. However, finding a globally optimal solution for stations using existing mixed-integer programming methods quickly becomes intractable at scale, especially when considering multiple providers and large satellite constellations. To address this issue, we introduce a scalable, hierarchical framework that decomposes the global selection problem into single-satellite, short time-window subproblems. Optimal station choices from each subproblem are clustered to identify consistently high-value locations across all decomposed cases. Cluster-level sets are then matched back to the closest GSaaS candidate sites to produce a globally feasible solution. This approach enables scalable coordination while maintaining near-optimal performance. We evaluate our method's performance on synthetic Walker-Star test cases (1-10 satellites, 1-10 stations), achieving solutions within 95% of the global IP optimum for all test cases. Real-world evaluations on Capella Space (5 satellites), ICEYE (40), and Planet's Flock (96) show that while exact IP solutions fail to scale, our framework continues to deliver high-quality site selections.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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