AI & ML
Network-Aware Progressive Replacement Sequence Planning for Laser-Enhanced BeiDou Navigation Constellations
arXiv eess.SY0 views
arXiv:2607.25235v2 Announce Type: replace Abstract: The early satellites of the third-generation BeiDou Navigation Satellite System (BDS-3) are approaching the end of their design lifetime, making progressive constellation replacement an inevitable engineering task. Meanwhile, laser inter-satellite links (LISLs) offer high-precision time transfer, ranging, and high-rate data forwarding, providing a promising upgrade path for future BeiDou satellites. This paper investigates the replacement-sequence planning problem for progressively replacing legacy microwave satellites with laser-enabled satellites from a networking perspective. Since practical replacement decisions remain constrained by legacy satellite lifetime, on-orbit condition, laser satellite manufacturing readiness, and launch scheduling, networking information is used as an auxiliary criterion in the actual engineering replacement process. An integer linear programming model is developed to evaluate the network value of intermediate laser--microwave hybrid states. Based on this common state evaluator, two complementary planning methods are developed. A priority-aware exact method supports round-wise network-assisted launch decisions, while a visibility-guided adaptive large-neighborhood search is used for full-sequence planning and provides a full-horizon network-oriented near-optimal reference. Simulations using the actual BDS-3 constellation show that stronger network participation leads to earlier and larger ground-connected laser subnetworks, fewer microwave-dependent satellites, better coordination between laser-backbone growth and high-rate ground access, and improved residual microwave-layer performance. These results provide a quantitative basis for incorporating network performance as an auxiliary criterion in future BeiDou replacement planning.
Source:https://arxiv.org/abs/2607.25235PreviousCalibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service marketsNextPhysically Constrained Agentic AI for Energy Scheduling
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