Robust Space-to-Ground Laser Communication Downlink Scheduling Under Uncertainty Derived From Multisource

Space-to-ground laser communication (SGLC) utilizes laser beams to establish high-capacity bidirectional links between satellites and ground stations (GSs). However, its performance is significantly impaired by cloud cover and atmospheric turbulence. In practical SGLC downlink scheduling, uncertainties derived from such atmospheric conditions are inevitable. To the best of our knowledge, this work is the first to tackle downlink scheduling for SGLC under such uncertainties, with the objective of maximizing the total amount of data downloaded from satellites. We present a robust formulation of the scheduling problem that incorporates multisource uncertainties through budgeted uncertainty sets, consequently transforming the original problem into a bi-level optimization one with conflicting objectives. To address such problems, we first utilize McCormick envelopes to linearize bilinear terms in the inner optimization problem. We subsequently propose a KKT condition-based method to convert the bi-level structure into a single-level reformulation, which is further transformed into a tractable mixed-integer linear programming (MILP) model. Compared with the existing method, which does not consider such uncertainties, the proposed approach achieves robust scheduling strategies with respect to data throughput.