A Robust Online Decision-Making Method Based on DNN Distillation for Energy-Constrained Scenarios in LEO Satellite Passes

The rapid motion of Low Earth Orbit (LEO) satellites causes highly time-varying downlink quality, while the radio-frequency energy available during a single pass is strictly limited. Dynamic Programming (DP) optimizes discrete power allocation, but repeated optimization occupies processing capacity shared with other onboard tasks. This motivates compact policies that retain useful transmission performance with lower online computational cost. Hard-label imitation learning neither captures the value similarity among power actions nor guarantees the energy constraint. This paper proposes a lightweight online power-control method based on value-aware policy distillation. DP is used offline to generate complete state–action values, which are converted into soft supervision targets for training a compact neural network. During online inference, energy-feasible action masking removes power levels that exceed the remaining energy budget, thereby ensuring strict constraint satisfaction. The resulting policy observes the actual link and geometric states at each time slot and dynamically reallocates the remaining energy. Simulation results show near-DP average performance under nominal conditions, improvement over hard-label training, and adaptation to signal-to-noise ratio (SNR) forecast errors and along-track ephemeris errors. Rolling cross-level greedy achieves better transmission performance, while the proposed policy uses substantially less CPU time in the tested implementations, illustrating a performance–computation trade-off.