AI RESEARCH
DRL-Based Beam Positioning for LEO Satellite Constellations with Weighted Least Squares
arXiv CS.LG
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ArXi:2511.08852v3 Announce Type: replace-cross This paper investigates a lightweight deep reinforcement learning (DRL)-assisted weighting framework for CSI-free multi-satellite positioning in LEO constellations, where each visible satellite provides one serving beam (one pilot response) per epoch. A discrete-action Deep Q-Network (DQN) learns satellite weights directly from received pilot measurements and geometric features, while an augmented weighted least squares (WLS) estimator provides physics-consistent localization and jointly estimates the receiver clock bias.