{"ID":2842529,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08852","arxiv_id":"2511.08852","title":"DRL-Based Beam Positioning for LEO Satellite Constellations with Weighted Least Squares","abstract":"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. The proposed hybrid design targets an accuracy-runtime trade-off rather than absolute supervised optimality. In a representative 2-D setting with 10 visible satellites, the proposed approach achieves sub-meter accuracy (0.395m RMSE) with low computational overhead, supporting practical deployment for resource-constrained LEO payloads.","short_abstract":"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 dire...","url_abs":"https://arxiv.org/abs/2511.08852","url_pdf":"https://arxiv.org/pdf/2511.08852v4","authors":"[\"Po-Heng Chou\",\"Chiapin Wang\",\"Kuan-Hao Chen\",\"Wei-Chen Hsiao\"]","published":"2025-11-12T00:14:10Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.LG\",\"cs.NI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}