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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.08852 |
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| _version_ | 1866912969049243648 |
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| author | Chou, Po-Heng Wang, Chiapin Chen, Kuan-Hao Hsiao, Wei-Chen |
| author_facet | Chou, Po-Heng Wang, Chiapin Chen, Kuan-Hao Hsiao, Wei-Chen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_08852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DRL-Based Beam Positioning for LEO Satellite Constellations with Weighted Least Squares Chou, Po-Heng Wang, Chiapin Chen, Kuan-Hao Hsiao, Wei-Chen Signal Processing Machine Learning Networking and Internet Architecture 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. |
| title | DRL-Based Beam Positioning for LEO Satellite Constellations with Weighted Least Squares |
| topic | Signal Processing Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2511.08852 |