DRL-Based Beam Positioning for LEO Satellite Constellations with Weighted Least Squares

Abstract--In this paper, we propose a reinforcement learning based beam weighting framework that couples a policy networ k with an augmented weighted least squares (WLS) estimator fo r accurate and low-complexity positioning in multi-beam LEO constellations. Unlike conventional geometry or CSI-depe ndent approaches, the policy learns directly from uplink pilot re sponses and geometry features, enabling robust localization witho ut explicit CSI estimation. Across representative scenar ios, the proposed method reduces the mean positioning error by 99.3% compared with the geometry-based baseline, achievin g 0.395 m RMSE with near real-time inference. The integration of terrestrial, aerial, and satellite segm ents into a unified ground-air-space architecture has emerged as a key enabler for future sixth-generation (6G) networks, promising seamless connectivity, low latency, and global coverage [1]. Among these, low Earth orbit (LEO) satellite constellations are particularly attractive due to their wi de coverage, rapid revisit capability, and suitability for de lay-sensitive services.