LocDreamer: World Model-Based Learning for Joint Indoor Tracking and Anchor Scheduling

Geng Wang, Zhouyou Gu, Shenghong Li, Peng Cheng, Jihong Park, Branka Vucetic, Yonghui Li

Keywords: World model, indoor localization and tracking, anchor scheduling, reinforcement learning

Abstract:

Accurate, resource-efficient localization and tracking enables numerous location-aware services in next-generation wireless networks.
However, existing machine learning-based methods often require large labeled datasets while overlooking spectrum and energy efficiencies. To fill this gap, we propose LocDreamer, a world model (WM)-based framework for joint target tracking and scheduling of localization anchors. LocDreamer learns a WM that captures the latent representation of the target motion and localization environment, thereby generating synthetic measurements to imagine arbitrary anchor deployments. These measurements enable imagination-driven training of both the tracking model and the reinforcement learning (RL)-based anchor scheduler that activates only the most informative anchors, which significantly reduce energy and signaling costs while preserving high tracking accuracy. Experiments on a real-world indoor dataset demonstrate that LocDreamer substantially improves data efficiency and generalization, outperforming conventional Bayesian filter with random scheduling by 37\% in tracking accuracy, and achieving 86\% of the accuracy of same model trained directly on real data.

Submission Number: 23