LocDreamer: World Model-Based Learning for Joint Indoor Tracking and Anchor Scheduling
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915785697394688 |
|---|---|
| author | Wang, Geng Gu, Zhouyou Li, Shenghong Cheng, Peng Park, Jihong Vucetic, Branka Li, Yonghui |
| author_facet | Wang, Geng Gu, Zhouyou Li, Shenghong Cheng, Peng Park, Jihong Vucetic, Branka Li, Yonghui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_08204 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LocDreamer: World Model-Based Learning for Joint Indoor Tracking and Anchor Scheduling Wang, Geng Gu, Zhouyou Li, Shenghong Cheng, Peng Park, Jihong Vucetic, Branka Li, Yonghui Signal Processing 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. |
| title | LocDreamer: World Model-Based Learning for Joint Indoor Tracking and Anchor Scheduling |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2602.08204 |