Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing With Zero-Shot Reinforcement Learning
Artikel i vetenskaplig tidskrift, 2025

Millimeter-wave (mmWave) communication is a vital component of future generations of mobile networks, offering not only high data rates but also precise beams, making it ideal for indoor navigation in complex environments. However, the challenges of multipath propagation and noisy signal measurements in indoor spaces complicate the use of mmWave signals for navigation tasks. Traditional physics-based methods, such as following the angle of arrival (AoA), often fall short in complex scenarios, highlighting the need for more sophisticated approaches. Digital twins, as virtual replicas of physical environments, offer a powerful tool for simulating and optimizing mmWave signal propagation in such settings. By creating detailed, physics-based models of real-world spaces, digital twins enable the training of machine learning algorithms in virtual environments, reducing the costs and limitations of physical testing. Despite their advantages, current machine learning models trained in digital twins often overfit specific virtual environments and require costly retraining when applied to new scenarios. In this paper, we propose a physics-informed reinforcement learning (PIRL) approach that leverages the physical insights provided by digital twins to shape the reinforcement learning (RL) reward function. By integrating physics-based metrics such as signal strength, AoA, and path reflections into the learning process, PIRL enables efficient learning and improved generalization to new environments without retraining. Digital twins play a central role by providing a versatile and detailed simulation environment that informs the RL training process, reducing the computational overhead typically associated with end-to-end RL methods. Our experiments demonstrate that the proposed PIRL, supported by digital twin simulations, outperforms traditional heuristics and standard RL models, achieving zero-shot generalization in unseen environments and offering a cost-effective, scalable solution for wireless indoor navigation.

physics-informed learning

wireless indoor navigation

millimeter-wave (mmWave) communication

zero-shot generalization

Digital twin

reinforcement learning (RL)

Författare

Tao Li

New York University

Haozhe Lei

New York University

Hao Guo

Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk

New York University

Mingsheng Yin

New York University

Yaqi Hu

New York University

Quanyan Zhu

New York University

Sundeep Rangan

New York University

IEEE Open Journal of the Communications Society

2644125X (eISSN)

Vol. 6 2356-2372

6G kommunikationsmedveten navigering för robotdirektiv

Vetenskapsrådet (VR) (2023-00272), 2023-07-01 -- 2025-06-30.

Ämneskategorier (SSIF 2025)

Datavetenskap (datalogi)

Signalbehandling

DOI

10.1109/OJCOMS.2025.3552277

Mer information

Senast uppdaterat

2025-05-05