Physics-Informed Digital Twins for Channel Estimation and Traffic Prediction of Non-Terrestrial Networks

In non-terrestrial networks (NTN), high-speed satellite orbital motion, limited pilot signaling resources, and spatiotemporally heterogeneous traffic make accurate channel and traffic state characterization particularly challenging. In this paper, we propose a physics-informed digital twin (DT) framework for channel estimation and traffic prediction. Particularly, it formulates channel state information (CSI) reconstruction as a controllable generative process guided by physical-prior tensors. Through a physics-aware attention mechanism, it effectively reconstructs the real-time full-resolution CSI from highly sparse and outdated pilots. Then, we develop an orbit-adaptive spatiotemporal graph neural network for traffic prediction. By leveraging a dual-stream attention mechanism to capture intra- and inter-plane spatial dependencies and a gated recurrent unit to model temporal evolution, the neural network effectively predicts stochastic traffic residuals, which are integrated with the deterministic physical traffic baseline to form the complete traffic state. To evaluate the proposed DT framework, we establish a high-fidelity NTN DT simulation platform based on real-world Starlink ephemeris, global population, and ERA5 weather data. Experimental results demonstrate that our framework significantly outperforms state-of-the-art baselines in both CSI reconstruction and traffic prediction accuracy.

A Novel 6G Dynamic Channel Map Based on a Hybrid Channel Model

In the sixth generation (6G) wireless communication networks, the device density, antenna number, and the complexity of communication scenarios will significantly increase, which brings great challenges for system design and network optimization. By obtaining channel information in advance, channel map has become a promising solution to these challenges in 6G era. However, conventional channel maps cannot be updated in time as physical environment changes. To solve the problem, a novel dynamic channel map (DCM) is proposed in this work. For DCM construction, we further present a ray tracing (RT) and geometric stochastic hybrid channel model (RT-GSHCM), which pre-constructs the DCM offline by RT and updates it online by geometry-based stochastic channel model (GBSM). By this way, the DCM can provide time-varying channel information and channel properties while matintaining accuracy. Next, a channel measurement campaign is conducted, and the measurement results are compared with the RT-GSHCM, RT, and GBSM. The comparison results validate the accuracy of DCM. Meanwhile, the time cost on DCM update is compared with that of conventional channel maps, illustrating the time-efficiency of DCM. Finally, important statistical channel properties of RT-GSHCM are further derived, analyzed, and compared under different configurations of interaction objects in physical environment.

Data-Model Co-Driven Continuous Channel Map Construction: A Perceptive Foundation for Embodied Intelligent Agents in 6G Networks

Future 6G networks will host massive numbers of embodied intelligent agents, which require real-time channel awareness over continuous-space for autonomous decision-making. By pre-obtaining location-specific channel state information (CSI), channel map can be served as a foundational world model for embodied intelligence to achieve wireless channel perception. However, acquiring CSI via measurements is costly, so in practice only sparse observations are available, leaving agents blind to channel conditions at unvisited locations. Meanwhile, purely model-driven channel maps can provide dense CSI but often yields unsatisfactory accuracy and robustness, while purely data-driven interpolation from sparse measurements is computationally prohibitive for real-time updates. To address these challenges, this paper proposes a data-model co-driven (DMcD) framework that performs a two-stage interpolation toward a space-time continuous channel map, First, a hybrid ray tracing and geometry-based channel model (H-RT/GBSM) is developed to capture dynamic scatterers, providing dense, time-variant channel properties that match measurement statistics as a physically consistent prior. Then, an inductive edge-conditioned graph neural network (InductE-GNN) fuses the prior with sparse measurements to perform real-time spatial interpolation, enabling rapid online adaptation without retraining, ensuring the synchronization with the dynamic physical reality. Evaluations with measured datasets show that the proposed DMcD framework significantly outperforms data-only and model-only baselines, providing accurate and queryable channel information for embodied intelligent agents.