DeepTelecom: A Differentiable Multimodal Framework for Channel Twinning in Dynamic Heterogeneous Wireless Environments
Abstract
Large-scale multimodal data are a key enabler for integrating large generative artificial intelligence models into wireless networks. However, existing wireless datasets offer limited support for heterogeneous environments, particularly in terms of reusable high-fidelity scenes, scalable differentiable ray tracing, and synchronized multimodal data. These limitations are more pronounced in mobile and aerial scenarios with time-varying environments and channels. To address these limitations, we propose DeepTelecom, a material-aware, high-fidelity multimodal channel dataset and reproducible generation framework for networks involving unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces. DeepTelecom refines coarse indoor/outdoor reconstructions and uses a large language model to map segmented surfaces to frequency-dependent electromagnetic parameters, yielding material-aware Level of Detail 3 digital twins. Then, a differentiable GPU-accelerated Sionna ray tracer simulates propagation across the sub-6 GHz/millimeter-wave/sub-terahertz bands, and specifically captures rotor-induced micro-Doppler signatures of stationary or moving UAVs at different pitch angles and translational velocities. The framework synchronously produces channel tensors, coverage maps, transmission videos, multi-view images, and weather-conditioned renderings, and supports differentiable material calibration from sparse channel anchors. The released dataset contains 10 material-aware indoor/outdoor scenes and 50,000 synchronized UAV samples; the framework supports scalable generation of additional data for user-defined scenes and system configurations. In 3D UAV localization, DeepTelecom reduces the 90th-percentile error from 31.05 m to 2.43 m compared with schemes considering only channel state information, demonstrating its significance for artificial-intelligence-enabled wireless systems.
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BibTeX
@misc{wang2026deeptelecom,
title = {{DeepTelecom: A Differentiable Multimodal Framework for Channel Twinning in Dynamic Heterogeneous Wireless Environments}},
author = {Bohao Wang and Tianen Yuan and William Yen and Zehua Jiang and Chongwen Huang and Li Wei and Chen Zhu and Jiguang He and Hang Zou and Lina Bariah and Brahim Mefgouda and Anis Bara and Yongliang Shen and Zhaoyang Zhang and Merouane Debbah},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.14545},
eprint = {2610.14545},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.14545},
}
RIS
TY - UNPB
TI - DeepTelecom: A Differentiable Multimodal Framework for Channel Twinning in Dynamic Heterogeneous Wireless Environments
AU - Bohao Wang
AU - Tianen Yuan
AU - William Yen
AU - Zehua Jiang
AU - Chongwen Huang
AU - Li Wei
AU - Chen Zhu
AU - Jiguang He
AU - Hang Zou
AU - Lina Bariah
AU - Brahim Mefgouda
AU - Anis Bara
AU - Yongliang Shen
AU - Zhaoyang Zhang
AU - Merouane Debbah
PY - 2026
DA - 2026/10/10
PB - arXiv
DO - 10.48550/arxiv.2610.14545
UR - https://arxiv.org/abs/2610.14545
AB - Large-scale multimodal data are a key enabler for integrating large generative artificial intelligence models into wireless networks. However, existing wireless datasets offer limited support for heterogeneous environments, particularly in terms of reusable high-fidelity scenes, scalable differentiable ray tracing, and synchronized multimodal data. These limitations are more pronounced in mobile and aerial scenarios with time-varying environments and channels. To address these limitations, we propose DeepTelecom, a material-aware, high-fidelity multimodal channel dataset and reproducible generation framework for networks involving unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces. DeepTelecom refines coarse indoor/outdoor reconstructions and uses a large language model to map segmented surfaces to frequency-dependent electromagnetic parameters, yielding material-aware Level of Detail 3 digital twins. Then, a differentiable GPU-accelerated Sionna ray tracer simulates propagation across the sub-6 GHz/millimeter-wave/sub-terahertz bands, and specifically captures rotor-induced micro-Doppler signatures of stationary or moving UAVs at different pitch angles and translational velocities. The framework synchronously produces channel tensors, coverage maps, transmission videos, multi-view images, and weather-conditioned renderings, and supports differentiable material calibration from sparse channel anchors. The released dataset contains 10 material-aware indoor/outdoor scenes and 50,000 synchronized UAV samples; the framework supports scalable generation of additional data for user-defined scenes and system configurations. In 3D UAV localization, DeepTelecom reduces the 90th-percentile error from 31.05 m to 2.43 m compared with schemes considering only channel state information, demonstrating its significance for artificial-intelligence-enabled wireless systems.
ER -