Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.14507 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908496240312320 |
|---|---|
| author | Wang, Bohao Jiang, Zehua Yang, Zhenyu Huang, Chongwen Shen, Yongliang Jiang, Siming Zhu, Chen Yang, Zhaohui Jin, Richeng Zhang, Zhaoyang Muhaidat, Sami Debbah, Merouane |
| author_facet | Wang, Bohao Jiang, Zehua Yang, Zhenyu Huang, Chongwen Shen, Yongliang Jiang, Siming Zhu, Chen Yang, Zhaohui Jin, Richeng Zhang, Zhaoyang Muhaidat, Sami Debbah, Merouane |
| contents | Domain-specific datasets are the foundation for unleashing artificial intelligence (AI)-driven wireless innovation. Yet existing wireless AI corpora are slow to produce, offer limited modeling fidelity, and cover only narrow scenario types. To address the challenges, we create DeepTelecom, a three-dimension (3D) digital-twin channel dataset. Specifically, a large language model (LLM)-assisted pipeline first builds the third level of details (LoD3) outdoor and indoor scenes with segmentable material-parameterizable surfaces. Then, DeepTelecom simulates full radio-wave propagation effects based on Sionna's ray-tracing engine. Leveraging GPU acceleration, DeepTelecom streams ray-path trajectories and real-time signal-strength heat maps, compiles them into high-frame-rate videos, and simultaneously outputs synchronized multi-view images, channel tensors, and multi-scale fading traces. By efficiently streaming large-scale, high-fidelity, and multimodal channel data, DeepTelecom not only furnishes a unified benchmark for wireless AI research but also supplies the domain-rich training substrate that enables foundation models to tightly fuse large model intelligence with future communication systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14507 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications Wang, Bohao Jiang, Zehua Yang, Zhenyu Huang, Chongwen Shen, Yongliang Jiang, Siming Zhu, Chen Yang, Zhaohui Jin, Richeng Zhang, Zhaoyang Muhaidat, Sami Debbah, Merouane Information Theory Domain-specific datasets are the foundation for unleashing artificial intelligence (AI)-driven wireless innovation. Yet existing wireless AI corpora are slow to produce, offer limited modeling fidelity, and cover only narrow scenario types. To address the challenges, we create DeepTelecom, a three-dimension (3D) digital-twin channel dataset. Specifically, a large language model (LLM)-assisted pipeline first builds the third level of details (LoD3) outdoor and indoor scenes with segmentable material-parameterizable surfaces. Then, DeepTelecom simulates full radio-wave propagation effects based on Sionna's ray-tracing engine. Leveraging GPU acceleration, DeepTelecom streams ray-path trajectories and real-time signal-strength heat maps, compiles them into high-frame-rate videos, and simultaneously outputs synchronized multi-view images, channel tensors, and multi-scale fading traces. By efficiently streaming large-scale, high-fidelity, and multimodal channel data, DeepTelecom not only furnishes a unified benchmark for wireless AI research but also supplies the domain-rich training substrate that enables foundation models to tightly fuse large model intelligence with future communication systems. |
| title | DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications |
| topic | Information Theory |
| url | https://arxiv.org/abs/2508.14507 |