中文标题#
DeepTelecom：用于信道和 MIMO 应用的数字孪生深度学习数据集
英文标题#
DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications
中文摘要#
领域特定的数据集是释放人工智能 (AI) 驱动的无线创新的基础。 然而现有的无线 AI 语料库生成速度缓慢，提供的建模保真度有限，并且仅涵盖狭窄的场景类型。 为了解决这些挑战，我们创建了 DeepTelecom，这是一个三维 (3D) 数字孪生信道数据集。 具体而言，一个大型语言模型 (LLM) 辅助的流程首先构建具有可分割材料参数化表面的第三级细节 (LoD3) 户外和室内场景。 然后，DeepTelecom 基于 Sionna 的光线追踪引擎模拟完整的无线电波传播效果。 利用 GPU 加速，DeepTelecom 流式传输光线路径轨迹和实时信号强度热图，将其编译成高帧率视频，并同时输出同步的多视角图像、信道张量和多尺度衰落痕迹。 通过高效地流式传输大规模、高保真和多模态的信道数据，DeepTelecom 不仅为无线 AI 研究提供了一个统一的基准，还提供了丰富的领域训练基础，使基础模型能够紧密融合大模型智能与未来通信系统。
英文摘要#
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.
文章页面#
DeepTelecom：用于信道和 MIMO 应用的数字孪生深度学习数据集
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