中文标题#
DeepTelecom：用於信道和 MIMO 應用的數位雙胞胎深度學習數據集
英文标题#
DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications
中文摘要#
領域特定的數據集是釋放人工智慧 (AI) 驅動的無線創新的基礎。 然而現有的無線 AI 語料庫生成速度緩慢，提供的建模保真度有限，並且僅涵蓋狹窄的場景類型。 為了解決這些挑戰，我們創建了 DeepTelecom，這是一個三維 (3D) 數位雙胞胎信道數據集。 具體而言，一個大型語言模型 (LLM) 輔助的流程首先構建具有可分割材料參數化表面的第三級細節 (LoD3) 戶外和室內場景。 然後，DeepTelecom 基於 Sionna 的光線追蹤引擎模擬完整的無線電波傳播效果。 利用 GPU 加速，DeepTelecom 串流光線路徑軌跡和實時信號強度熱圖，將其編譯成高幀率視頻，並同時輸出同步的多視角圖像、信道張量和多尺度衰落痕跡。 通過高效地串流大規模、高保真和多模態的信道數據，DeepTelecom 不僅為無線 AI 研究提供了一個統一的基準，還提供了豐富的領域訓練基礎，使基礎模型能夠緊密融合大模型智能與未來通信系統。
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：用於信道和 MIMO 應用的數位雙胞胎深度學習數據集
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