DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications

TL;DR

DeepTelecom introduces a high-fidelity, multimodal 3D digital-twin dataset for wireless AI, enabling advanced research and development in channel modeling, MIMO, and communication systems.

Contribution

It presents a novel pipeline combining LLM-assisted scene generation and GPU-accelerated ray tracing to produce comprehensive wireless channel data.

Findings

Provides a large-scale, high-fidelity dataset for wireless AI research

Enables training of foundation models for communication systems

Offers synchronized multimodal channel data streams

Abstract

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.