构建了用于6G MIMO通信的城市级信道地图数据集，支持高精度环境感知。
UrbanMIMOMap: A Ray-Traced MIMO CSI Dataset with Precoding-Aware Maps and Benchmarks
- 用高精度射线追踪生成城市场景下的完整复数信道状态信息
- 覆盖密集空间网格，包含复杂相位与幅度信息，超越传统路径损耗数据
- 适用于6G智能通信、感知融合及深度学习的信道建模，适合研究者直接使用
第六代移动通信（6G）系统需要基于环境感知的智能通信，依赖原生人工智能与传感通信一体化（ISAC）。无线地图（RMs）提供空间连续的信道信息，是关键技术。然而，通过电磁仿真生成高质量的真值地图计算成本极高，促使采用机器学习方法构建地图。这些数据驱动方法的有效性依赖大规模高质量训练数据。当前公开数据集多集中于单输入单输出（SISO）和有限信息（如路径损耗），难以满足需详细信道状态信息（CSI）的先进多输入多输出（MIMO）系统需求。为此，本文提出UrbanMIMOMap——一个基于高精度射线追踪生成的大规模城市MIMO CSI数据集。该数据集提供密集空间网格上的完整复数CSI矩阵，远超传统路径损耗数据。丰富的CSI对构建高保真无线地图至关重要，是数据驱动地图生成（包括深度学习）的基础资源。我们通过代表性机器学习方法在该数据集上的基准性能评估，验证了其应用价值。本工作为高精度无线地图生成、MIMO空间性能分析及6G环境感知中的机器学习研究提供了关键数据与参考。代码与数据见：https://github.com/UNIC-Lab/UrbanMIMOMap。
原文摘要 · Abstract (English)
Sixth generation (6G) systems require environment-aware communication, driven by native artificial intelligence (AI) and integrated sensing and communication (ISAC). Radio maps (RMs), providing spatially continuous channel information, are key enablers. However, generating high-fidelity RM ground truth via electromagnetic (EM) simulations is computationally intensive, motivating machine learning (ML)-based RM construction. The effectiveness of these data-driven methods depends on large-scale, high-quality training data. Current public datasets often focus on single-input single-output (SISO) and limited information, such as path loss, which is insufficient for advanced multi-input multi-output (MIMO) systems requiring detailed channel state information (CSI). To address this gap, this paper presents UrbanMIMOMap, a novel large-scale urban MIMO CSI dataset generated using high-precision ray tracing. UrbanMIMOMap offers comprehensive complex CSI matrices across a dense spatial grid, going beyond traditional path loss data. This rich CSI is vital for constructing high-fidelity RMs and serves as a fundamental resource for data-driven RM generation, including deep learning. We demonstrate the dataset's utility through baseline performance evaluations of representative ML methods for RM construction. This work provides a crucial dataset and reference for research in high-precision RM generation, MIMO spatial performance, and ML for 6G environment awareness. The code and data for this work are available at: https://github.com/UNIC-Lab/UrbanMIMOMap.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。