面向RIS辅助超5G网络的3GPP合规基准数据集

中文摘要 AI 辅助

可重构智能表面（RIS）正成为超第五代（B5G）网络中可编程无线环境的关键技术。然而，数据驱动的RIS研究仍受限于缺乏标准化、高保真且开源的数据集。本文介绍了一个大规模、符合3GPP TR 38.901标准的RIS辅助毫米波（mmWave）网络数据集，该数据集考虑了严重的路径损耗、阻塞敏感性和空间信道稀疏性，这些因素使得RIS辅助更具影响力。该数据集涵盖20个受控变体下的各种典型3GPP部署场景，捕获了不同的用户密度、衰落条件和阻塞机制。独特的是，每个样本都包含通过全局最优暴力搜索码本获得的oracle RIS相位配置，提供了任何现有公共数据集所缺乏的金标准监督标签。丰富的多任务标注包括完整信道状态信息（CSI）、每链路信道分解、最优相位矩阵和信道质量指标（CQI）标签，支持广泛的机器学习范式和下游任务，包括相位优化、信道估计和干扰管理。作为主要基准任务，我们引入了一种新颖的CSI到CQI映射，将RIS辅助链路质量预测构建为可扩展的标量分类问题，从而避免了直接相位向量预测的指数级输出复杂度。我们已在分布内、分布外和真实硬件测量条件下，将该映射与最先进的架构进行了评估。我们的数据集为加速RIS辅助B5G网络中数据驱动的研究提供了一个可复现、可扩展且社区就绪的基础。

英文摘要

Reconfigurable Intelligent Surfaces (RIS) are emerging as a key technology for programmable wireless environments in the beyond the fifth generation (B5G) networks. However, data-driven RIS research remains bottleneck by the lack of standardized, high-fidelity and open-source datasets. In this paper, we introduce a large-scale 3GPP TR 38.901-compliant dataset for RIS-aided millimeter wave (mmWave) networks, that considers severe path loss, blockage sensitivity, and spatial channel sparsity make the RIS assistance more impactful. The dataset spans various canonical 3GPP deployment scenarios across 20 controlled variants, capturing diverse user densities, fading conditions, and blockage regimes. Uniquely, every sample includes oracle RIS phase configurations obtained via a globally optimal brute-force codebook search, providing gold-standard supervision labels that are absent from any existing public dataset. Rich multi-task annotations comprising full channel state information (CSI), per-link channel decomposition, optimal phase matrices, and channel quality index (CQI) labels support a broad range of machine learning paradigms and downstream tasks, including phase optimization, channel estimation, and interference management. As the primary benchmark task, we introduce a novel CSI-to-CQI mapping that frames RIS-aided link-quality prediction as a scalable scalar classification problem, thereby avoiding the exponential output complexity of the direct phase vector prediction. We have evaluated this mapping against state-of-the-art architectures under in-distribution, out-of-distribution, and real-world hardware measurement conditions. Our dataset provides a reproducible, extensible, and community-ready foundation to accelerate data-driven research in RIS-aided B5G networks.

发表机构

• Indian Institute of Technology（印度理工学院）

机构由 AI 辅助整理，请以论文原文为准。