Location-based services (LBSs) provide necessary infrastructure for daily life, from bicycle sharing to nursing care. In contrast to traditional positioning methods such as Wi-Fi, Bluetooth, and ultra-wideband (UWB), fifth-generation (5G) networking is defined as a paradigm of integrated sensing and communication (ISAC). With its advantages of wide-range coverage and indoor-outdoor integration, 5G is promising for high-precision positioning in indoor and urban canyon environments. However, 5G location studies face great obstacles due to the lack of commercialized 5G ISAC base stations that support positioning functions as well as publicly available datasets. In this paper, we first propose a dataset generation method, the Multilevel Feature Synthesis Method (Multilevel-FSM), to obtain positioning features. In particular, the features of a multiple-input multiple-output (MIMO) channel are flattened into a single image to increase the information density and improve feature expression, and data augmentation is performed to provide stronger robustness to noise. Subsequently, we devise a specially designed deep learning positioning method, Multipath Res-Inception (MPRI), trained on the proposed dataset to enhance positioning accuracy. Finally, the results of extensive experiments conducted in two typical 5G scenarios (indoors and urban canyon) show that Multilevel-FSM and MPRI outperform state-of-the-art works in accuracy, time overhead and robustness to noise.

中文翻译：

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通过信道频率响应实现 5G NR 高精度室内定位：一种新的范式和数据集生成方法

基于位置的服务 (LBS) 为日常生活提供必要的基础设施，从自行车共享到护理。与 Wi-Fi、蓝牙和超宽带 (UWB) 等传统定位方法相比，第五代 (5G) 网络被定义为集成传感和通信的范式(ISAC)。5G凭借其覆盖范围广、室内外一体化的优势，在室内和城市峡谷环境中的高精度定位方面大有可为。然而，由于缺乏支持定位功能的商业化 5G ISAC 基站以及公开可用的数据集，5G 定位研究面临巨大障碍。在本文中，我们首先提出了一种数据集生成方法，即多级特征合成方法（Multilevel-FSM），以获取定位特征。特别是，将多输入多输出（MIMO）通道的特征扁平化为单个图像以增加信息密度并改善特征表达，并执行数据增强以提供更强的噪声鲁棒性。随后，我们设计了一种专门设计的深度学习定位方法，Multipath Res-Inception (MPRI)，在提议的数据集上进行训练以提高定位精度。最后，在两个典型的 5G 场景（室内和城市峡谷）中进行的广泛实验的结果表明，Multilevel-FSM 和 MPRI 在准确性、时间开销和对噪声的鲁棒性方面优于最先进的工作。