A Measured Dataset for ISAC Based on 5G Air Interface
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摘要: 通感一体化是国际电信联盟定义的6G的六大场景之一。为了推动通感一体化的技术落地和标准制定，该文公开了一个实测的、基于5G空口的通感一体化感知信号数据集。该数据集使用通用软件无线电外设工作于sub-6 GHz频段，运行5G NR (New Radio)物理层协议栈，复用NR的下行解调参考信号作为感知信号进行数据采集，包含了2个场景和2种感知模式共8组数据。在每个场景和每种感知模式下，提供了包含运动感知目标和背景环境的连续30 s的8通道信道信息数据，并提供了仅包含背景环境的数据。为了清晰地展示数据特征，该文通过经典的2维离散傅里叶变换(2D-DFT)算法给出了典型感知信号的时延谱和时延-多普勒谱，并对其进行了分析和描述。此外，该文提供了基于过采样离散傅里叶逆变换(IDFT)算法的时延域参考径方法，用来进行双基地感知模式下的感知非理想因素消除，以验证数据集的可靠性和有效性。Abstract:
Objective Integrated Sensing and Communication (ISAC) is one of the six scenarios of 6G confirmed by the International Telecommunication Union (ITU). In particular, by enabling separable sensing transceivers, bi-static sensing is free from self-interference and can leverage ubiquitous network devices, making it an essential scenario for ISAC. However, bi-static sensing faces challenges due to non-idealities, including Timing Offset (TO), Timing Drift (TD), and Carrier Frequency Offset (CFO), which significantly affect signal detection and parameter estimation. Therefore, the suppression of sensing non-idealities is a key research area, as it directly influences the reliability of sensing results. Many researchers use proprietary datasets to investigate and suppress these non-idealities, which complicates fair and unified evaluations of different methods and technologies. Moreover, such reliance on specific experimental conditions hinders the reproducibility of relative studies. To support the development and standardization of ISAC techniques, a measured ISAC sensing signal dataset based on the 5G air interface has been constructed. This dataset enables the parallel comparison of various studies and facilitates research implementation even in the absence of specific experimental conditions. Methods This dataset utilizes Universal Software Radio Peripherals (USRPs), to operate in the sub-6 GHz frequency band and to run the 5G New Radio (NR) physical layer protocol stack, with the DeModulated Reference Signal (DMRS) in Physical Downlink Shared CHannel (PDSCH) reused as the sensing signal for data acquisition. The physical layer protocol stack is developed based on the NR protocol Release 15. The dataset comprises 2 scenarios and 2 sensing modes, resulting in a total of 8 data groups. The two sensing modes are bi-static and mono-static sensing, allowing for independent research on either sensing mode as well as comparative studies between the two. For mono-static sensing, a single USRP serves as the Base Station (BS), transmitting and receiving the sensing signal. For bi-static sensing, two USRPs are used: one acts as the BS and the other acts as the User Equipment (UE), with the BS transmitting the sensing signal and the UE receiving it. For both sensing modes, the transmitter uses a signal panel antenna, while the receiver is equipped with an antenna array consisting of 8 antenna elements. These 8 antenna elements correspond to 8 radio channels in the receiver, facilitating 8-channel reception. For each scenario and sensing mode, Channel State Information (CSI) from the 8 channels is provided over a continuous 30-second period, capturing both the moving sensing target and the background environment. Additionally, data corresponding only to the background environment is also included in this dataset. In each scenario, the positions and orientations of the transmitting and receiving antennas, as well as the moving trajectory of the sensing target, remain unchanged for both sensing modes. This ensures that the ground truth remains identical for both mono-static and bi-static sensing, enabling comparative research between the two sensing modes. Results and Discussions To provide a clearer demonstration of the dataset, this paper presents the delay spectrums and delay-Doppler spectrums of typical sensing signals using the classical 2-Dimensional Discrete Fourier Transformation (2D-DFT) algorithm, with corresponding analyses and descriptions. The delay-Doppler spectrums of mono-static sensing are much clearer ( Fig. 7 ), with the sensing target easily detectable. However, the delay-Doppler spectrums of bi-static sensing exhibit significant dispersion (Fig. 8 ), which results from sensing non-idealities and hinders signal detection and parameter estimation. Therefore, suppressing sensing non-idealities is critical for improving bi-static sensing performance. As an example, this paper provides a reference path method in the delay domain, based on the oversampling Inverse Discrete Fourier Transformation (IDFT) algorithm, to mitigate sensing non-idealities in bi-static sensing and to validate the reliability and effectiveness of the dataset. The results demonstrate that the reference path method effectively suppresses the impact of sensing non-idealities (Fig. 9 ), yielding acceptable position measurements for the sensing target in bi-static sensing (Fig. 10 ). However, further research is needed to develop comprehensive solutions to address sensing non-idealities, which is the primary motivation for releasing this dataset.Conclusions Currently, there is a lack of an effective, standardized, and flexible dataset for sensing signals in ISAC based on air interfaces. Datasets derived from air interfaces in practical systems are critical foundations for research on bi-static sensing signal processing in 6G ISAC. To address this gap, this paper constructs and publicly releases an ISAC dataset based on the 5G air interface. The data is collected using USRPs running the 5G NR physical layer protocol stacks. Users can apply segmentation, decimation, or sliding-window extraction to the data to meet specific research needs. This dataset supports research on sensing non-idealities, signal detection, parameter estimation, clutter elimination, and sensing signal design. It facilitates independent research on mono-static and bi-static sensing, as well as comparative studies between the two sensing modes. Future efforts will focus on maintaining and expanding the dataset to include more complex scenarios, such as outdoor environments, low-altitude scenarios, and collaborative sensing. -
表 1 文件夹标签与数据含义
文件夹标签 数据含义 数据文件数量 sc1_mono_bg 场景1、单基地感知、仅背景杂波 10 sc1_mono_st 场景1、单基地感知、有感知目标 150 sc1_bi_bg 场景1、双基地感知、仅背景杂波 10 sc1_bi_st 场景1、双基地感知、有感知目标 150 sc2_mono_bg 场景2、单基地感知、仅背景杂波 10 sc2_mono_st 场景2、单基地感知、有感知目标 150 sc2_bi_bg 场景2、双基地感知、仅背景杂波 10 sc2_bi_st 场景2、双基地感知、有感知目标 150 
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