DOI: 10.57760/sciencedb.23350
title: A Measured Dataset for ISAC based on 5G Air Interface
title: 基于5G空口的通感一体化实测数据集

description: Solemnly declare: when using this data set to publish papers, books and other works, you must formally quote the papers to which this data set belongs:Citation: DING Shengli, CHEN Baolong, JIANG Dajie. A Measured Dataset for ISAC Based on 5G Air Interface[J]. Journal of Electronics & Information Technology, 2025, 47(4): 909-920. doi: 10.11999/JEIT241142Authors: Ding Shengli, Chen Baolong, Jiang DajieAuthor Unit: Vivo Software Technology Co., Ltd.Correspondent: Ding Shengli victording@vivo.comOriginal link: 基于5G空口的通感一体化实测数据集Funds: The National Science and Technology Major Project (2024ZD1300500)Integrated sensing and communication (ISAC) is one of the six scenarios of 6G confirmed by International Telecommunication Union (ITU). Oriented to the technique implementation and standardization establishment of ISAC, a measured ISAC sensing signal dataset is constructed based on 5G air interface. This dataset uses a universal software radio peripheral to run the 5G New Radio (NR) physical layer protocol stack, in which the downlink demodulated reference signal is reused as the sensing signal to execute the data acquisition, including 2 scenarios and 2 sensing modes, totaling 8 groups of data. In each scenario and each sensing mode, channel information of 8 channels in continuous 30 seconds is provided containing both moving sensing target and background environment, and data corresponding to only background environment is also provided. For clearer demonstration of the dataset, this paper shows the delay spectrums and delay-Doppler spectrums of typical sensing signal through the classical 2-dimensional discrete Fourier transformation (2D-DFT) algorithm with corresponding analyses and descriptions. Furthermore, this paper provides a reference path method in the delay domain based on the oversampling inverse discrete Fourier transformation (IDFT) algorithm to suppress the sensing idealities in bi-static sensing, to verify the reliability and effectiveness of the dataset.
description: Solemnly declare: when using this data set to publish papers, books and other works, you must formally quote the papers to which this data set belongs:Citation: DING Shengli, CHEN Baolong, JIANG Dajie. A Measured Dataset for ISAC Based on 5G Air Interface[J]. Journal of Electronics & Information Technology, 2025, 47(4): 909-920. doi: 10.11999/JEIT241142Authors: Ding Shengli, Chen Baolong, Jiang DajieAuthor Unit: Vivo Software Technology Co., Ltd.Correspondent: Ding Shengli victording@vivo.comOriginal link: 基于5G空口的通感一体化实测数据集Funds: The National Science and Technology Major Project (2024ZD1300500)Integrated sensing and communication (ISAC) is one of the six scenarios of 6G confirmed by International Telecommunication Union (ITU). Oriented to the technique implementation and standardization establishment of ISAC, a measured ISAC sensing signal dataset is constructed based on 5G air interface. This dataset uses a universal software radio peripheral to run the 5G New Radio (NR) physical layer protocol stack, in which the downlink demodulated reference signal is reused as the sensing signal to execute the data acquisition, including 2 scenarios and 2 sensing modes, totaling 8 groups of data. In each scenario and each sensing mode, channel information of 8 channels in continuous 30 seconds is provided containing both moving sensing target and background environment, and data corresponding to only background environment is also provided. For clearer demonstration of the dataset, this paper shows the delay spectrums and delay-Doppler spectrums of typical sensing signal through the classical 2-dimensional discrete Fourier transformation (2D-DFT) algorithm with corresponding analyses and descriptions. Furthermore, this paper provides a reference path method in the delay domain based on the oversampling inverse discrete Fourier transformation (IDFT) algorithm to suppress the sensing idealities in bi-static sensing, to verify the reliability and effectiveness of the dataset.