A Measured Dataset for ISAC Based on 5G Air Interface

DING Shengli; CHEN Baolong; JIANG Dajie

doi:10.11999/JEIT241142

Volume 47 Issue 4

Apr. 2025

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(4): 909-920

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/JEIT241142

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/JEIT241142

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/JEIT241142

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/JEIT241142

PDF( 4445 KB)

A Measured Dataset for ISAC Based on 5G Air Interface

doi: 10.11999/JEIT241142 cstr: 32379.14.JEIT241142

vivo Software Technology Co., Ltd., Beijing 100015, China

Funds: The National Science and Technology Major Project (2024ZD1300500)

Received Date: 2024-12-27
Rev Recd Date: 2025-03-31

Available Online: 2025-04-07

Publish Date: 2025-04-01

Abstract

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.
- Integrated Sensing and Communication (ISAC),
- 5G air interface,
- Bi-static sensing,
- Sensing non-idealities

FullText(HTML)

References(23)

References

[1]	LIU Fan, MASOUROS C, PETROPULU A P, et al. Joint radar and communication design: Applications, state-of-the-art, and the road ahead[J]. IEEE Transactions on Communications, 2020, 68(6): 3834–3862. doi: 10.1109/TCOMM.2020.2973976.
[2]	CUI Yuanhao, LIU Fan, JING Xiaojun, et al. Integrating sensing and communications for ubiquitous IoT: Applications, trends, and challenges[J]. IEEE Network, 2021, 35(5): 158–167. doi: 10.1109/MNET.010.2100152.
[3]	PAUL B, CHIRIYATH A R, and BLISS D W. Survey of RF communications and sensing convergence research[J]. IEEE Access, 2017, 5: 252–270. doi: 10.1109/ACCESS.2016.2639038.
[4]	DE LIMA C, BELOT D, BERKVENS R, et al. Convergent communication, sensing and localization in 6G systems: An overview of technologies, opportunities and challenges[J]. IEEE Access, 2021, 9: 26902–26925. doi: 10.1109/ACCESS.2021.3053486.
[5]	International Telecommunication Union. Recommendation ITU-R M. 2160 Framework and overall objectives of the future development of IMT for 2030 and beyond[S]. Geneva: International Telecommunication Union, 2023.
[6]	BARNETO C B, RIIHONEN T, TURUNEN M, et al. Full-duplex OFDM radar with LTE and 5G NR waveforms: Challenges, solutions, and measurements[J]. IEEE Transactions on Microwave Theory and Techniques, 2019, 67(10): 4042–4054. doi: 10.1109/TMTT.2019.2930510.
[7]	WEI Zhiqing, QU Hanyang, WANG Yuan, et al. Integrated sensing and communication signals toward 5G-A and 6G: A survey[J]. IEEE Internet of Things Journal, 2023, 10(13): 11068–11092. doi: 10.1109/JIOT.2023.3235618.
[8]	EVERETT E, SAHAI A, and SABHARWAL A. Passive self-interference suppression for full-duplex infrastructure nodes[J]. IEEE Transactions on Wireless Communications, 2014, 13(2): 680–694. doi: 10.1109/TWC.2013.010214.130226.
[9]	ZHANG J A, RAHMAN M L, WU Kai, et al. Enabling joint communication and radar sensing in mobile networks—A survey[J]. IEEE Communications Surveys & Tutorials, 2022, 24(1): 306–345. doi: 10.1109/COMST.2021.3122519.
[10]	3rd Generation Partnerships Project. Rel-19 Channel Modeling for ISAC and new spectrum (7~24 GHz)[R]. RP-231798, 2023.
[11]	ZHANG J A, LIU Fan, MASOUROS C, et al. An overview of signal processing techniques for joint communication and radar sensing[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(6): 1295–1315. doi: 10.1109/JSTSP.2021.3113120.
[12]	WU Yiling, ZHAO Yuping, and LI Dou. Sampling frequency offset estimation for pilot-aided OFDM systems in mobile environment[J]. Wireless Personal Communications, 2012, 62(1): 215–226. doi: 10.1007/s11277-010-0049-x.
[13]	WU Kai, PEGORARO J, MENEGHELLO F, et al. Sensing in bistatic ISAC systems with clock asynchronism: A signal processing perspective[J]. IEEE Signal Processing Magazine, 2024, 41(5): 31–43. doi: 10.1109/MSP.2024.3418725.
[14]	丁圣利, 李健之, 陈保龙, 等. 通感一体化中的感知非理想因素及其消除方法[J]. 移动通信, 2023, 47(9): 46–56. doi: 10.3969/j.issn.1006-1010.20230721-0001. DING Shengli, LI Jianzhi, CHEN Baolong, et al. Cancellation methods for sensing related non-ideal factors in integrated sensing and communication system[J]. Mobile Communications, 2023, 47(9): 46–56. doi: 10.3969/j.issn.1006-1010.20230721-0001.
[15]	LUO Jiajin, ZHOU Baojian, YU Yang, et al. Sensiverse: A dataset for ISAC study[J]. arXiv preprint arXiv: 2308.13789, 2023. doi: 10.48550/arXiv.2308.13789.
[16]	LI Xiang, ZHANG Daqing, LV Qin, et al. IndoTrack: Device-free indoor human tracking with commodity Wi-Fi[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2017, 1(3): 72. doi: 10.1145/3130940.
[17]	WANG Zhongqin, ZHANG J A, XU Min, et al. Single-target real-time passive WiFi tracking[J]. IEEE Transactions on Mobile Computing, 2023, 22(6): 3724–3742. doi: 10.1109/TMC.2022.3141115.
[18]	PEGORARO J, LACRUZ J O, AZZINO T, et al. JUMP: Joint communication and sensing with unsynchronized transceivers made practical[J]. IEEE Transactions on Wireless Communications, 2024, 23(8): 9759–9775. doi: 10.1109/TWC.2024.3365853.
[19]	DING Shengli, CHEN Baolong, LI Jianzhi, et al. Integrated sensing and communication: Prototype and key processing algorithms[C]. 2023 IEEE International Conference on Communications Workshops (ICC Workshops), Rome, Italy, 2023: 225–230. doi: 10.1109/ICCWorkshops57953.2023.10283732.
[20]	LIU Fan, CUI Yuanhao, MASOUROS C, et al. Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(6): 1728–1767. doi: 10.1109/JSAC.2022.3156632.
[21]	MENEGHELLO F, GARLISI D, DAL FABBRO N, et al. SHARP: Environment and person independent activity recognition with commodity IEEE 802.11 access points[J]. IEEE Transactions on Mobile Computing, 2023, 22(10): 6160–6175. doi: 10.1109/TMC.2022.3185681.
[22]	YU Xiaohan, CHEN Xiaolong, HUANG Yong, et al. Radar moving target detection in clutter background via adaptive dual-threshold sparse Fourier transform[J]. IEEE Access, 2019, 7: 58200–58211. doi: 10.1109/ACCESS.2019.2914232.
[23]	ESTER M, KRIEGEL H P, SANDER J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Portland, USA, 1996: 226–231.