基于5G空口的通感一體化實(shí)測數(shù)據(jù)集

丁圣利; 陳保龍; 姜大潔

doi:10.11999/JEIT241142

基于5G空口的通感一體化實(shí)測數(shù)據(jù)集

doi: 10.11999/JEIT241142 cstr: 32379.14.JEIT241142

維沃軟件技術(shù)有限公司北京 100015

基金項(xiàng)目: 國家科技重大專項(xiàng)(2024ZD1300500)

詳細(xì)信息

作者簡介:
丁圣利：男，高級工程師，研究方向?yàn)橥ǜ幸惑w化、非地面網(wǎng)絡(luò)等

陳保龍：男，高級工程師，研究方向?yàn)橐苿油ㄐ盼锢韺釉O(shè)計(jì)

姜大潔：男，高級工程師，研究方向?yàn)橥ǜ幸惑w化、智能超表面、反向散射通信等

通訊作者:
丁圣利　victording@vivo.com

中圖分類號: TN926.1
計(jì)量
- 文章訪問數(shù): 447
- HTML全文瀏覽量: 157
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-12-27
- 修回日期: 2025-03-31
- 網(wǎng)絡(luò)出版日期: 2025-04-07
- 刊出日期: 2025-04-01

A Measured Dataset for ISAC Based on 5G Air Interface

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

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

摘要

摘要: 通感一體化是國際電信聯(lián)盟定義的6G的六大場景之一。為了推動通感一體化的技術(shù)落地和標(biāo)準(zhǔn)制定，該文公開了一個實(shí)測的、基于5G空口的通感一體化感知信號數(shù)據(jù)集。該數(shù)據(jù)集使用通用軟件無線電外設(shè)工作于sub-6 GHz頻段，運(yùn)行5G NR (New Radio)物理層協(xié)議棧，復(fù)用NR的下行解調(diào)參考信號作為感知信號進(jìn)行數(shù)據(jù)采集，包含了2個場景和2種感知模式共8組數(shù)據(jù)。在每個場景和每種感知模式下，提供了包含運(yùn)動感知目標(biāo)和背景環(huán)境的連續(xù)30 s的8通道信道信息數(shù)據(jù)，并提供了僅包含背景環(huán)境的數(shù)據(jù)。為了清晰地展示數(shù)據(jù)特征，該文通過經(jīng)典的2維離散傅里葉變換(2D-DFT)算法給出了典型感知信號的時延譜和時延-多普勒譜，并對其進(jìn)行了分析和描述。此外，該文提供了基于過采樣離散傅里葉逆變換(IDFT)算法的時延域參考徑方法，用來進(jìn)行雙基地感知模式下的感知非理想因素消除，以驗(yàn)證數(shù)據(jù)集的可靠性和有效性。
- 通感一體化 /
- 5G空口 /
- 雙基地感知 /
- 感知非理想因素
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

HTML全文

圖 1 6種感知模式示意圖

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圖 2 單基地感知數(shù)據(jù)采集系統(tǒng)

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圖 3 雙基地感知數(shù)據(jù)采集系統(tǒng)

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圖 4 測試場景示意圖

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圖 5 一次往返運(yùn)動金屬導(dǎo)體球的速度和位置

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圖 6 感知信號配置

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圖 7 單基地感知數(shù)據(jù)特征

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圖 8 雙基地感知數(shù)據(jù)特征

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圖 9 感知非理想因素消除

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圖 10 感知目標(biāo)運(yùn)動軌跡

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表 1 文件夾標(biāo)簽與數(shù)據(jù)含義

文件夾標(biāo)簽	數(shù)據(jù)含義	數(shù)據(jù)文件數(shù)量
sc1_mono_bg	場景1、單基地感知、僅背景雜波	10
sc1_mono_st	場景1、單基地感知、有感知目標(biāo)	150
sc1_bi_bg	場景1、雙基地感知、僅背景雜波	10
sc1_bi_st	場景1、雙基地感知、有感知目標(biāo)	150
sc2_mono_bg	場景2、單基地感知、僅背景雜波	10
sc2_mono_st	場景2、單基地感知、有感知目標(biāo)	150
sc2_bi_bg	場景2、雙基地感知、僅背景雜波	10
sc2_bi_st	場景2、雙基地感知、有感知目標(biāo)	150

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