高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

超大规模MIMO阵列可视区域空间分布数据集

高锐锋 苗艳春 陈颖 王珏 张军 韩瑜 金石

高锐锋, 苗艳春, 陈颖, 王珏, 张军, 韩瑜, 金石. 超大规模MIMO阵列可视区域空间分布数据集[J]. 电子与信息学报. doi: 10.11999/JEIT231273
引用本文: 高锐锋, 苗艳春, 陈颖, 王珏, 张军, 韩瑜, 金石. 超大规模MIMO阵列可视区域空间分布数据集[J]. 电子与信息学报. doi: 10.11999/JEIT231273
GAO Ruifeng, MIAO Yanchun, CHEN Ying, WANG Jue, ZHANG Jun, HAN Yu, JIN Shi. Visibility Region Spatial Distribution Dataset for XL-MIMO Arrays[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231273
Citation: GAO Ruifeng, MIAO Yanchun, CHEN Ying, WANG Jue, ZHANG Jun, HAN Yu, JIN Shi. Visibility Region Spatial Distribution Dataset for XL-MIMO Arrays[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231273

超大规模MIMO阵列可视区域空间分布数据集

doi: 10.11999/JEIT231273
基金项目: 国家自然科学基金 (62001254, 62171240);江苏省高校自然科学基金 (22KJB510039)
详细信息
    作者简介:

    高锐锋:男,副教授,研究方向为无线通信、智能信息处理等

    苗艳春:女,硕士生,研究方向为智能通信

    陈颖:女,硕士生,研究方向为智能通信

    王珏:男,副教授,研究方向为MIMO 系统及超大规模 MIMO 系统等

    张军:男,教授,研究方向为大规模MIMO、人工智能通信等

    韩瑜:女,副研究员,研究方向为超大规模MIMO、可见区域等

    金石:男,教授,研究方向为6G移动通信理论与关键技术研究等

    通讯作者:

    王珏 wangjue@ntu.edu.cn

  • 中图分类号: TN929.5

Visibility Region Spatial Distribution Dataset for XL-MIMO Arrays

Funds: The National Natural Science Foundation of China (62001254, 61771264), The Natural Science Foundation of Jiangsu Provincial Universities (22KJB510039)
  • 摘要: 可视区域(VR)信息可用于降低超大规模多输入多输出(XL-MIMO)系统传输设计复杂度,但现有理论分析与传输设计多基于简化的VR统计分布模型。为评估分析XL-MIMO在实际物理传播场景中的性能,该文公开了XL-MIMO阵列VR空间分布数据集,其由环境参数设置、射线追踪仿真、天线场强数据预处理和VR判定准则等步骤构建。该数据集针对典型城区无线传播场景,建立了用户位置采样与场强数据、VR数据之间的关联,总数据条目数量达上亿级。进一步对其中VR形态、VR分布进行了可视化展示与分析,并以基于VR的XL-MIMO用户接入协议为例,利用该数据集对其在真实传播场景中的性能进行了仿真,为该数据集的应用提供了典型样例。
  • 图  1  VRD构建整体流程图

    图  2  实际3维城区场景以及采用射线追踪技术仿真得到的部分天线场强数据图

    图  3  所选用户位置示例

    图  4  两站点天线场强图

    图  5  site2不同VR判定准则下的VR示意图

    图  6  site2能量集中度80%时两种准则下的VR天线/子阵列数量分布图

    图  7  site2不同VR判定准则下的VR分布图

    图  8  两种随机接入协议的接入性能在所公布VRD下的仿真比较

    表  1  仿真参数设置

    参数参数设置
    站点位置场景内最高建筑物表面,site1:150 m, site2:50 m
    天线类型及详细参数每个站点200根天线(10×20),天线间距3 m,全向天线,频率4800 MHz,发送功率1 W
    天线序号site1:011-210,site2:20011-20210
    场强空间分辨率1 m
    用户高度高度统一为1.5 m
    传播模型智能射线追踪
    下载: 导出CSV

    1  天线能量集中度VR判定算法

     输入:用户位置j天线场强数据$ {d_j}\left[ i \right] $,其中i表示天线标号;能量集中度P;初始化VR集合Sj为空;天线总数Nt
     输出:用户j的VR集合Sj
     1 计算用户j所在位置的场强和:${F^j} = \sum\nolimits_{i = 1}^{{N_t}} {{d_j}\left[ i \right]} $;
     2 用户j所能接收到的天线阵列上P(%)的能量,即阈值:${F^{j,P}} = {F^j} \times P$;
     3 对数组$ {d_j}\left[ i \right] $按场强降序排序,生成新的2维数组${d'_j}\left[ {i,k} \right]$,其中i表示重新排序后的索引,k表示排序前天线的标号;
     4 for (int t=0; t<=Nt ;t++)
     5  ${sum} = {\text{sum}} + {d'_j}\left[ {i,k} \right]$;
     6  天线k加入集合Sj
     7  if${sum} > {F^{j,P}}$
     8   Break;
     9 返回VR Sj
    下载: 导出CSV

    表  2  数据集汇总表

    数据集类型数据集名称数据集含义数据量
    天线场强空间分布数据集Antenna_site1site1天线场强信息60071000
    Antenna_site2site2天线场强信息60071000
    VRD基于天线能量集中度的VRDS1_Antenna_user_80site1下能量集中度80%的用户位置-天线VR构成信息7702400
    S2_Antenna_user_80site2下能量集中度80%的用户位置-天线VR构成信息3515400
    S1_Antenna_VRsite1峰值下天线VR分布464
    S2_Antenna_VRsite2峰值下天线VR分布279
    基于子阵列能量集中度的VRDAntenna_subarray天线-子阵列映射数据集400
    S1_user_sub_power_80site1下能量集中度80%的用户位置-子阵列VR构成信息547280
    S2_user_sub_power_80site2下能量集中度80%的用户位置-子阵列VR构成信息725660
    S1_Subarray_VRsite1峰值下子阵列VR分布27353
    S2_Subarray_VRsite2峰值下子阵列VR分布36284
    下载: 导出CSV
  • [1] LARSSON E G, EDFORS O, TUFVESSON F, et al. Massive MIMO for next generation wireless systems[J]. IEEE Communications Magazine, 2014, 52(2): 186–195. doi: 10.1109/MCOM.2014.6736761.
    [2] WANG Zhe, ZHANG Jiayi, DU Hongyang, et al. Extremely large-scale MIMO: Fundamentals, challenges, solutions, and future directions[J]. IEEE Wireless Communications. doi: 10.1109/MWC.132.2200443. (查阅网上资料,未找到卷期页码信息,请确认) .
    [3] 张军, 陆佳程, 刘同顺, 等. 超大规模MIMO系统中基于交叠可视区域的功率分配方法[J]. 电子与信息学报, 2023, 45(12): 4262–4270. doi: 10.11999/JEIT221468.

    ZHANG Jun, LU Jiacheng, LIU Tongshun, et al. Power allocation method based on overlapping visibility region in extra large scale MIMO system[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4262–4270. doi: 10.11999/JEIT221468.
    [4] CUI Mingyao and DAI Linglong. Channel estimation for extremely large-scale MIMO: Far-field or near-field?[J]. IEEE Transactions on Communications, 2022, 70(4): 2663–2677. doi: 10.1109/TCOMM.2022.3146400.
    [5] IIMORI H, TAKAHASHI T, ISHIBASHI K, et al. Joint activity and channel estimation for extra-large MIMO systems[J]. IEEE Transactions on Wireless Communications, 2022, 21(9): 7253–7270. doi: 10.1109/TWC.2022.3157271.
    [6] 丁瑞, 钱晓涵, 刘道华, 等. 超大规模MIMO信道测量建模研究综述[J]. 电讯技术, 2022, 62(7): 1014–1022. doi: 10.3969/j.issn.1001-893x.2022.07.028.

    DING Rui, QIAN Xiaohan, LIU Daohua, et al. Survey of channel measurement and modeling for extra-large scale massive MIMO[J]. Telecommunication Engineering, 2022, 62(7): 1014–1022. doi: 10.3969/j.issn.1001-893x.2022.07.028.
    [7] DE CARVALHO E, ALI A, AMIRI A, et al. Non-stationarities in extra-large-scale massive MIMO[J]. IEEE Wireless Communications, 2020, 27(4): 74–80. doi: 10.1109/MWC.001.1900157.
    [8] ZHANG Jingjing, ZHANG Jun, HAN Yu, et al. Average spectral efficiency for TDD-based non-stationary XL-MIMO with VR estimation[C]. Proceedings of the 14th International Conference on Wireless Communications and Signal Processing, Nanjing, China, 2022: 973–977. doi: 10.1109/WCSP55476.2022.10039284.
    [9] ALI A, DE CARVALHO E, and HEATH R W. Linear receivers in non-stationary massive MIMO channels with visibility regions[J]. IEEE Wireless Communications Letters, 2019, 8(3): 885–888. doi: 10.1109/LWC.2019.2898572.
    [10] MARINELLO FILHO J C, BRANTE G, SOUZA R D, et al. Exploring the non-overlapping visibility regions in XL-MIMO random access and scheduling[J]. IEEE Transactions on Wireless Communications, 2022, 21(8): 6597–6610. doi: 10.1109/TWC.2022.3151329.
    [11] RODRIGUES V C, AMIRI A, ABRÃO T, et al. Low-complexity distributed XL-MIMO for multiuser detection[C]. Proceedings of 2020 IEEE International Conference on Communications Workshops, Dublin, Ireland, 2020: 1–6. doi: 10.1109/ICCWorkshops49005.2020.9145378.
    [12] TIAN Jiachen, HAN Yu, JIN Shi, et al. Low-overhead localization and VR identification for subarray-based ELAA systems[J]. IEEE Wireless Communications Letters, 2023, 12(5): 784–788. doi: 10.1109/LWC.2023.3244000.
    [13] 杨小龙, 佘媛, 周牧, 等. 基于CSI的三维联合参数估计算法[J]. 电子与信息学报, 2022, 44(2): 627–636. doi: 10.11999/JEIT200698.

    YANG Xiaolong, SHE Yuan, ZHOU Mu, et al. 3D parameter estimation method based on CSI[J]. Journal of Electronics & Information Technology, 2022, 44(2): 627–636. doi: 10.11999/JEIT200698.
    [14] ALKHATEEB A. DeepMIMO: A generic deep learning dataset for millimeter wave and massive MIMO applications[EB/OL].https://doi.org/10.48550/arXiv.1902.06435, 2023.
    [15] ARNOLD M, HOYDIS J, and TEN BRINK S. Novel massive MIMO channel sounding data applied to deep learning-based indoor positioning[C]. Proceedings of the 12th International ITG Conference on Systems, Communications and Coding, Rostock, Germany, 2019: 1–6. doi: 10.30420/454862021.
    [16] DU Xu and SABHARWAL A. Massive MIMO channels with inter-user angle correlation: Open-access dataset, analysis and measurement-based validation[J]. IEEE Transactions on Vehicular Technology, 2022, 71(2): 1602–1616. doi: 10.1109/TVT.2021.3131606.
    [17] HAN Yu, TANG Wankai, JIN Shi, et al. Large intelligent surface-assisted wireless communication exploiting statistical CSI[J]. IEEE Transactions on Vehicular Technology, 2019, 68(8): 8238–8242. doi: 10.1109/TVT.2019.2923997.
    [18] TRINDADE I, MÜLLER F, and KLAUTAU A. Accuracy analysis of the geometrical approximation of MIMO channels using ray-tracing[C]. Proceedings of 2020 IEEE Latin-American Conference on Communications, Santo Domingo, Dominican Republic, 2020: 1–5. doi: 10.1109/LATINCOM50620.2020.9282262.
    [19] LIN Zhijian, DU Xiaojiang, CHEN H H, et al. Millimeter-wave propagation modeling and measurements for 5G mobile networks[J]. IEEE Wireless Communications, 2019, 26(1): 72–77. doi: 10.1109/MWC.2019.1800035.
    [20] SHIKHANTSOV S, GUEVARA A, THIELENS A, et al. Spatial correlation in indoor massive MIMO: Measurements and ray tracing[J]. IEEE Antennas and Wireless Propagation Letters, 2021, 20(6): 903–907. doi: 10.1109/LAWP.2021.3066607.
    [21] HARON A S, MANSOR Z, AHMAD I, et al. The performance of 2.4GHz and 5GHz Wi-Fi router placement for signal strength optimization using Altair WinProp[C]. Proceedings of the IEEE 7th International Conference on Smart Instrumentation, Measurement and Applications, Bandung, Indonesia, 2021: 25–29. doi: 10.1109/ICSIMA50015.2021.9526299.
    [22] DE SOUZA J H I, AMIRI A, ABRÃO T, et al. Quasi-distributed antenna selection for spectral efficiency maximization in subarray switching XL-MIMO systems[J]. IEEE Transactions on Vehicular Technology, 2021, 70(7): 6713–6725. doi: 10.1109/TVT.2021.3081462.
    [23] LIU Daohua, WANG Jue, LI Ye, et al. Location-based visible region recognition in extra-large massive MIMO systems[J]. IEEE Transactions on Vehicular Technology, 2023, 72(6): 8186–8191. doi: 10.1109/TVT.2023.3242615.
    [24] GAO Xiang, TUFVESSON F, and EDFORS O. Massive MIMO channels—Measurements and models[C]. Proceedings of 2013 Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 2013: 280–284. doi: 10.1109/ACSSC.2013.6810277.
  • 加载中
图(8) / 表(3)
计量
  • 文章访问数:  96
  • HTML全文浏览量:  43
  • PDF下载量:  11
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-11-17
  • 修回日期:  2024-04-30
  • 网络出版日期:  2024-05-15

目录

    /

    返回文章
    返回