不依赖位置坐标的室内Wi-Fi网络覆盖度量方法

谢泽锋; 陈伟栋; 黄黎霞; 顾一帆; 张博钧; 全智

doi:10.11959/j.issn.2096-3750.2024.00397

您当前的位置：

首页 >

文章列表页 >

不依赖位置坐标的室内Wi-Fi网络覆盖度量方法

理论与技术 | 更新时间：2024-08-16

- 不依赖位置坐标的室内Wi-Fi网络覆盖度量方法
- Indoor Wi-Fi coverage measurement without spatial coordinates
- 物联网学报 2024年8卷第2期页码：71-80
- 作者机构：
  
  1.中国移动通信集团广东有限公司,广东广州 510623
  2.深圳大学射频异质异构集成全国重点实验室,广东深圳 518060
  3.深圳大学电子与信息工程学院,广东深圳 518060
  4.中国信息通信研究院南方分院(深圳信息通信研究院),广东深圳 518035
- 作者简介：
  
  [ "谢泽锋（1983‒ ），男，现就职于中国移动通信集团广东有限公司，主要研究方向为业务质量和客户感知分析优化。" ]
  [ "陈伟栋（1974‒ ），男，中国移动通信集团广东有限公司高级工程师，主要研究方向为业务质量和客户感知分析优化。" ]
  [ "黄黎霞（1999‒ ），女，深圳大学电子与信息工程学院硕士生，主要研究方向为无线通信系统、射频系统校准与测量、机器学习等。" ]
  [ "顾一帆（1990‒ ），男，博士，深圳大学电子与信息工程学院助理教授，主要研究方向为无线通信系统、射频系统校准与测量、信息年龄和图神经网络。" ]
  [ "张博钧（1981‒ ），男，中国信息通信研究院南方分院（深圳信息通信研究院）副院长，主要研究方向为无线通信设备检测技术研究与标准研制、国内外认证政策与技术。" ]
  [ "全智（1978‒ ），男，博士，深圳大学电子与信息工程学院特聘教授，主要研究方向为无线通信系统、射频系统校准与测量、数据驱动信号处理和机器学习。" ]
- 基金信息：
  
  国家自然科学基金项目(62201361)
- DOI：10.11959/j.issn.2096-3750.2024.00397
  中图分类号：
- 纸质出版日期：2024-06-10，
  
  收稿日期：2024-03-26，
  
  修回日期：2024-06-15，
- 稿件说明：
移动端阅览
谢泽锋,陈伟栋,黄黎霞等.不依赖位置坐标的室内Wi-Fi网络覆盖度量方法[J].物联网学报,2024,08(02):71-80.

XIE Zefeng,CHEN Weidong,HUANG Lixia,et al.Indoor Wi-Fi coverage measurement without spatial coordinates[J].Chinese Journal on Internet of Things,2024,08(02):71-80.
谢泽锋,陈伟栋,黄黎霞等.不依赖位置坐标的室内Wi-Fi网络覆盖度量方法[J].物联网学报,2024,08(02):71-80. DOI： 10.11959/j.issn.2096-3750.2024.00397.

XIE Zefeng,CHEN Weidong,HUANG Lixia,et al.Indoor Wi-Fi coverage measurement without spatial coordinates[J].Chinese Journal on Internet of Things,2024,08(02):71-80. DOI： 10.11959/j.issn.2096-3750.2024.00397.

摘要

3GPP在版本16（R16

Release 16）中升级了最小化路测（MDT

minimization of drive test）技术，提出移动终端可利用4G/5G网络自主上报Wi-Fi信号的接收信号强度指示（RSSI

received signal strength indicator），为运营商度量Wi-Fi网络的覆盖率带来了可能性。然而，现有基于MDT技术的网络覆盖度量方法严重依赖GPS提供的位置坐标，但全球定位系统（GPS

global positioning system）不能提供室内精准定位，无法用于室内Wi-Fi网络的覆盖度量。为此，提出了一种不依赖位置坐标的RSSI聚类方法，充分利用室内相近位置RSSI的统计相似性，区分不同位置的RSSI测量差异，在无位置坐标条件下准确估计出室内Wi-Fi网络的覆盖率。实验结果表明，所提方法估计的覆盖率与基于真实位置坐标测量的覆盖率相近，度量准确度明显优于现有的其他方法。

Abstract

In R16

3GPP proposed that user devices could utilize the 4G/5G cellular networks to report the received signal strength indicator (RSSI) of Wi-Fi signal based on the existing minimization of drive test (MDT) technology

making it possible to measure the coverage probability of indoor Wi-Fi networks. However

existing measurement methods of network coverage probability based on the MDT require the spatial coordinates provided by the GPS. As GPS has poor indoor localization accuracy

it is not able to be applied to indoor Wi-Fi networks. A measurement method for network coverage probability based on clustering without spatial coordinates was proposed. The proposed method could distinguish RSSI measurement reported on different locations

with the fact that their statistics were similar at similar locations. The coverage probability was accurately measured by utilizing the clustering results without knowing the spatial coordinates. Experimental results show that the coverage probability measured by the proposed method is very close to the probability measured by the known spatial coordinates

and the accuracy is much higher than existing methods.

关键词

网络覆盖率Wi-Fi网络最小化路测聚类算法接收信号强度指示

Keywords

coverage probabilityWi-Fi networkminimization of drive testclustering algorithmreceived signal strength indicator

references

OUGHTON E J, LEHR W, KATSAROS K, et al. Revisiting wireless internet connectivity: 5G vs Wi-Fi 6[J]. Telecommunications Policy, 2021, 45(5): 102127.

NAIK G, PARK J M, ASHDOWN J, et al. Next generation Wi-Fi and 5G NR-U in the 6 GHz bands: opportunities and challenges[J]. IEEE Access, 2020, 8: 153027-153056.

刘峻朋, 夏玮玮, 刘晗, 等. 面向电力业务质量保障的NR-U与Wi-Fi频谱共享[J]. 电信科学, 2023, 39(7): 11-22.

LIU J P, XIA W W, LIU H, et al. NR-U and Wi-Fi spectrum sharing for quality guaranteeing of power services[J]. Telecommunications Science, 2023, 39(7): 11-22.

黄宇, 李雨汝. Wi-Fi 6技术现状综述[J]. 中国无线电, 2021(5): 90-91.

HUANG Y, LI Y R. Overview of Wi-Fi 6 technology[J]. China Radio, 2021(5): 90-91.

SZOTT S, KOSEK-SZOTT K, GAWŁOWICZ P, et al. Wi-Fi meets ML: a survey on improving IEEE 802.11 performance with machine learning[J]. IEEE Communications Surveys & Tutorials, 2022, 24(3): 1843-1893.

ANDREWS J G, BACCELLI F, GANTI R K. A tractable approach to coverage and rate in cellular networks[J]. IEEE Transactions on Communications, 2011, 59(11): 3122-3134.

BAI T Y, HEATH R W. Coverage and rate analysis for millimeter-wave cellular networks[J]. IEEE Transactions on Wireless Communications, 2015, 14(2): 1100-1114.

GUO A J, HAENGGI M. Spatial stochastic models and metrics for the structure of base stations in cellular networks[J]. IEEE Transactions on Wireless Communications, 2013, 12(11): 5800-5812.

AKBARI I, ONIRETI O, IMRAN A, et al. Impact of inaccurate user and base station positioning on autonomous coverage estimation[C]//Proceedings of the 2015 IEEE 20th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD). Piscataway: IEEE Press, 2015: 114-118.

TAUFIQUE A, JABER M, IMRAN A, et al. Planning wireless cellular networks of future: outlook, challenges and opportunities[J]. IEEE Access, 2017, 5: 4821-4845.

MINOVSKI D, ÖGREN N, MITRA K, et al. Throughput prediction using machine learning in LTE and 5G networks[J]. IEEE Transactions on Mobile Computing, 2023, 22(3): 1825-1840.

3GPP. 3GPP TS 37.320-Universal terrestrial radio access (UTRA) and evolved universal terrestrial radio access (E-UTRA); radio measurement collection for minimization of drive tests (MDT); Overall description (Version 10.2.0)[S]. 2011.

QURESHI H N, IMRAN A, ABU-DAYYA A. Enhanced MDT-based performance estimation for AI driven optimization in future cellular networks[J]. IEEE Access, 1030, 8: 161406-161426.

潘饶元, 林宁, 刘华, 等. 一种4G/5G自适应过覆盖评估分析方法与工具[J]. 广西通信技术, 2022(2): 50-54.

PAN R Y, LIN N, LIU H, et al. An adaptive overcover analysis method and tool of 4G/5G network[J]. Guangxi Communication Technology, 2022(2): 50-54.

杜安静, 何家辉. 基于MDT技术的网络综合分析方法研究与应用[J]. 电信工程技术与标准化, 2020, 33(2): 65-68.

DU A J, HE J H. Research and application of network comprehensive analysis method based on MDT technology[J]. Telecom Engineering Technics and Standardization, 2020, 33(2): 65-68.

SHODAMOLA J, QURESHI H, MASOOD U, et al. Towards addressing the spatial sparsity of MDT reports to enable zero touch network automation[C]//Proceedings of the 2021 IEEE Global Communications Conference (GLOBECOM). Piscataway: IEEE Press, 2021: 1-6.

RIAZ M S, QURESHI H N, MASOOD U, et al. A hybrid deep learning-based (HYDRA) framework for multifault diagnosis using sparse MDT reports[J]. IEEE Access, 2022, 10: 67140-67151.

雷泽临, 苏俭, 郭伟. 基于机器学习的蜂窝网络故障管理框架及方法综述[J]. 计算机应用研究, 2022, 39(12): 3521-3533.

LEI Z L, SU J, GUO W. Survey of cellular network fault management framework and methods based on machine learning[J]. Application Research of Computers, 2022, 39(12): 3521-3533.

王颖. 一种基于MDT的基站工参诊断及实现方法[J]. 长江信息通信, 2023, 36(1): 205-208.

WANG Y. A method for base station parameter diagnosis and implementation based on MDT[J]. Changjiang Information & Communications, 2023, 36(1): 205-208.

李维. 面向5G的覆盖效率与质量提升策略研究[J]. 信息与电脑(理论版), 2022, 34(14): 193-195, 220.

LI W. Research on 5G-oriented coverage efficiency and quality improvement strategies[J]. Information & Computer, 2022, 34(14): 193-195, 220.

DENG J, ZHENG Q B, LIU G Y, et al. A digital twin approach for self-optimization of mobile networks[C]//Proceedings of the 2021 IEEE Wireless Communications and Networking Conference Workshops (WCNCW). Piscataway: IEEE Press, 2021: 1-6.

SKOCAJ M, AMOROSA L M, GHINAMO G, et al. Cellular network capacity and coverage enhancement with MDT data and deep reinforcement learning[J]. Computer Communications, 2022, 195: 403-415.

SÁNCHEZ-MARTÍN J M, TORIL M, WILLE V, et al. On the improvement of cellular coverage maps by filtering MDT measurements[J]. IEEE Transactions on Mobile Computing, 2023, 22(7): 4119-4133.

3GPP. 3GPP TS 38.331-NR; Radio Resource Control (RRC); Protocol specification (Version 16.7.0)[S]. 2021.

SINGH N, CHOE S, PUNMIYA R. Machine learning based indoor localization using Wi-Fi RSSI fingerprints: an overview[J]. IEEE Access, 1083, 9: 127150-127174.

ZHOU M, LI Y H, TAHIR M J, et al. Integrated statistical test of signal distributions and access point contributions for Wi-Fi indoor localization[J]. IEEE Transactions on Vehicular Technology, 2021, 70(5): 5057-5070.

LIU S J, CHANG R Y, CHIEN F T. Analysis and visualization of deep neural networks in device-free Wi-Fi indoor localization[J]. IEEE Access, 2019, 7: 69379-69392.

TONG X Y, WANG H, LIU X L, et al. MapFi: autonomous mapping of Wi-Fi infrastructure for indoor localization[J]. IEEE Transactions on Mobile Computing, 2023, 22(3): 1566-1580.

LAM K H, CHEUNG C C, LEE W C. RSSI-based LoRa localization systems for large-scale indoor and outdoor environments[J]. IEEE Transactions on Vehicular Technology, 2019, 68(12): 11778-11791.

3GPP. 3GPP TS 32.422 - Telecommunication management; Subscriber and equipment trace; Trace control and configuration management (Version 18.0.0)[S]. 2023.

CHEN Y W, ZHOU L D, PEI S W, et al. KNN-BLOCK DBSCAN: fast clustering for large-scale data[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(6): 3939-3953.

DONG G S, JIN Y, WANG S W, et al. DB-kmeans: an intrusion detection algorithm based on DBSCAN and K-means[C]//Proceedings of the 2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS). Piscataway: IEEE Press, 2019: 1-4.

LIN Y, GIACOUMIDIS E, O’DUILL S, et al. DBSCAN-based clustering for nonlinearity induced penalty reduction in wavelength conversion systems[J]. IEEE Photonics Technology Letters, 2019, 31(21): 1709-1712.

JANG J, JIANG H. DBSCAN++: towards fast and scalable density clustering[C]//Proceedings of the 36th International Conference on Machine Learning. New York: Curran Associates, 2019: 3019-3029.

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. CA: AAAI Press.1996: 226-231.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据