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...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。展开更多
Due to the fine-grained communication scenarios characterization and stability,Wi-Fi channel state information(CSI)has been increasingly applied to indoor sensing tasks recently.Although spatial variations are explici...Due to the fine-grained communication scenarios characterization and stability,Wi-Fi channel state information(CSI)has been increasingly applied to indoor sensing tasks recently.Although spatial variations are explicitlyreflected in CSI measurements,the representation differences caused by small contextual changes are easilysubmerged in the fluctuations of multipath effects,especially in device-free Wi-Fi sensing.Most existing datasolutions cannot fully exploit the temporal,spatial,and frequency information carried by CSI,which results ininsufficient sensing resolution for indoor scenario changes.As a result,the well-liked machine learning(ML)-based CSI sensing models still struggling with stable performance.This paper formulates a time-frequency matrixon the premise of demonstrating that the CSI has low-rank potential and then proposes a distributed factorizationalgorithm to effectively separate the stable structured information and context fluctuations in the CSI matrix.Finally,a multidimensional tensor is generated by combining the time-frequency gradients of CSI,which containsrich and fine-grained real-time contextual information.Extensive evaluations and case studies highlight thesuperiority of the proposal.展开更多
In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive influence.This paper presents an innovative approach to passenger cou...In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive influence.This paper presents an innovative approach to passenger countingonbuses throughthe analysis ofWi-Fi signals emanating frompassengers’mobile devices.The study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels,thereby addressing a crucial aspect of public transportation.The proposed system comprises three crucial elements:Signal capture,data filtration,and the calculation and estimation of passenger numbers.The pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts undermoderate-crowding conditions,with an average deviation of 20%from the ground truth and an accuracy rate ranging from 90%to 100%.This underscores its efficacy in scenarios characterized by moderate levels of crowding.However,in densely crowded conditions,the system exhibits a tendency to overestimate passenger numbers,occasionally doubling the actual count.While acknowledging the need for further research to enhance accuracy in crowded conditions,this study presents a pioneering avenue to address a significant concern in public transportation.The implications of the findings are poised to contribute substantially to the enhancement of bus operations and service quality.展开更多
文摘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。
基金the National Natural Science Foundation of China under Grant 61771258 and Grant U1804142the Key Science and Technology Project of Henan Province under Grants 202102210280,212102210159,222102210192,232102210051the Key Scientific Research Projects of Colleges and Universities in Henan Province under Grant 20B460008.
文摘Due to the fine-grained communication scenarios characterization and stability,Wi-Fi channel state information(CSI)has been increasingly applied to indoor sensing tasks recently.Although spatial variations are explicitlyreflected in CSI measurements,the representation differences caused by small contextual changes are easilysubmerged in the fluctuations of multipath effects,especially in device-free Wi-Fi sensing.Most existing datasolutions cannot fully exploit the temporal,spatial,and frequency information carried by CSI,which results ininsufficient sensing resolution for indoor scenario changes.As a result,the well-liked machine learning(ML)-based CSI sensing models still struggling with stable performance.This paper formulates a time-frequency matrixon the premise of demonstrating that the CSI has low-rank potential and then proposes a distributed factorizationalgorithm to effectively separate the stable structured information and context fluctuations in the CSI matrix.Finally,a multidimensional tensor is generated by combining the time-frequency gradients of CSI,which containsrich and fine-grained real-time contextual information.Extensive evaluations and case studies highlight thesuperiority of the proposal.
基金from Prince Sattam bin Abdulaziz UniversityProject Number(PSAU/2023/R/1445).
文摘In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive influence.This paper presents an innovative approach to passenger countingonbuses throughthe analysis ofWi-Fi signals emanating frompassengers’mobile devices.The study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels,thereby addressing a crucial aspect of public transportation.The proposed system comprises three crucial elements:Signal capture,data filtration,and the calculation and estimation of passenger numbers.The pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts undermoderate-crowding conditions,with an average deviation of 20%from the ground truth and an accuracy rate ranging from 90%to 100%.This underscores its efficacy in scenarios characterized by moderate levels of crowding.However,in densely crowded conditions,the system exhibits a tendency to overestimate passenger numbers,occasionally doubling the actual count.While acknowledging the need for further research to enhance accuracy in crowded conditions,this study presents a pioneering avenue to address a significant concern in public transportation.The implications of the findings are poised to contribute substantially to the enhancement of bus operations and service quality.
