三维空间统计信道的空时参数AOA和TOA分析

针对在均匀散射体分布以及空间对称的3-D室内移动通信环境,细化了对于基站（BS,Base Station）以及移动台（MS,Mobile Station）端空间角度的研究。对在指向性天线覆盖下的室内微小区移动通信环境下,模型能估计多径衰落信道的重要空时信道参数,如波达信号在水平面以及竖直平面上的信号到达角度（AOA,Angle of Arrival）以及到达时间（TOA,Time of Arrival）。数值仿真结果与2一D多径衰落信道对比表明本模型的信道参数估计结果符合理论和经验,扩展了3一D空间统计信道模型的研究和应用。     

基于雨衰雪衰影响的空间信道统计模型

针对某些特殊的移动通信环境中信号的到达角度( TOA)、到达时间( AOA)等信道参数估计的复杂性,提出一种基于散射体均匀分布室外统计信道模型。该模型将树木中的树冠层作为散射体呈均匀分布在移动台与基站周围的椭圆形区域内,通过树冠层在雨雪环境中的相对介电常数来分析降雨降雪量对信号传输带来的影响,可以方便地估计出此室外微小区的空时信道参数,如AOA概率密度分布函数、TOA概率密度分布函数以及到达角度/到达时间( AOA/TOA )联合概率密度分布函数。仿真结果表明,信号的到达时间TOA以及到达角度AOA随着降雨降雪量的增大而增大,尤其当降雪量增大使得树冠层相对介电常数达到4.5时,信号的传输将被阻塞。该模型的信道参数估计结果符合理论与经验,扩展了移动无线通信信道模型的研究与应用。     

室内室外统计信道模型下的TOA分布研究

多输入多输出(MIMO)技术被认为是现代通信技术中的重大突破之一,越来越成为无线通信领域的研究热点。针对于指向性天线以及室内室外特殊环境所造成的空时多径衰落信道,本文通过假设散射体服从高斯(Gaussian)分布,指数(Exponential)分布以及瑞利(Rayleigh)分布的情况下,分别导出室内室外模型下基站BS和移动台MS的时延特性TOA(Time of Arriva)l。数值结果与多径衰落信道圆模型GBSBM(geometrically based single bounce mode)l对比表明本模型的信道参数估计结果符合理论和经验,扩展了空时统计系统模型的研究和应用,对评估多天线MIMO系统空时处理算法和仿真无线通信系统提供有力的工具。     

非对称空间统计信道模型的TOA分布研究

针对指向性天线所造成的覆盖区域面积是非对称特点,建立了非对称空间分布的空间统计信道模型,并且提出两种情况,一种是移动接收台(MS,Mobile Station)在覆盖区域内,另一种是移动接收台MS不在覆盖区域内。利用统计分析的方法,通过推导计算出在此空间统计信道模型下的到达时间(TOA, Time of Arrival)概率分布密度函数。实验仿真结果表明本模型的信道参数估计结果符合理论和经验,拓展了空间统计信道模型的研究和应用。     

一种新的空时无线信道模型及其计算机模拟方法

信道模型对分析智能天线系统的性能是非常重要的。针对基于(散射体)几何分布的单反射椭圆模型(GBsBEM)在计算机模拟时参数难以确定的不足,本文结合UMTS-IMT-2000推荐的信道模型,提出了一种新的空时信道模型,并给出了计算机模拟方法:以GBSBEM模型为基础,吸收UMTS-IMT-2000推荐的信道模型的时延和功率方面的参数并考虑GBSBEM模型中DOppler效应对信号幅度的影响,模拟空时Rayleigh信道。在这一方法的基础上,又提出了多用户空时信道简化模型以及模拟方法。计算机仿真结果表明,所提出的信道模型计算机模拟方法是可行的。     

移动通信室内外空间几何信道模型的分析与比较

针对频率非选择性无线多径衰落信道,基于室外通信环境下的空心圆模型,提出了一种改进的适应于室内通信环境的空心圆模型。通过推导接收信号的空时联合分布以及边缘分布概率密度函数,分析和比较了两种空心圆几何信道模型的空时统计特性。通过空心圆的空心率以及收发机间距的信道参数变化,仿真结果阐明了空心圆模型在圆盘模型和圆环模型之间过渡的变化机理,以及不同无线通信环境下的信道信息。     

3G LTE空间信道模型的三维修正及容量分析

空间信道模型SCM(Spatial Channel Model)是3GPP用于LTE系统仿真的MIMO二维信道模型。本文在SCM模型的基础上,综合考虑基站端和移动台端电波的离开角和到达角的三维特性,给出了三维MIMO信道模型的信道参数描述,提升了信道建模的准确度,并推导出该信道模型下包含极化特性的MIMO信道系数表达式。同时探究了天线间距、水平角扩展、俯仰角扩展等对MIMO信道空时相关性的影响。研究结果显示,随着俯仰角扩展从0°到60°增大,散射环境越来越强,信号在空间的色散度越高,不同天线单元接收到的信号之间的相关性越小,信道容量将会越大。     

MIMO信道的GBDB模型及其信道容量分析

针对非频率选择性Rice衰落MIMO移动信道，建立GBDB模型，并推导出新的MIMO系统的GBDB模型的空时联合相关函数。新的GBDB模型统一现有的多种MIMO信道模型，新的空时联合相关函数综合考虑了无线衰落信道收发两端的多普勒扩展、非均匀角度扩展以及平均发射与到达方向，综合考虑了收发端天线阵的配置。最后基于新的空时联合相关函数，分析了非均匀散射参数以及Rice分布参数变化对MIMO信道统计容量的影响。     

一种改进的椭圆散射空-时无线信道模型

空-时无线信道模型的建立是研究无线信道空-时特性的关键,然而广泛使用的基于散射体分布的单反射椭圆模型(GBSBEM)在描述TOA(Time-Of-Arrival)分布方面存在局限性.本文在GBSBEM的基础上引入了"有效散射体"和"反射概率"两个有明确物理意义的概念,给出一种改进的椭圆散射空-时无线信道模型,并推导TOA和AOA(Azimuth-Of-Arrival)的联合分布和各自的边缘分布表达式.分析表明该模型不仅能有效克服GBSBEM的局限性,并且具有适用范围广泛的特点.     

无人机三维空地信道模型的空间特性研究

针对现有无人机(UAV)空地信道几何统计模型(GBSM)对信号到达角和出射角分布的假设过于理想,无法准确描述UAV空地传播环境的空间统计特性,影响建模准确性的问题,面向基于圆柱散射体的UAV三维空地信道模型,从俯仰面和水平面详细分析了到达角和出射角对应散射区域的空间几何特征,进而推导得到角度分布的概率密度函数。通过仿真研究了信道模型的多种参数对概率密度函数的影响,证明了所提成果能够准确地刻画UAV空地信道到达角和出射角的分布,可为空地通信信道的准确建模提供支撑。     

Geometrical statistical channel model arisen from scatterers around the mobile with an inverted-parabolic spatial distribution

In this paper,firstly presented a geometrically based statistical channel model with scatterers that are with an inverted parabolic spatial distribution around mobile station(MS) within a circle wherein the base station(BS) and MS are included.This paper proposed a technique to simply derive probability density functions(PDFs) of angle of arrival(AOA),time of arrival(TOA) and Doppler spectra to characterize the outdoor macrocell and microcell environments by employing various distances between BS and MS,or different size of circular region.Employing this channel model,we analyze the impacts of a directional antenna at BS with the main-lobe width 2 on the fading and the Doppler spectra.     

Angle and Time of Arrival Statistics for a 3-D Pie-Cellular-Cut Scattering Channel Model

In this paper, investigated a 3-D pie-cellular-cut (PCC) scattering channel model for microcell environments, that idealizes the mobile station (MS) located inside of a 3-D scattering semispheroid and base station (BS) employing a directional antenna at the center of the semispheroid. The joint probability density functions (PDFs) and marginal PDFs of Angle of arrival (AOA) and Time of Arrival (TOA) seen at the BS and the MS in correspondence with azimuth and elevation angles are derived. The results show that the proposed 3D scattering channel model performs better compared with previously proposed 2D models for outdoor and indoor environments, which promotes the research of the statistical channel models.     

Angle and time of arrival statistics for circular and ellipticalscattering models

With the introduction of antenna array systems into wireless communication networks comes the need to better understand the spatial characteristics of the channel. Scattering models provide both angle of arrival (AOA) and time of arrival (TOA) statistics of the channel. A number of different scattering models have been proposed in the literature including elliptical and circular models. These models assume that scatterers lie within an elliptical and circular region in space, respectively. In this paper, the joint TOA/AOA, the marginal TOA, and the marginal AOA probability density functions (PDFs) are derived for the elliptical and circular scattering models. These PDFs provide insight into the properties of the spatial wireless channel     

基于CFR虚拟阵列天线的AOA室内定位

准确地估计信号的到达角（Angle Of Arrival,AOA）为实现在室内高精度定位提供了可能,为了能够准确地估计室内多径信号的AOA,并提取出直射路径的AOA信息进行定位,本文提出一种利用信道频率响应信息（Channel Frequency Response,CFR）扩展阵列天线的亚米级室内定位系统.首先,采集CFR信息进行AOA和信号到达时间（Time Of Arrival,TOA）的联合估计;其次,提出了一种基于AOA和TOA二维聚类信息的直射路径识别算法;另外,还提出了可视环境（Line Of Sight,LOS）以及非可视环境（Non Line Of Sight,NLOS）的识别算法,可以准确的判断出当前接收机相对发射机是处于LOS还是NLOS环境;最后,利用现有的三天线Wi-Fi设备在室内进行了测角以及定位测试,实验结果表明本文提出的定位系统在室内LOS和NLOS环境下分别可以达到中值误差为0.8m,1.3m的定位精度,可用于室内高精度定位.     

一种基于圆环模型的网格搜索定位算法

为了降低非视距(Non-Line-of-Sight,NLOS)误差对定位精度的不利影响,提出了一种基于圆环模型的到达时间(Time of Arrival,TOA)/到达角(Angle of Arrival,AOA)混合定位算法。假设各基站(Base Station,BS)接收的多径信号都经历了散射体的单次散射,首先利用圆环模型的几何特征搜索圆环的半径,再根据圆环的半径、TOA信息和AOA信息确定散射体的位置;然后将散射体作为虚拟基站规划移动台(Mobile Station,MS)的可能位置范围,并使用网格搜索和约束判断来获得多个MS可能的估计位置;最后根据误差目标函数的大小进行选择性平均。在不同NLOS场景下的仿真结果表明,与现有算法相比,所提算法取得了更好的定位性能。     

Statistical cellular Gaussian scatter density channel model employing a directional antenna for mobile environments

The statistical Gaussian scatter density model (GSDM) is used to evaluate the effects on the Doppler power density spectrum (PDS) when a directional antenna at base station (BS) is employed. We derive the angle of arrival (AOA) probability density function (pdf ) of the multipath components at the mobile station (MS). The AOA pdf at the MS and the PDS, are compared with those for the Clarke's model and the macrocell circular model. We demonstrate that the circular model overestimates the probability of scatterers with AOA near to the line of sight (LoS), resulting in a poorer estimation of the PDS unlike the Gaussian model.     

一种基于单次散射体定位的TOA/AOA混合定位算法

为了降低非视距(Non-Line-of-Sight,NLOS)误差对定位精度的不利影响,提出了一种基于散射体定位的到达时间(Time of Arrival,TOA)/到达角(Angle of Arrival,AOA)混合定位算法.假设各基站(Base Station,BS)接收的多径信号都经历了散射体单次散射,首先利用...     

Joint TOA/AOA Positioning Scheme with IP-OFDM Systems

In this paper, a new positioning scheme with impulse postfix OFDM (IP-OFDM) systems is proposed for estimating the positional information of a user within a wireless local area network. In the proposed scheme, the channel information is estimated by the IP of the IP-OFDM symbol in the time domain, and then the round-trip time and the angle of signal arrival from a wireless terminal to an access point equipped with a uniform linear antenna array are investigated for time-of-arrival (TOA) and angle-of-arrival (AOA) estimation for a user. The position of the wireless terminal is finally estimated by combining the estimated TOA and AOA information. The performance of the proposed scheme is evaluated with computer simulations, and the error characteristics are analyzed with respect to the TOA error, and the AOA error influence on the positioning error. According to the simulation results, the positioning error of the proposed scheme can be reduced to some centimeters for high signal-to-noise ratio (SNR). The major influence on the positioning error comes from the TOA error in low SNR, while the AOA error has more influence on the positioning error in high SNR.