随机射线的概率分布及其应用

在使用随机射线方法建模无线传播信道时,需要求解以反射次数为指标的无线电波经过若干次反射以后达到特定位置的概率分布。该文使用信息论中的最大熵原理,首先计算在Manhattan距离度量下二维和三维空间连续情形和离散情形下随机射线的概率密度函数。然后计算在Euclid距离度量下二维和三维空间连续情形下随机射线的概率密度函数,以及作随机游动的随机射线在二维空间的概率密度函数。使用城市密集传播地区的测量数据验证随机射线理论模型结果的可靠性。所得结果对于无线随机传播信道建模具有理论指导意义。     

距离无关的室内路径损耗模型的建立

针对传统室内路径损耗依赖于距离以及其信道参数在LOS(视距)和NLOS(非视距)下的不确定性问题,提出了一种室内的纯统计路径损耗模型.该模型以距离作为随机变量,并引入室内路径切换频度表示LOS和NLOS的信道条件,从而消除路径损耗对距离的依赖以及信道参数的不确定性.通过该模型,在毋需知道路径长度的情况下,根据操作空间的半径和室内路径切换频度以及路径损耗的置信度估算出路径损耗,同时该模型也提出了3种服务质量(QoS)以满足不同的客户需求,与总是以最大功率发送信号的传统模型相比大大优化了资源配置.经实例验证表明,此模型应用方便,是一种简单且实用的模型.     

一种新的无线电波传播路径损耗模型

使用渗流网格建模无线电波的随机传播环境,在此基础上使用随机射线方法,从概率论的角度得到若干路径损耗的解析公式.经过与经典路径损耗模型以及其他由非波动方法得到的路径损耗模型的比较之后,从逻辑上推断出一个新的路径损耗模型.对新模型中参数的取值范围进行了讨论和分析,最后使用城市密集传播地区的测量数据验证新的路径损耗模型的精确性.     

28 GHz室内毫米波信道路径损耗模型研究

毫米波信道建模是第五代（the 5th Generation,5G）移动通信系统的关键技术,而路径损耗是表征毫米波信道传播大尺度衰落影响的重要参数.为了更好地理解毫米波信道的传播特性,应进行广泛的信道测量与建模.因此,对28 GHz室内环境进行了信道测量,并给出了相应的毫米波信道路径损耗模型,同时基于入射及反弹射线法/镜像法仿真分析了路径损耗传播特性.研究结果表明:实测结果与仿真结果一致性吻合良好,从而验证了入射及反弹射线法/镜像法的正确性;自由空间邻近（Close-In,CI）参考距离路径损耗模型表达式更简洁,鲁棒性更强.最后,本文给出了一种普遍适用的用来表征室内视距（Line-of-Sight,LOS）与非视距（Non-Line-of-Sight,NLOS）环境28 GHz与60 GHz毫米波信道的路径损耗模型.     

室内X波段离体信道传播特性的测量与建模

研究了在X波段以人体为中心环境的无线信道衰落模型建模方法，围绕可穿戴天线在体表不同部位的无线信道进行了实验和分析.分别在离体与无体场景下测量视距（line-of-sight, LoS）和非视距（non-lineof-sight, NLoS）两种路径的信道衰落，对比分析人体不同部位的阴影效应和穿透损耗，结果表明改变天线穿戴部位，信道衰落曲线的损耗指数和截距均会发生变化；人体穿透损耗与信道频率、收发端天线之间的距离无关.基于现有的模型对实验数据进行拟合，提出一种与人体不同部位相关的带有体表阴影效应、穿透损耗修正因子的离体信道衰落模型.该模型细化了人体不同部位的信道特性，与通用模型相比能更加精地确描述离体信道传播环境.     

校园休闲区环境2.4GHz无线信道路径损耗建模

无线信道的质量在很大程度上制约着无线通信系统的质量,发射机到接收机之间的信号传播路径从简单的视距,到遭遇各种各样的地形物。不同环境下的无线电传播模型研究一直是通信方面的热点,文章针对校园休闲区环境无线网络应用需求,开展了无线信道路径损耗建模适用性分析。首先利用WSN节点进行2.4GHz无线信道的实时测试,得到不同传播距离上的信号损耗值,然后与自由空间模型,单、双折线对数距离路径损耗模型进行了对比分析,最后通过对比分析发现改进后双折线对数距离路径损耗模型更适用于此环境。     

39 GHz室外微蜂窝信道测量、建模与仿真研究

基于39 GHz室外微蜂窝场景实测数据,开展了毫米波段路径损耗、阴影衰落和大尺度参数的建模与仿真研究.介绍了毫米波段喇叭旋转测量系统下空间交替广义期望最大化(Space-Alternating Generalized Expectation-maximization,SAGE)算法信号模型,优化的分簇算法与莱斯因子计算方法.基于SAGE提取多径参数,利用优化的分簇算法提取并分析了簇参数,包括簇内角度扩展、簇内时延扩展以及簇的数目,并根据测量结果验证了第三代合作伙伴计划(The 3rd Generation Partnership Project,3GPP)第五代(the 5th Generation,5G)移动通信标准推荐的仿真平台准确定性无线信道产生器(Quasi-Deterministic Radio Channel Generator,QuaDRiGa)在39 GHz的可用性.结果表明:在视距径下,方向性路损和全向路损在固定截距和浮动截距两种拟合方式下与自由空间路损模型接近;大尺度参数统计特性与基于毫米波的第五代集成通信移动无线电接入网络(Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications,mmMAGIC)、3GPP结论接近;视距径与非视距径的簇参数差别较小,且簇的个数较6 GHz下的频段更少.本文为5G毫米波39 GHz频段信道仿真和系统设计提供了重要的信道模型和参数.     

超短波频段复杂城区场景的信道测量与建模

为了准确反映复杂城区环境下移动自组织网络（mobile ad hoc network,MANET）通信节点间的无线信道传播特性,以150 MHz频点为例对山东省青岛市典型城区场景的超短波无线信道进行了外场测量.基于测量数据开展统计分析,提取了路径损耗指数、阴影衰落标准差、多径时延拓展以及频率相关性等信道参数,并针对准视距（quasi-light-of-sight,QLOS）区和非视距（non-light-of-sight,NLOS）区建立了大尺度衰落模型和小尺度抽头延迟线模型.分析和建模结果表明:在MANET城市通信场景下,路径损耗指数随距离增加呈现出双斜率特性;当累积分布函数（cumulative distribution function,CDF）为0.9时,均方根时延在QLOS区为726 ns,在阻挡严重的NLOS区为967 ns;相关带宽在QLOS区和NLOS区分别为700 kHz和300 kHz.论文所建信道模型有利于准确衡量MANET通信设备在复杂城区的通信性能,并为相关通信系统设计提供参考.     

隧道内圆极化电磁波传播特性的研究

交通隧道的发展要求实现全程的无线通信。运用波导理论及格林函数法对圆极化电磁波在长直圆形隧道中的场分布及传播特性进行了研究,得到了轴向距离的电场分布计算表达式,与已有隧道内传播损耗模型的比较也表明此模型更准确有效,其结果对于隧道内通信网络的优化有一定的指导意义。     

雾环境下非视距散射光通信最佳链路分析

基于概率论和随机迁移理论,建立多次散射随机模型,采用蒙特卡洛方法仿真分析了辐射雾条件下,不同的通信距离条件时,近红外信号光在大气传输信道中传输的路径损耗与能见度之间的关系,指出在给定的通信距离以及给定的系统发送端发送仰角和发送光束束散角,接收端接收仰角和接收视场角等几何参数下,会存在一个能见度使得在这个能见度条件下信号光传输的损耗最小。在通信距离、能见度给定的情况下,针对大气散射通信几何构架中的各个参数的改变都会对非视距大气散射光通信链路路径损耗产生影响,通过模型仿真,提出辐射雾环境下最优化通信链路几何构架。仿真中采取808 nm 波长的激光二极管(LD)作为光源。     

Path-Loss Prediction Models for Intervehicle Communication at 60 GHz

Propagation path losses affecting intervehicle communication (IVC) at 60 GHz are presented. In order to examine the radio-propagation characteristics in the IVC system, in the first stage of our investigation, we have carried out propagation tests between two vehicles communicating in a line-of-sight (LOS) situation at fixed positions on a smooth surface paved with asphalt, which is the normal communication condition. Furthermore, the non-LOS (NLOS) situation with up to three intermediate vehicles, which can cause an obstruction, has been conducted. To construct path-loss models in NLOS cases, the uniform theory of diffraction technique was applied to the calculation of waves propagating through the intermediate vehicles. The propagation path models were derived from the measured results. In the second stage, a path-loss prediction formula was derived by statistically processing the data that had been calculated using the propagation path model. The propagation tests between the vehicles in motion confirm that the path-loss prediction formula is very useful when the communicating vehicles are moving, and it is therefore suitable for designing the cells that form the IVC.     

Propagation prediction model and performance analysis of RFID system under metallic container production circumstance

Radio-wave attenuation negatively affects RF communications in a RFID system. In order to ensure the reliability of RFID system, one must predict the radio-wave path-loss accurately before detailed design. However, it is infeasible to estimate the path-loss for the container RFID systems using the conventional path-loss models such as the Okumura model, two-ray model and so on. This paper firstly focuses on studying the radio propagation mechanism in the container RFID system, analyzing the radio-wave diffraction in the container metallic environment and establishing a mathematic model to estimate the path-loss between the readers and tags. A test in a real container working area was conducted and it was found that the actual measuring results are consistent with the corresponding computing results according to the established path-loss model. The model is further used to analyze some performances of the container RFID system. A group of curves are obtained to show the relationships among the identification distance, the sending power and the BER (bit error rate). The paper is concluded that the TE polarization mode cannot be applied to a container RFID system and the attenuation caused by multi-path propagation will dramatically reduce the recognition reliability of a container RFID system.     

基于L1范数稀疏距离测度学习的单类分类算法

 已有单类分类算法通常采用欧氏测度描述样本间相似关系,然而欧氏测度有时难以较好地反映一些数据集样本的内在分布结构,为此提出一种用于改善单类分类器描述性能的高维空间单类数据距离测度学习算法,与已有距离测度学习算法相比,该算法只需提供目标类数据,通过引入样本先验分布正则化项和L1范数惩罚的距离测度稀疏性约束,能有效解决高维空间小样本情况下的单类数据距离测度学习问题,并通过采用分块协调下降算法高效的解决距离测度学习的优化问题.学习得到的距离测度能容易地嵌入到单类分类器中,仿真实验结果表明采用学习得到的距离测度能有效改善单类分类器的描述性能,特别能够改善覆盖分类的描述能力,从而使得单类分类器具有更强的推广能力.     

Comment on diffraction loss of rooftop-to-street in COST231-Walfisch-Ikegami model

As a small-cell path-loss prediction model, the COST 231-Walfisch-Ikegami model has been used extensively. It facilitates radio frequency (RF) path-loss predictions in typical suburban and urban environments where the building heights are quasi-uniform. However, it was found that the expression accounting for the diffraction loss from the last rooftop to the street was erroneously obtained. In this paper, we show why the error is present in the COST 231-Walfisch-Ikegami model and how it impacts on path-loss prediction     

PDA-SVM Hybrid: A Unified Model for Kernel-Based Supervised Classification

For most practical supervised learning applications, the training datasets are often linearly nonseparable based on the traditional Euclidean metric. To strive for more effective classification capability, a new and flexible distance metric has to be adopted. There exist a great variety of kernel-based classifiers, each with their own favorable domain of applications. They are all based on a new distance metric induced from a kernel-based inner-product. It is also known that classifier’s effectiveness depends strongly on the distribution of training and testing data. The problem lies in that we just do not know in advance the right models for the observation data and measurement noise. As a result, it is impossible to pinpoint an appropriate model for the best tradeoff between the classifier’s training accuracy and error resilience. The objective of this paper is to develop a versatile classifier endowed with a broad array of parameters to cope with various kinds of real-world data. More specifically, a so-called PDA-SVM Hybrid is proposed as a unified model for kernel-based supervised classification. This paper looks into the interesting relationship between existing classifiers (such as KDA, PDA, and SVM) and explains why they are special cases of the unified model. It further explores the effects of key parameters on various aspects of error analysis. Finally, simulations were conducted on UCI and biological data and their performance compared.     

On Unbounded Path-Loss Models: Effects of Singularity on Wireless Network Performance

This paper addresses the following question: how reliable is it to use the unbounded path-loss model G(d) = d^?α, where α is the path-loss exponent, to model the decay of transmitted signal power in wireless networks? G(d) is a good approximation for the path-loss in wireless communications for large values of d but is not valid for small values of d due to the singularity at 0. This model is often used along with a random uniform node distribution, even though in a group of uniformly distributed nodes some may be arbitrarily close to one another. The unbounded path-loss model is compared to a more realistic bounded path-loss model, and it is shown that the effect of the singularity on the total network interference level is significant and cannot be disregarded when nodes are uniformly distributed. A phase transition phenomenon occurring in the interference behavior is analyzed in detail. Several performance metrics are also examined by using the computed interference distributions. In particular, the effects of the singularity at 0 on bit error rate, packet success probability and wireless channel capacity are analyzed.     

Bound for Minkowski metric or quadratic metric applied to VQcodeword search

A bound for a Minkowski metric based on L_p distortion measure is proposed and evaluated as a means to reduce the computation in vector quantisation. This bound provides a better criterion than the absolute error inequality (AEI) elimination rule on the Euclidean distortion measure. For the Minkowski metric of order n, this bound contributes the elimination criterion from the L₁ metric to L _n metric. This bound can also be an extended quadratic metric which can be a hidden Markov model (HMM) with a Gaussian mixture probability density function (PDF). In speech recognition, the HMM with the Gaussian mixture VQ codebook PDF has been shown to be a promising method     

基于三维的射线跟踪八叉树算法的城市微区电波传播预测算法模型

本文建立了一种有效的基于射线跟踪技术的三维城市微区电波传播预测的计算模型.该模型将计算机图形学中的八叉树分区技术扩展至射线跟踪技术,并由此提出了一种新的有效的射线跟踪算法,射线跟踪八叉树算法.并基于几何光学、一致性绕射(UTD)对场强进行计算,所得结果与实测值及FDTD的结果进行了比较.     

A method for improving the classification speed of clustering algorithms which use a Euclidean distance metric

Many pattern recognition computer programs use one of the clustering algorithm techniques. Often these algorithms use a Euclidean distance metric as a similarity measure. A scheme is proposed where both the Euclidean metric and a more simple city-block metric are utilized together to reduce overall classification time. The relation between the Euclidean and city-block distances is introduced as a scalar function. The bounds of the function are given and used to decide whether classification of each pattern vector is to be achieved by the computationally slow Euclidean distance or the faster city-block distance. The criteria is that the classification should be identical to the original Euclidean only scheme.