街道环境V2V通信信道建模与容量分析

针对街道3D散射环境下的车载通信信道,考虑到道路两边高楼、树木等散射体,且收发两端处于移动状态,采用多天线技术,建立椭圆柱散射模型。运用几何分析法,推导了时间自相关函数（ACF）、空间互相关函数（CCF）,比较视距（LOS）和非视距（NLOS）情况下ACF和CCF的差异,验证仿真方法的可行性;研究了信道容量在不同天线阵列和散射环境下的区别,突出了3D模型更加精确。测量结果与理论分析的一致性表明该3D模型的可用性,拓宽了车载通信系统的研究。     

降雨环境下毫米波MIMO信道特性研究

米波作为5G通信的重要核心技术,具有广阔的发展前景。随着通信频率的提升,天气(尤其是降雨环境)对信号传播的影响日益增大。为了建立准确有效的传播信道模型,针对降雨环境下车对基站移动通信场景,提出了基于25GHz, 30GHz和78GHz的三维毫米波的MIMO空间信道模型。首先介绍了雨滴的几何物理特性,然后对雨衰工程模型作了简要描述,在此基础上提出关于本模型的动态雨衰衰减表达式。该模型考虑了基站与信号接收机的视距与非视距路径,以及接收机运动和动态散射簇对信道在时域和空间域上的非平稳性。最后采用生灭过程对信道进行理论分析,推导频率、降雨量、俯仰角等参数对互相关函数以及信道容量的影响。通过与以往文献实测数据的对比发现,该模型与实际测量结果拟合程度高。该研究对降雨环境下无线通信系统的应用具有重要的理论和实际意义,丰富了移动无线通信信道模型的研究和应用。     

新型基于几何的3D非平稳V2V MIMO信道模型

针对具有多种通信场景和收发端快速移动的V2V通信系统,提出一种基于几何的3D V2V MIMO信道模型.该模型首次结合双球模型和半椭球体模型,分别使用双球和半椭球体模型表征动态和静止散射体;考虑到收发端的运动状态,引入时变的出发角、到达角以及路径长度用于研究V2 V信道的非平稳特性;对所提出的信道模型,推导了空间互相关、时间自相关和多普勒功率谱密度等统计特性函数,分析了不同场景和参数下的V2 V信道统计特性.结果表明,不同场景下V2 V信道各统计特性有较大差异,信道相关性与时间变化和散射体分布密切相关.仿真结果与理论值的高度拟合验证了模型的正确性,为V2 V通信链路的设计提供了理论依据,拓展了基于几何的V2 V信道建模领域的研究.     

面向楼梯应急通信系统的随机多簇信道模型

楼梯信道模型是设计楼梯应急通信系统的关键,而信号多簇特性对信道建模具有重要影响,对此提出了新的随机多簇信道冲击响应（Channel Impulse Response,CIR）模型。对于视距（Line of Sight,LOS）环境,提出用距离相关函数拟合簇到达时间。而非视距（Non LOS,NLOS）环境下,簇到达时间与距离无关。CIR中每个簇用指数衰减函数描述,其中衰减常量随距离变化。最后用实测数据验证所提出模型的准确性,并用S-V模型作比较。结果表明,所提出模型更适合描述室内楼梯传播特性。     

基于PUF的5G车联网V2V匿名认证与密钥协商协议

针对目前5G车联网中车辆之间(vehicle-to-vehicle, V2V)通信的认证与密钥协商方案算法复杂、时延高的问题,提出一种基于物理不可克隆函数(physical unclonable function, PUF)的5G车联网V2V匿名认证与密钥协商协议.协议通过引入轻量级PUF避免了V2V认证中的数字签名操作,并精简通信步骤,成功减轻车辆的计算和通信开销.协议还借助PUF实现了车辆的车载单元(on board unit, OBU)和5G SIM卡的绑定,解决了身份假冒问题.同时,通过构建身份索引表,实现监管部门通过5G服务网(serving work, SN)对车辆的伪身份溯源,满足条件匿名性要求.使用形式化工具AVISPA验证了协议在Dolve-Yao模型下的安全性,并在计算开销、通信开销、安全性方面优于已有的车联网匿名通信协议,可为5G车联网的V2V通信提供基本安全保障.     

紫外光非视距通信大气信道模拟与分析

自由空间紫外光非视距通信具有隐蔽、安全、高效的特性。本文根据Mie散射理论在Matlab环境下对紫外光非视距大气信道进行了模拟,分析了典型参数大气粒子对紫外光通信散射效率因子和特征图像的影响。模拟结果表明,大气粒子尺寸越大,紫外光散射的方向性越强;粒子的折射率越大,吸收作用越明显,紫外光散射的方向性越弱。本文的模拟结果为紫外光非视距通信系统的进一步发展提供了借鉴。     

一种V2V多天线几何去极化信道建模

随着极化相关技术的发展,极化调制在V2V（vehicle-to-vehicle)通信系统中的应用越来越广泛。但由于V2V通信环境复杂,极化调制在V2V通信中会产生去极化效应,从而影响通信质量。为了能够识别去极化效应的大小,提出一种新的基于几何散射理论的V2V多天线极化信道模型。该模型可通过已知的多天线配置、极化场辐射模式及散射体的空间分布,建立去极化效应模型,从而将信道的去极化效应由交叉极化鉴别度——XPD(cross polarization discrimination)的值表示,以拓展极化调制在V2V系统中的使用范围,提高传输速率。实验结果表明,XPD的值受到车辆间距离影响存在阈值,并强烈依赖于到达方位角的分布常数,且当到达方位角的值不同时,改变到达仰角的最大值的大小,XPD值的变化呈固定趋势。因此,本文所提出的多天线几何信道模型,成功的将极化引入多天线的V2V通信,可以为极化域通信的进一步研究奠定基础。     

基于MI-PUF的V2X车联网通信安全认证协议北大核心CSCD

针对目前车联万物(Vehicle-to-Everything,V2X)中车辆与路边单元(Vehicle-to-Infrastructure,V2I)、车辆与车辆(Vehicle-to-Vehicle,V2V)通信的认证协议计算开销大、易受到攻击者假冒合法身份攻击的问题,文章提出一种基于索引图与索引提示符物理不可克隆函数(Map-Index Physical Unclonable Function,MI-PUF)的车联网通信安全认证协议。该协议引入PUF并利用其轻量级计算的特性降低车辆的计算开销和通信开销;借助PUF自身不可克隆的特性,解决身份假冒攻击问题;通过构建索引图以及哈希函数对PUF的输出信号进行处理,有效解决了机器学习攻击问题。在Dolve-Yao模型下使用形式化验证工具AVISPA验证该协议的安全性,实验结果表明,该协议能够为车联网的V2I及V2V通信提供基本的安全保障。     

面向车联网通信的非平稳信道仿真方法

针对车联网通信过程中收发端双移动带来的信道状态快速时变和非平稳问题,提出一种面向车-车通信的非平稳信道仿真方法。通过引入积分项保证输出信道衰落相位的连续性,考虑收发端参数的时变特性,实现多普勒频率参数的准确计算。数值仿真结果表明,该方法生成的信道统计特性如概率密度函数、自相关函数和多普勒功率谱密度的仿真值与理论值吻合。因此,该仿真方法可用于未来车载通信设备研发和批量生产阶段的验证评估和性能测试。     

基于移动扩展模型的无人机三维空地非平稳信道建模

无人机空地信道几何统计模型通常采用固定的速度和移动方向,无法描述UAV空地传播环境的非平稳特性。面向基于双圆柱散射体的UAV三维空地信道模型,提出在空地建模中引入具有动态速度和移动方向的高斯马尔可夫模型,模拟无人机在现实环境中的变化;为了使该模型模拟不同的运动,引入扩展因子切换模型的运动轨迹;此外,运用几何分析法,根据时变的速度和运动方向推导了空时相关函数和多普勒功率谱密度,并研究了无人机水平和垂直运动对信道统计特性的影响。仿真结果表明,扩展的模型仅会导致时域的非平稳,对空域没有影响,并验证了扩展模型的通用性。     

3D non-stationary vehicle-to-vehicle MIMO channel model for 5G millimeter-wave communications

Vehicle-to vehicle (V2V) is one of the most challenging scenarios in the fifth-generation (5G) millimeter-wave (mmW) mobile communication networks. Existing models cannot be utilized without development for compatibility with modern measurements, which have proved that the wide-sense stationary (WSS) modeling assumption is valid only for very short intervals. In this paper, a novel three-dimensional (3D) geometry-based stochastic model (GBSM) for non-stationary (non-WSS) narrowband V2V multi-input-multi-output (MIMO) channel with both 3D fixed and moving scatterers around the transmitter (Tx) and the receiver (Rx) is proposed. The azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA) and elevation angle of arrival (EAoA) of the 3D moving scatterers have been considered as time varying angles, and this makes the model non-stationary. Based on the proposed model, the statistical properties, including space-time correlation function (STCF), space-Doppler power spectral density (SD-PSD), level crossing rate (LCR), and average fade duration (AFD) are derived and compared with those of measured data and state-of-the-art channel models. Finally, the close agreement between results reveal that the proposed 3D model with 3D moving scatterers reflects the real-world V2V channel characteristics, especially in 5G mmW networks.     

3D non-stationary wideband circular tunnel channel models for high-speed train wireless communication systems

This paper proposes three-dimensional (3D) non-stationary wideband circular geometry-based stochastic models (GBSMs) for high-speed train (HST) tunnel scenarios. Considering single-bounced (SB) and multiple-bounced (MB) components from the tunnel’s internal surfaces, a theoretical channel model is first established. Then, the corresponding simulation model is developed using the method of equal volume (MEV) to calculate discrete angular parameters. Based on the proposed 3D GBSMs, important time-variant statistical properties are investigated, such as the temporal autocorrelation function (ACF), spatial cross-correlation function (CCF), and space-Doppler (SD) power spectrum density (PSD). Results indicate that all statistical properties of the simulation model, verified by simulation results, can match well those of the theoretical model. The statistical properties of the proposed 3D GBSMs are further validated by relevant measurement data, demonstrating the flexibility and utility of our proposed tunnel GBSMs.     

基于EMD的四边域曲面光顺算法

曲面光顺在计算机辅助几何设计（CAGD）中有重要应用,带噪声离散曲面可视为一种非平稳离散几何信号。经验模式分解（EMD）方法是分析非线性、非平稳信号的有效方法。提出了一种空间任意曲线EMD光顺方法和基于2维可分离的EMD曲面光顺方法。针对四边域离散曲面可视为U和V离散曲线构成的网格,且U和V曲线呈现空间任意形态。空间曲线光顺中,首先对数字曲线进行1维参数化,将曲线展开成1维信号;然后采用EMD对展开信号进行多分辨率分解,得到不同尺度下的内蕴模式函数(IMF),去除高频的IMF,重构信号;最后将重构信号逆映射回3维,得到光顺后的曲线。四边域曲面沿每条U,V线进行EMD光顺处理,得到光顺后曲面。实验结果表明,该方法可有效剔除曲面上的随机噪声,达到良好的曲面光顺效果。     

Electric vehicle smart charging and vehicle-to-grid operation

Electric vehicle (EV) charging must be optimised for grid load while guaranteeing that drivers' schedules and range requirements are met. A system encompassing EV owner input via a mobile application, an aggregation middleware, a charge scheduling and vehicle-to-grid (V2G) operation algorithm and a radio-frequency identification reader is proposed. The algorithm's parameters and effectiveness are presented and discussed using simulation results. Simulation results show the algorithm to effectively optimise charging and V2G operation for a given electricity price curve. The proposed system is shown to alleviate grid load during peak hours, take advantage of off-peak charging benefits and generate revenue for the parking garage operator.     

Clustering data in an uncertain environment using an artificial immune system

Clustering of data in an uncertain environment can result into different partitions of the data at different points in time. Therefore, the initial formed clusters of non-stationary data can adapt over time which means that feature vectors associated with different clusters can follow different migration types to and from other clusters. This paper investigates different data migration types and proposes a technique to generate artificial non-stationary data which follows different migration types. Furthermore, the paper proposes clustering performance measures which are more applicable to measure the clustering quality in a non-stationary environment compared to the clustering performance measures for stationary environments. The proposed clustering performance measures in this paper are then used to compare the clustering results of three network based artificial immune models, since the adaptability and self-organising behaviour of the natural immune system inspired the modelling of network based artificial immune models for clustering of non-stationary data.     

Design of Semipersistent Resource Allocation in LTE-V Network

Radio network access technology currently used in 4G/5G is Long Term Evolution-Advanced (LTE-A), which was developed by 3rd Generation Partnership Project (3GPP). Device-to-device (D2D) communication is a technology enabling direct communications among wireless devices without forwarding through an evolved Node B (eNB). Moreover, D2D transmission can support vehicles as a vehicle-to-vehicle (V2V) environment. It is possible to avoid accidents via exchanging movement-related information among vehicles and effectively increase driving safety (and efficiency). However, radio resources are limited in radio networks. A vehicle transmits through D2D in Long Term Evolution-Vehicle (LTE-V) mode-3 standard, and an eNB can allocate the same spectrum radio resources for cellular and V2V links simultaneously. When using the same radio resources, the probability of interference may increase. This study designed a semipersistent resource allocation algorithm based on different cycles in an LTE-V network. Moreover, resource allocation under different cycles was analyzed, and a scheme for resource selection is proposed based on cycle size. The proposed Semi Persistent Gain Aware Resource Allocation (SP-GARA) scheme selects resources based on the expected sum rate of the cycle size and analyzes and discusses the results of the total sum rate at different cycles and speeds for an improved performance.     

基于自适应混合高斯模型的时空背景建模

提出了一种基于自适应混合髙斯模型的时空背景建模方法, 有效地融合了像素在时空域上的分布信息, 改善了传统的混合髙斯背景建模方法对非平稳场景较为敏感的缺点. 首先利用混合髙斯模型学习每个像素在时间域上的分布, 构造了基于像素的时间域背景模型, 在此基础上, 通过非参数密度估计方法统计每个像素邻域内表示背景的髙斯成分在空间上的分布, 构造了基于像素的空间域背景模型; 在决策层融合了基于时空背景模型的背景减除结果. 为了提高本文时空背景建模的效率, 提出了一种新的混合高斯模型髙斯成分个数的自适应选择策略, 并利用积分图实现了空间域背景模型的快速计算. 通过在不同的场景下与多个背景建模方法比较, 实验结果验证了本文算法的有效性.     

Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression

Despite growing concerns for the variation of urban thermal environments and driving factors, relatively little attention has been paid to issues of spatial non-stationarity and scale-dependence, which are intrinsic properties of the urban ecosystem. In this paper, using Shenzhen City in China as a case study, a geographically weighted regression (GWR) model is used to explore the scale-dependent and spatial non-stationary relationships between urban land surface temperature (LST) and environmental determinants. These determinants include the distance between city and highway, patch richness density of forestland, wetland, built-up land and unused land and topographic factors such as elevation and slope aspect. For reference, the ordinary least squares (OLS) model, a global regression technique, was also employed, using the same response variable and explanatory variables as in the GWR model. The results indicate that the GWR model not only provides a better fit than the traditional OLS model, but also provides local detailed information about the spatial variation of LST, which is affected by geographical and ecological factors. With the GWR model, the strength of the regression relationships increased significantly, with a mean of 59% of the changes in the LST values explained by the predictors, compared with only 43% using the OLS model. By computing a stationarity index, one finds that different predictors have different variational trends which tend towards the stationary state with the coarsening of the spatial scale. This implies that underlying natural processes affecting the land surface temperature and its spatial pattern may operate at different spatial scales. In conclusion, the GWR model is an alternative approach to addressing spatial non-stationary and scale-dependent problems in geography and ecology.