针对室外无线信道视距(line of sight,LOS)/非视距(non-line of sight,NLOS)传输环境下的车到车(vehicular-to-vehicular,V2V)通信系统,本文提出了一种基于标准街道散射的统计信道模型,其移动发射机(mobile transmitter,MT)与移动接收机...针对室外无线信道视距(line of sight,LOS)/非视距(non-line of sight,NLOS)传输环境下的车到车(vehicular-to-vehicular,V2V)通信系统,本文提出了一种基于标准街道散射的统计信道模型,其移动发射机(mobile transmitter,MT)与移动接收机(mobile receiver,MR)处于运动状态,街道两旁分布的散射体固定.由几何模型出发又引入了一种随机的参考信道模型,其散射体有无穷多个,均以平行于街道两侧的散射条纹形式均匀分布在三维(three dimensional,3D)空间的一个二维(two dimensional,2D)矩形内部.在室外街道通信环境下,模型推导了散射信道中发射角(angle of departure,AOD)以及到达角(angle of arrival,AOA)的概率密度函数(probability density functions,PDFs)解析式;研究了多普勒功率谱密度(power spectral density,PSD)及其时间自相关函数(autocorrelation function,ACF);分析了模型多普勒参数以及街道散射体等因素对V2V通信系统性能的影响.与城市、农村的测量信道对比分析,表明本模型仿真的统计特性符合理论与实际,拓宽了室外V2V无线通信信道建模的研究.为评估室外V2V通信系统的传输特性、仿真无线通信系统提供了有力的研究工具.展开更多
This paper presents a new method of detecting multi-periodicities in a seasonal time series. Conventional methods such as the average power spectrum or the autocorrelation function plot have been used in detecting mul...This paper presents a new method of detecting multi-periodicities in a seasonal time series. Conventional methods such as the average power spectrum or the autocorrelation function plot have been used in detecting multiple periodicities. However, there are numerous cases where those methods either fail, or lead to incorrectly detected periods. This, in turn in applications, produces improper models and results in larger forecasting errors. There is a strong need for a new approach to detecting multi-periodicities. This paper tends to fill this gap by proposing a new method which relies on a mathematical instrument, called the Average Power Function of Noise (APFN) of a time series. APFN has a prominent property that it has a strict local minimum at each period of the time series. This characteristic helps one in detecting periods in time series. Unlike the power spectrum method where it is assumed that the time series is composed of sinusoidal functions of different frequencies, in APFN it is assumed that the time series is periodic, the unique and a much weaker assumption. Therefore, this new instrument is expected to be more powerful in multi-periodicity detection than both the autocorrelation function plot and the average power spectrum. Properties of APFN and applications of the new method in periodicity detection and in forecasting are presented.展开更多
文摘针对室外无线信道视距(line of sight,LOS)/非视距(non-line of sight,NLOS)传输环境下的车到车(vehicular-to-vehicular,V2V)通信系统,本文提出了一种基于标准街道散射的统计信道模型,其移动发射机(mobile transmitter,MT)与移动接收机(mobile receiver,MR)处于运动状态,街道两旁分布的散射体固定.由几何模型出发又引入了一种随机的参考信道模型,其散射体有无穷多个,均以平行于街道两侧的散射条纹形式均匀分布在三维(three dimensional,3D)空间的一个二维(two dimensional,2D)矩形内部.在室外街道通信环境下,模型推导了散射信道中发射角(angle of departure,AOD)以及到达角(angle of arrival,AOA)的概率密度函数(probability density functions,PDFs)解析式;研究了多普勒功率谱密度(power spectral density,PSD)及其时间自相关函数(autocorrelation function,ACF);分析了模型多普勒参数以及街道散射体等因素对V2V通信系统性能的影响.与城市、农村的测量信道对比分析,表明本模型仿真的统计特性符合理论与实际,拓宽了室外V2V无线通信信道建模的研究.为评估室外V2V通信系统的传输特性、仿真无线通信系统提供了有力的研究工具.
文摘This paper presents a new method of detecting multi-periodicities in a seasonal time series. Conventional methods such as the average power spectrum or the autocorrelation function plot have been used in detecting multiple periodicities. However, there are numerous cases where those methods either fail, or lead to incorrectly detected periods. This, in turn in applications, produces improper models and results in larger forecasting errors. There is a strong need for a new approach to detecting multi-periodicities. This paper tends to fill this gap by proposing a new method which relies on a mathematical instrument, called the Average Power Function of Noise (APFN) of a time series. APFN has a prominent property that it has a strict local minimum at each period of the time series. This characteristic helps one in detecting periods in time series. Unlike the power spectrum method where it is assumed that the time series is composed of sinusoidal functions of different frequencies, in APFN it is assumed that the time series is periodic, the unique and a much weaker assumption. Therefore, this new instrument is expected to be more powerful in multi-periodicity detection than both the autocorrelation function plot and the average power spectrum. Properties of APFN and applications of the new method in periodicity detection and in forecasting are presented.
