OFDM第四代移动通信技术理论的简要论述

OFDM即正交频分复用技术,多载波调制的一种.其主要思想是:将信道分成若干正交子信道,将高速数据信号转换成并行的低速子数据流,调制到在每个子信道上进行传输.正交信号可以通过在接收端采用相关技术来分开,这样可以减少子信道之间的相互干扰ICI.每个子信道上的信号带宽小于信道的相关带宽,因此每个子信道上的可以看成平坦性衰落,从而可以消除符号闻干扰.而且由于每个子信道的带宽仅仅是原信道带宽的一小部分,信道均衡变得相对容易.在向B3G/4G演进的过程中,OFDM是关键的技术之一,可以结合分集,时空编码,干扰和信道间干扰抑制以及智能天线技术,最大限度的提高了系统性能.本文从OFDM技术基本原理,关键技术,优缺点等方面对OFDM技术进行了阐述与分析.     

量化误差对信道估计的影响

针对提高无线信道的传输性能,提出了改进的基于叠加训练序列的MIMO信道估计算法。利用训练序列与信息序列的不相关,在没有带宽损失的情况下初步估计出信道参数;然后利用加权因子对信道参数在相邻信号段之间进行加权平均,得到最终的信道参数。与以往的叠加训练序列估计方法比较,利用算法具有更低的估计均方误差,且适用于时变信道。通过计算机仿真结果表明,估计方法提高了信道传输精度和性能。     

一种降低OFDM系统峰均比改进的SLM算法

选择性映射(SLM)算法是一种无失真降低OFDM系统峰均比(PAPR)的技术,但经典SLM算法的峰均比降低性能不甚明显。文章提出了一种改进的SLM算法,ISLM算法。该算法有效地利用了IFFT的线性性质,大大降低了IFFT的运算次数,降低了系统的复杂度。仿真结果表明,该算法可以有效地改进SLM算法的性能,大大降低OFDM系统的峰均比。     

关于OFDM系统中抗多普勒频移的研究

在OFDM （Orthogonal Freq uency Division Multiplex-ing，即正交频分多路复用）系统中，由于无线信道存在时变性，在传输过程中出现无线信号的频率偏移，最为明显的就是多普勒频移，或者由于发射机载波频率与接收机本地振荡器之间存在频率偏差，都会使OFDM系统子载波之间的正交性遭到破坏，从而导致子载波间信号的相互干扰（Inter-Carrier Interference ICI），因而研究OFDM系统下的多普勒问题势在必行。     

OFDM旋转调制技术探析

正交频分复用(OFDM)是一种特殊的多载波传输方案,它既是一种调制技术,也是一种复用技术.OFDM技术具有许多优点,是下一代移动通信系统的主流调制和复用技术.文章介绍了OFDM的基本原理,说明采用旋转调制的OFDM技术对通信性能的改善,并与传统的调制方式进行比较.     

OFDM旋转调制技术探析

[摘要]正交频分复用（OFDM）是一种特殊的多载波传输方案,它既是一种调制技术,也是一种复用技术。OFDM技术具有许多优点,是下一代移动通信系统的主流调制和复用技术。文章介绍了OFDM的基本原理,说明采用旋转调制的OFDM技术对通信性能的改善,并与传统的调制方式进行比较。     

同步误差对OFDM系统的影响

OFDM系统相对于单载波系统来说,同步技术尤为关键。由于OFDM系统各个子载波间的正交性,使得OFDM系统对频率偏移非常敏感,为了保证系统性能的良好,我们要保证频率的稳定性。本文分析了同步误差对OFDM系统性能的影响,给出了一种新算法,仿真表明OFDM系统频率同步误差比时间同步误差对系统性能的影响要严重得多,只有当OFDM系统频偏小于0.02个子载波间隔时,系统才是处于稳定性能的,这时它所产生的ICI对系统的影响可以忽略不计。     

OFDM旋转调制技术探析

[摘要]正交频分复用(OFDM)是一种特殊的多载波传输方案,它既是一种调制技术,也是一种复用技术。OFDM技术具有许多优点,是下一代移动通信系统的主流调制和复用技术。文章介绍了OFDM的基本原理,说明采用旋转调制的OFDM技术对通信性能的改善,并与传统的调制方式进行比较。     

MIMO-OFDM信道估计新方法

近几年来,无线通信技术正发生着巨大的变化,无线传播的独特功能如反射,折射等,包括多径衰落,路径损耗的结果和多普勒频移,使无线通信具有高速的信息传递功能。此外无线通信信道由于不同的结构,在互联网中由于数据包的损失而引起的网络拥塞已不再只是无线环境下的问题了。OFDM和MIMO两种技术的结合使得多用户在无线信道中传输成为可能,本文通过对比两种不同信道估计技术,改善了无线信道估计的性能。     

多业务电力线通信 OFDM 系统下行资源分配

本文在探讨了单用户 OFDM 电力线资源分配的基础上,提出了一种多业务多用户在电力线信道动态 OFDM 系统资源分配方案.在功率受限条件下最大化系统速率.同时满足不同业务用户最低速率要求.     

普赖斯文献增长模型中参数估计方法的再讨论

本文分析了普赖斯文献增长模型中参数的意义,并分析了其他作者提出的参数的三个估计方法中存在的缺陷,给出了改进了的估计方法。实例表明改进后的方法更具合理性,且计算结果是满意的。     

Structural equation modeling with ordinal variables: a large sample case study

In the behavioral sciences, response variables are often non-continuous, ordinal variables. Conventional structural equation models (SEMs) have been generalized to accommodate ordinal responses. In this study, three different estimation methods on real data were performed with ordinal variables. Empirical results obtained from the different estimation methods on given real large sample educational data were investigated and compared to recent simulation results. As a result, even very large sample is available, model estimations and fits for ordinal data are affected from inconvenient estimation methods thus it is concluded that asymptotically distribution free estimation method specialized for ordinal variables is more convenient way to model ordinal variables.     

Interval estimation for the two-parameter exponential distribution under progressive censoring

The interval estimation of the scale parameter and the joint confidence region of the parameters of two-parameter exponential distribution under Type II progressive censoring is proposed. In addition, the simulation study for the performance of all proposed pivotal quantities is done in this paper. The criteria of minimum confidence length and minimum confidence area are used to obtain the optimal estimation. The predictive intervals of the future observation and the future interarrival times based on the Type II progressive censored sample are also provided. One biometrical example is also given to illustrate the proposed methods.     

The use of studentized diagnostics in regression

Recent work by LaMotte (1999) uncovered redundancies and inconsistencies in the current practice of selected deletion diagnostics in regression. The present study extends earlier work to include further diagnostics on using different methods. Benchmarks adjusted to the scale of each diagnostic are given to assure consistency across diagnostics. Case studies illustrate anomalies in the use of these diagnostics as currently practiced. Alternative diagnostics are given to gauge effects of single-case deletions on variances and biases in prediction and estimation. Received: November 1999     

Serial correlation in latent discrete variable models

We consider the problems of estimation and testing in models with serially correlated discrete latent variables. A particular case of this is the time series regression model in which a discrete explanatory variable is measured with error. Test statistics are derived for detecting serial correlation in such a model. We then show that the likelihood function can be evaluated by a recurrence relation, and thus maximum likelihood estimation is computationally feasible. An illustrative example of these methods is given, followed by a brief discussion of their applicability to a Markov model of switching regressions.     

On finite sample properties of alternative estimators of coefficients in a structural equation with many instruments

We compare four different estimation methods for the coefficients of a linear structural equation with instrumental variables. As the classical methods we consider the limited information maximum likelihood (LIML) estimator and the two-stage least squares (TSLS) estimator, and as the semi-parametric estimation methods we consider the maximum empirical likelihood (MEL) estimator and the generalized method of moments (GMM) (or the estimating equation) estimator. Tables and figures of the distribution functions of four estimators are given for enough values of the parameters to cover most linear models of interest and we include some heteroscedastic cases and nonlinear cases. We have found that the LIML estimator has good performance in terms of the bounded loss functions and probabilities when the number of instruments is large, that is, the micro-econometric models with “many instruments” in the terminology of recent econometric literature.     

Schools and public buildings in decay: the role of political fragmentation

It is a widespread concern that schools and other public buildings are in poor conditions. A popular explanation is that maintenance is given too little priority in the budgetary process because politicians are shortsighted. In this paper we investigate this hypothesis using two novel survey data sets on school and general building conditions in Norwegian local governments. We use political fragmentation as a proxy for myopic behavior and provide strong empirical evidence that a high degree of political fragmentation is associated with poor building conditions, both for schools and for buildings in general. The finding is robust to handling of controls, outliers, and estimation method. We also provide evidence that lack of maintenance is the channel for poor building conditions.     

Interval Estimation for the Two-Parameter Exponential Distribution Based on the Doubly Type II Censored Sample

The interval estimation of the scale parameter and the joint confidence region of the parameters of two-parameter exponential distribution under doubly Type II censoring is proposed. In addition, the simulation study for the performance of our methods is done in this paper. One biometrical example is also given to illustrate the proposed methods.     

A Review of Nonparametric Time Series Analysis

Various features of a given time series may be analyzed by nonparametric techniques. Generally the characteristic of interest is allowed to have a general form which is approximated increasingly precisely when the sample size goes to infinity. We review nonparametric methods of this type for estimating the spectral density, the conditional mean, higher order conditional moments or conditional densities. Moreover, density estimation with correlated data, bootstrap methods for time series and nonparametric trend analysis are described.