Transmit Diversity Schemes for WCDMA

Previous work on transmitter antenna diversity has shown that the use of multiple transmitter antennas at the base station results in improved performance due to increase of diversity (path diversity). This happens with no bandwidth cost, even when the signal quality along several paths is poor, but the receiver estimates the channel with accuracy. This paper evaluates the effects of channel estimation errors in the performance of the schemes designated as Space-Time Transmitter Diversity (STTD) and Selective Transmitter Diversity (STD). We consider low to medium signal to noise ratios characteristic of Wideband Code Division Multiple Access (WCDMA) networks. Different fading channel models are considered for comparison between the two schemes. The improvement due to convolutional coding associated with these techniques is also evaluated.     

数字混沌扩频序列的盲估计

混沌序列具有非线性、宽带类噪声、大的码族、任意长的周期且容易产生的特性,因此在扩频通信系统中很有实用价值。本文将文献[1]中对伪随机扩频序列盲估计的方法推广应用到混沌扩频通信中,并对其不足提出了改进方法。在只知道扩频码周期而无其它任何先验知识的条件下,利用特征值分析的方法可以对数字混沌直扩序列进行盲估计,仿真实验证明了该方法的有效性。     

位置参数估计量的损失函数之估计

本文考虑损失函数的估计问题,分别对于球对称分布和均匀分布情形给出了其参数的J-S型估计量的损失之估计,它们满足[1]中提出的条件(Ⅰ)和(Ⅱ).     

Spectral estimation of video signals

Spectrum analyzers are ubiquitous in laboratory work concerning one dimensional signals. This is because linear operators are best examined in the frequency domain. Linear operators, such as linear filters, DCT coders, line shufflers, etc., dominate also the video systems scenario. Their frequency domain study is as appropriate and informative as it is in the case of their one-dimensional counterparts. This paper considers the problems associated with the introduction of two well-known spectral estimation techniques, the periodogram and AR estimates, to the context of television signals. The potential for application of spectral estimation to video problems is exemplified by a number of applications related to the fields of enhanced quality television and HDTV. Special attention is paid to the computational aspects, whose effective solution conditions the practical applicability of the proposed spectral estimation techniques.R. Rinaldo is currently at the Department of Electrical and Computer Engineering of the University of California, Berkeley.     

准连续波体制雷达最大熵方法的算法选择

准连续体制作为一种新型雷达体制以其固有的优越性给雷达界带来了巨大的冲击。其数字信号处理系统的开发是进一步完善该体制雷达的关键。本文针对数字信号处理系统中最大熵谱外推电路的算法选择进行了研究。     

移动运营商渠道管理模式及存在问题分析

渠道作为营销体系中的重要一环，对移动运营商的发展起着举足轻重的作用。随着国内移动市场竞争的加剧，营销渠道的竞争将成为移动运营商间竞争的焦点。目前，国内两大移动运营商在渠道管理上存在诸多问题，从而影响了其市场拓展。分析了移动选营商渠道管理存在的三个较为突出的问题，并提出了可行的解决方法，从而为移动运营商的渠道管理工作提供思路。     

最大似然方法和Bayes方法在结构方程模型分析中的讨论(英文)

本文通过模拟研究,讨论了最大似然方法和Bayes方法在分析结构方程模型中的相似点和不同之处。     

带有块间串扰MC-CDMA的时域信道估计和多用户检测

本文把带有IBI的MC-CDMA系统等效解释为特殊的直接序列码分多址（DS-CDMA）系统，并给出了相应的时域信道估计方法和时域线性最小均方误差（MMSE）多用户检测方法。仿真实验表明了本文方法的性能。     

A new algorithm for fixed design regression and denoising

In this paper, we present a new algorithm to estimate a regression function in a fixed design regression model, by piecewise (standard and trigonometric) polynomials computed with an automatic choice of the knots of the subdivision and of the degrees of the polynomials on each sub-interval. First we give the theoretical background underlying the method: the theoretical performances of our penalized least-squares estimator are based on non-asymptotic evaluations of a mean-square type risk. Then we explain how the algorithm is built and possibly accelerated (to face the case when the number of observations is great), how the penalty term is chosen and why it contains some constants requiring an empirical calibration. Lastly, a comparison with some well-known or recent wavelet methods is made: this brings out that our algorithm behaves in a very competitive way in term of denoising and of compression.     

Covariance estimation under spatial dependence

Correlated multivariate processes have a dependence structure which must be taken into account when estimating the covariance matrix. The natural estimator of the covariance matrix is introduced and is shown that to be biased under the dependence structure. This bias is studied under two different asymptotic models, namely increasing the domain by increasing the number of observations, and increasing the number of observations in the fixed domain. Using the first asymptotic model, we quantify the convergence rate of the bias and of the covariance between the components of the estimated covariance matrix. The second asymptotic model serves to derive a fast and accurate bias correction. As shown, under mild hypotheses, the asymptotic normality of the estimated covariance matrix holds and can be used to test whether the bias is significant, for example, in the sense that the eigenvectors of the estimated and true covariance matrices are significantly different.