CDMA时变色散信道的快速半盲辨识方法

利用CDMA时变色散信道的离散正则模型,给出了一种新的信道快速盲辨识算法,首先使用时-频解相关RAKE接收机消除了多址干扰的影响,然后再利用解相关处理后信号的一阶统计量,实现对信道的辨识。和基于二阶统计量以及高阶统计量的盲辨识算法相比,该算法仅需要少量的导频符号,就可实现对信道的估计,具有更低的计算复杂度和更好的抗噪性能。仿真结果证明了算法的有效性。     

对CDMA时变色散信道基于离散正则模型的盲辨识方法

本文通过将扩展函数在时延扩展域和Ｄｏｐｐｌｅｒ扩展域进行采样，得到了ＣＤＭＡ时变色散信迢的离散正则模型，此模型适宜于采用二阶统计量方法对ＣＤＭＡ时变色散系统进行盲信号处理。文章同时给出了一种对正则采样值的子空间盲辨识算法。与基于基展开模型处理时变信道的方法不同的是，这种基于离散正则模型的盲辨识方法不需借助高阶统计量估计信道的指数基频率，也不必假设相邻符号间的时延变化呈线性关系，而且赋予了基展开坐标明确的物理意义。文章通过仿真验证了算法的有效性。     

利用调制引入循环平稳的CDMA盲信道估计

针对CDMA系统给出了一种新颖的盲辨识方法.它利用了恒模复序列调制产生的循环平稳性,其特色是,在接收机上可以根据循环统计量的支撑的不同,将各个用户的二阶循环统计量分离开来,每个用户形成自己的小规模子空间.采用已成熟的子空间方法,可以识别出不同的用户各自的片率传输信道,以便于均衡和检测.文中给出了自己的信道可辨识性的条件和证明,提出了辨识算法,并对算法进行了模拟.方法简单明了,适合于CDMA上行链路信道.     

通信

Y2002-63328-2517 0303213点对多点通信树通过链锁的网络线路有损速率估计=Estimation of network link loss rates via chaining in mul-ticast trees[会,英]/Ziotopoulos,A.-G.P.& HeroⅢ,A.O.//The 2001 IEEE International Conferenceon Acoustics,Speech,and Signal Processing Vol.ⅣofⅥ.—2517～2520(HE)0303214关于 CDMA 直放站工程建设中的若干建议[刊]/吴杰//移动通信.—2002,26(9).—67～70(C)0303215CDMA 时变色散信道的两级盲辨识算法[刊]/刘郁林//通信学报.—2002,23(9).—74～81(L)本文通过对时变信道的扩展函数在时频域进行正     

一种逆迭代互关系子空间SIMO信道盲辨识方法

基于SIMO系统互关系模型提出了逆迭代辨识时间色散信道子空间方法,基于一阶扰动理论分析了提出方法的MSE性能.为增强算法稳定性,基于扰动理论和泰勒级数展开给出了一种迭代计算协方差CR矩阵逆的方法.理论分析和实验表明:和现有的自适应信道盲辨识方法相比,本文方法提高了信道估计精度,迭代计算CR矩阵逆的方法简单有效,实验结果和扰动分析得到的理论值有很高的一致性.     

一种基于矩阵外积分解的信道盲辨识与盲均衡算法

本文提出了一种鲁棒性较好的信道盲辨识和盲均衡算法.在仅需知道信道阶数上界的条件下,首先采用改进的信道阶数估计算法对信道阶数进行精确估计,与现有算法相比,所需信噪比降低、对信道的适应性提高;然后,在矩阵外积分解算法的基础上,加入特征值扰动分析,提高了信道盲辨识和盲均衡性能.仿真结果证明了算法的有效性.     

基于粒子滤波器的盲辨识和盲均衡新方法

提出了一种新的基于粒子滤波器的盲辨识及盲均衡算法。在对信道进行辨识时，通过对信道均值的采样来代替对真实信道的采样，避免了对信道的后验密度进行采样，从而降低了算法的复杂度。算法还采用先验密度作为重要性函数，以便于对重要性函数进行采样。仿真结果表明，该算法收敛速度快，所需的数据量少，在信噪比较低时也能完成对信道的盲辨识和盲均衡。     

采用奇异值分解方法的半盲信道辨识算法

针对全盲信道辨识算法无法辨识含公零点信道且对信道阶数误差敏感的问题,提出一种半盲信道辨识算法。通过奇异值分解将信道矩阵分解为同维矩阵与酉矩阵乘积的形式,分别利用接收数据和已知符号求解同维矩阵与酉矩阵,最终得到信道矩阵的闭式解。该算法有效地克服了全盲信道辨识算法的诸多局限性,避免了传统半盲方法面临的最优加权选择问题,性能稳定,且对信道噪声与信道阶数都具有较强的鲁棒性。仿真实验验证了所提算法的有效性与优越性。     

基于过采样的六二阶归一化累积量盲均衡算法

现有盲均衡算法大多采用先辨识信道、再进行均衡的方法,但信道阶数的估计是一个难点。将高阶归一化累积量用于盲均衡,提出一种基于过采样的六二阶归一化累积量盲均衡算法。该算法不需要辨识信道和估计信道阶数,只对接收信号的六二阶归一化累积量进行优化。仿真结果验证了算法的有效性。     

基于QR分解的MIMO信道盲辨识和盲均衡方法

针对SIMO信道的经典盲估计方法,如子空间法(SS)等,都是基于接收端样本自相关阵的特征值分解(EVD)或奇异值分解(SVD)来实现信道估计的,而基于QR分解的SIMO信道盲辨识方法是最近提出的一种性能优良的新算法.本文将该算法推广为MIMO信道盲辨识算法,并且证明了在一定的假设下,即使各路源信号为空间相关且其统计特性未知时,该算法仍然保持有效.实验结果表明这种MIMO辨识算法具有收敛速度快、计算量小、无须对噪声做额外的处理、对噪声不敏感等优点.我们还将这种算法与经典的MIMO辨识算法进行了性能比较.     

双循环相关复倒谱的多用户盲信道辨识方法

该文针对多用户系统提出了一种新颖的盲辨识方法.它利用了调制引入的循环平稳性,可以识别出不同的用户不同的传感器(天线)各自的传输信道.利用循环复倒谱的方法可以给出信道的估计,方法简单明了,与现有的方法相比有一定的优势,适合于多用户上行链路信道.文中还给出了信道可辨识性的条件和证明,提出了辨识算法,并对算法进行了模拟.     

Blind identification and equalization based on second-orderstatistics: a time domain approach

A new blind channel identification and equalization method is proposed that exploits the cyclostationarity of oversampled communication signals to achieve identification and equalization of possibly nonminimum phase (multipath) channels without using training signals. Unlike most adaptive blind equalization methods for which the convergence properties are often problematic, the channel estimation algorithm proposed here is asymptotically ex-set. Moreover, since it is based on second-order statistics, the new approach may achieve equalization with fewer symbols than most techniques based only on higher-order statistics. Simulations have demonstrated promising performance of the proposed algorithm for the blind equalization of a three-ray multipath channel     

Recursive blind identification of non-Gaussian time-varying ARmodel and application to blind equalisation of time-varying channel

A novel method for the blind identification of a non-Gaussian time-varying autoregressive model is presented. By approximating the non-Gaussian probability density function of the model driving noise sequence with a Gaussian-mixture density, a pseudo maximum-likelihood estimation algorithm is proposed for model parameter estimation. The real model identification is then converted to a recursive least squares estimation of the model time-varying parameters and an inference of the Gaussian-mixture parameters, so that the entire identification algorithm can be recursively performed. As an important application, the proposed algorithm is applied to the problem of blind equalisation of a time-varying AR communication channel online. Simulation results show that the new blind equalisation algorithm can achieve accurate channel estimation and input symbol recovery     

一种基于IIR信道输出过采样的SIMO模型

通过对IIR信道输出的过采样(Oversampling),该文提出一种新的单输入多输出(Single-Input- Multiple-Output,SIMO)模型。较现有模型大大简化,可作为IIR信道盲辨识研究的依据。该文同时分析了采样率和SIMO模型子信道数的关系,并用计算机仿真给出他们对辨识效果的影响,可作为选择采样率和子信道数的参考。     

Precoder design and blind identification of MIMO channels

In this paper, blind identification problem based on second-order statistics (SOS) of received signals is investigated for multiple-input multiple-output (MIMO) channels. It is shown that precoders introduced in transmitters can provide certain extra information for blind identification problem and commonly used identifiability conditions on MIMO channels can be relaxed. A design criterion for the precoders in frequency domain is proposed. Two blind identification algorithms based on the knowledge of the precoders and SOS of the received signals are then developed. In these algorithms, due to particular structure of the precoders, the blind identification problem for a MIMO channel is decomposed into several independent single-input multiple-output (SIMO) channel identification problems. The presented algorithms are robust to an overestimated channel order and not sensitive to signal-to-noise ratio (SNR). Compared with the existing results, a larger class of MIMO finite-impulse-response (FIR) channels can be identified by the new methods.     

Subspace-Based Blind Channel Identification for Cyclic Prefix Systems Using Few Received Blocks

In this paper, a novel generalization of subspace-based blind channel identification methods in cyclic prefix (CP) systems is proposed. For the generalization, a new system parameter called repetition index is introduced whose value is unity for previously reported special cases. By choosing a repetition index larger than unity, the number of received blocks needed for blind identification is significantly reduced compared to all previously reported methods. This feature makes the method more realistic especially in wireless environments where the channel state is usually fast-varying. Given the number of received blocks available, the minimum value of repetition index is derived. Theoretical limit allows the proposed method to perform blind identification using only three received blocks in absence of noise. In practice, the number of received blocks needed to yield a satisfactory bit-error-rate (BER) performance is usually on the order of half the block size. Simulation results not only demonstrate the capability of the algorithm to perform blind identification using fewer received blocks, but also show that in some cases system performance can be improved by choosing a repetition index larger than needed. Simulation of the proposed method over time-varying channels clearly demonstrates the improvement over previously reported methods.     

基于卡尔曼滤波的同步盲系统辨识与解卷积方法

在盲信道均衡或盲语音去混响应用中,盲多信道系统辨识通常是信号解卷积的前提条件,即盲辨识过程后跟一个解卷积过程。本文提出一种基于卡尔曼滤波的同步盲系统辨识与解卷积方法,其中卡尔曼滤波的状态矢量由多信道系统参数与源信号矢量组成,过程方程和测量方程则建立在单输入-多输出系统(SIMO)的输入输出关系及信道间交叉关联关系(Cross Relation)基础上。此外,盲系统辨识部分与解卷积部分是可以解耦的,生成两个看似独立的卡尔曼滤波问题,并且这两个卡尔曼滤波问题可以实现并行计算。与级联结构相比,这种并行结构更有利于算法优化和实时信号处理。仿真表明,对于无噪声理想信号模型,本算法可以实现完全系统辨识和解卷积(信号误差比可达到100 dB以上),说明理论正确;对于实测的混响语音信号亦可以实现一定的去混响效果。      

一种基于信道盲辨识和盲均衡的多径信道调制方式识别算法

针对多径环境下MPSK和MQAM信号的调制识别问题,该文提出了一种基于信道盲辨识和盲均衡的调制识别算法。算法首先进行信道阶数精确估计,然后以矩阵外积分解为基础,加入特征值扰动分析,提高了信道盲辨识和盲均衡的稳定性,最后,对均衡后的星座图进行多级减法聚类,得到信号的调制方式。与现有算法相比,算法结构简单,所需数据量小,适用范围广,实用性强。仿真结果证明了算法的有效性。     

EM-based blind LDPC identification in multipath channels

As the advent of cognitive radios,blind encoder identification has attracted increasingly attentions since it plays an important role.The existing works mainly focus on additive white Gaussian noise (AWGN) channel,while the blind identification in multipath scenarios has not been sufficiently investigated.Considering the blind low density parity-check (LDPC) codes identification in the presence of unknown multipath fading channel,a likelihood-based classifier was proposed using the expectation maximization (EM) algorithm to obtain the maximum likelihood estimates of the unknown parameters.Then,an average log-likelihood ratio (LLR) estimator was adopted to classify the unknown encoder.Numerical results show that the proposed algorithm provides promising identification performance in multipath channels,especially in the low signal-to-noise ratio region.     

光盘预刻槽形测量的扫描隧道显微技术

本文报道用自行设计的扫描隧道显微镜对光盘预刻槽进行测量。为观察大范围的结构,我们设计了一个新的大范围扫描头(～6μm),并成功地观察到预刻的槽形,给出了光盘预刻槽的灰度图像、三维轮廓像及剖面轮廓。为评价预刻的槽形提出了一种有效的测量手段。