基于多项式模型的时频二维OFDM信道估计算法

本文提出了一种内插导频的时频二维信道估计算法,用于无线移动信道下的正交频分复用(OFDM)系统。该算法基于梳状内插的导频信号模型,采用多项式模型对信道进行拟合,从而能够精确的估计出信道信息。与时频二维维纳滤波以及自适应判决反馈的算法相比,该算法不需要预先知道信道相关矩阵以及信噪比等信道信息,简单易于实现,并且能够有效的抑制信道估计中的噪声干扰。仿真结果表明,在衰落环境下,该算法的信道估计最小方差(MSE)性能与最小二乘(LS)估计最小方差性能相比有显著提高。     

加权系数平均法改进的小波域LMMSE信道估计算法

针对传统LMMSE算法需要知道信道特性的问题,提出了一种加权系数平均法改进的小波域LMMSE信道估计算法.运用离散小波变换对LS初始估计和预滤波处理后的信号实行阈值量化去噪处理,然后结合时域信道能量分布的稀疏性特征,利用加权系数平均法求出各子载波的频域响应,从而克服了传统LMMSE算法需要预先知晓信道统计特性的缺陷.对算法的BER和MSE性能进行实验仿真,结果表明:文中所提改进算法的信道估计整体性能显然会更优于LS、SVD-LMMSE以及加权平均改进后的LMMSE算法.另外,在信噪比较低且信道统计特性未可知的状况下,文中算法要优于传统的LMMSE算法,并能够较好地降低噪声的影响,有效提升信道估计的精确度.     

MIMO—OFDM信道估计新方法

利用小波变换思想,提出一种基于小波去噪的多输入多输出正交频分复用（MIMO—OFDM）系统信道估计方法,以提高信道估计性能。该方法首先利用最小二乘（LS）方法进行信道估计,然后对估计后的结果进行小波去噪处理。该方法不需要预先知道信道的统计特性,与传统最小二乘信道估计方法相比,性能有明显提高。     

基于子空间信道矩阵迭代的阶数估计算法

针对二阶统计量信道盲辨识算法中有效阶数估计问题,该文提出了一种基于子空间信道矩阵迭代的阶数估计(SS-CMR)算法.基于过估计条件下的Q矩阵特殊结构和其解空间向量可等价为真实信道冲激响应与公共零点信道的卷积,该算法首先利用子空间算法获得估计的信道矩阵,然后构建迭代形式的代价函数进行阶数估计.仿真表明,SS-CMR算法较CMR算法性能提升,且明显优于现有其他阶数估计算法;解析分析表明算法复杂度较CMR算法明显降低,对首尾系数很小的信道的阶数估计尤为有效.     

针对块稀疏信道的估计算法

无线多径信道中存在着块稀疏结构。针对块稀疏信道中分块信息是否已知的不同场景,分别提出了两种基于块稀疏贝叶斯学习(BSBL)框架的OFDM系统信道估计算法。这两种算法根据边界最优(BO)方法估计信道分块的稀疏度参数,提升算法运算速率。为进一步提升信道估计性能,在基于BSBL框架算法仅利用导频信号估计信道的基础上,又提出了基于联合块稀疏贝叶斯学习(JBSBL)的信道估计新算法,该算法利用导频与数据子载波实现信道的联合估计。仿真结果表明,与传统的最小二乘算法比较,本文提出的算法均可获得很好的信道估计性能,且基于JBSBL的信道估计算法性能更佳。      

基于多项式拟合的二维OFDM信道估计算法

该文提出了一种内插导频的二维信道估计算法,用于无线移动信道下的正交频分复用(OFDM)系统。该算法能够适用于矩形内插的导频信号内插方式,相比于梳状内插的导频信号内插方式,矩形内插具有更高的数据传输效率。与已有线性最小均方差(LMMSE)信道估计方法相比,该算法简单并且不需要预先知道信道相关矩阵以及信噪比等信道信息。仿真结果表明,在各种的衰落环境下,该算法都有良好的性能。     

基于快速贝叶斯匹配追踪优化的海上稀疏信道估计方法

正交频分复用(OFDM)系统中,由于频率发生选择性衰落会导致信道在数据传输中产生符号间干扰,因此接收机往往需要知道信道状态信息。而在海上通信的情况下,信道传输会受到多种外界因素的干扰,往往需要预先进行信道探测估计。为了提高估计性能,该文提出一种基于奇异值分解优化观测矩阵的快速贝叶斯匹配追踪稀疏信道估计优化算法(FBMPO),该算法不仅能够充分考虑海上通信的信道稀疏性,也能够降低信道的不确定性带来的影响。计算机仿真实验表明,与传统的信道估计算法相比,该算法能够提高信道估计的精确度。     

一种用于实际OFDM系统的次优信道估计算法

基于对实际系统中信道冲激响应(CIR)泄漏问题的详细分析,提出了一种次优的时域信道估计算法。该算法克服了CIR泄漏的影响,因此能够很好地适应实际系统中的非整数采样多径衰落信道。该算法不需要预先知道信道统计特性,还具有低复杂度的特点。与已有信道估计算法进行了仿真比较,结果证明该算法极大地降低了信道估计MSE值,同时也不存在“地板效应”。     

稀疏表示框架下无需参数配对的二维到达角估计

现有二维到达角估计算法大多基于子空间理论及需要参数配对,针对这一问题,在稀疏表示理论框架下提出了一种参数自动配对的二维到达角估计新算法。该算法在L阵列下构建阵列互相关矩阵的稀疏表示模型,利用奇异值分解降低复杂度并基于群LASSO(Least Absolute Shrinkage and Selection Operator)获得方位角估计。在方位角估计的基础上,基于向量化操作构建稀疏空间谱匹配模型,然后利用LASSO 获得俯仰角估计。与参数配对ESPRIT 和改进的传播算子方法相比,所提算法不仅无需参数配对过程,而且可以提供改进的估计精度。计算机仿真结果验证了所提算法的有效性。     

DS-CDMA系统新的空时并联结构 及二维自适应DBF算法

针对直接序列扩频码分多址(DS-CDMA)移动通信接收系统,本文提出了一种新的空时二维信号处理结构,这是一种"智能天线与瑞克(RAKE)接收机的并联连接"(PI-SA&RR)结构.基于PI-SA&RR结构,提出了空时二维自适应数字波束形成(ST2-DADBF)算法.与"二维RAKE接收机"(2-DRR)相比,ST2-DADBF算法不需要对延时-波达方向(DOA)进行估计就能够获得最优空-时域联合输出.与"导频符号辅助相干自适应天线阵列分集接收机"(PSA-CAAADR)相比,ST2-DADBF算法能够使智能天线和RAKE接收机联合参与自适应算法,不需要对RAKE分支信道参数进行估计,是一种真正的二维自适应算法.针对多用户移动通信系统,同时考虑到信道的时延扩展和空间角度弥散的影响,本文给出了一种接收信号的通用模型,基于该模型对ST2-DADBF算法进行了计算仿真.仿真结果表明,ST2-DADBF算法能够在空时二维域中捕获感兴趣用户的各个多径分量,并将这些多径分量同步相干合成,同时抑制其它用户在时域和空域形成的干扰,因而可获得良好的误码率(BER)性能.     

A comparative investigation on channel estimation algorithms for OFDM in mobile communications

A comparative investigation on various channel estimation algorithms for OFDM system in the mobile communication environment is presented and analyzed in terms of computational complexity, mean square error, and bit error rate in this paper. As a result, Wiener filter estimation shows the best error performance. Concerning the computational complexity as well as the performance, however, the piecewise linear estimator is considered as a proper choice when the reference signal spacing is relatively narrow. And the cubic-spline estimator is a good alternative to the Wiener filter estimation if the reference signal spacing is wider than the coherent bandwidth of transmission channel.     

Direct blind MMSE channel equalization based on second-orderstatistics

A family of new MMSE blind channel equalization algorithms based on second-order statistics are proposed. Instead of estimating the channel impulse response, we directly estimate the cross-correlation function needed in Wiener-Hopf filters. We develop several different schemes to estimate the cross-correlation vector, with which different Wiener filters are derived according to minimum mean square error (MMSE). Unlike many known sub-space methods, these equalization algorithms do not rely on signal and noise subspace separation and are consequently more robust to channel order estimation errors. Their implementation requires no adjustment for either single- or multiple-user systems. They can effectively equalize single-input multiple-output (SIMO) systems and can reduce the multiple-input multiple-output (MIMO) systems into a memoryless signal mixing system for source separation. The implementations of these algorithms on SIMO system are given, and simulation examples are provided to demonstrate their superior performance over some existing algorithms     

OFDM系统中的盲信道估计

本文从OFDM信号的矩阵表示出发,分析比较了OFDM系统中现有的各种盲信道估计方法。OFDM盲信道估计方法分为两类,一类是统计型方法,它利用了发送信号和接收信号的统计特性;另一类是确定型方法,它利用了发送调制信号的固有特性。一般而言,统计型方法的计算量较小,但是估计精度不高且估计的实时性不好;而确定型方法的估计精度较高,实时性较好,但是其计算量较大。计算机仿真表明,这些盲信道估计方法的性能受信道参数尤其是多普勒频率影响很大,盲信道估计的实用化有待进一步研究。     

Tracking of time-varying mobile radio channels .1. The Wiener LMSalgorithm

Adaptation algorithms with constant gains are designed for tracking smoothly time-varying parameters of linear regression models, in particular channel models occurring in mobile radio communications. In a companion paper, an application to channel tracking in the IS-136 TDMA system is discussed. The proposed algorithms are based on two key concepts. First, the design is transformed into a Wiener filtering problem. Second, the parameters are modeled as correlated ARIMA processes with known dynamics. This leads to a new framework for systematic and optimal design of simple adaptation laws based on prior information. The algorithms can be realized as Wiener filters, called learning filters, or as "LMS/Newton" updates complemented by filters that provide predictions or smoothing estimates. The simplest algorithm, named the Wiener LMS, is presented. All parameters are here assumed governed by the same dynamics and the covariance matrix of the regressors is assumed known. The computational complexity is of the same order of magnitude as that of LMS for regressors which are either white or have autoregressive statistics. The tracking performance is, however, substantially improved     

对IEEE 802．16e OFDMA下行链路信道估计方法的仿真研究

IEEE 802．16e下行链路具有特殊的子信道分配方式。针对这种子信道分配方式,一般采用基于簇的二维信道估计算法。文章首先介绍了算法的原理,重点比较了频域采用不同插值算法时的系统BER性能,并分别在低、中、高车速条件下对算法进行仿真分析。仿真结果表明,频域采用线性内插算法时系统性能最好且复杂度较低,同时也表明,基于簇的二维信道估计算法对移动环境有良好的适应性。     

一种改进的LTE下行系统信道估计算法

基于3GPP长期演进（LTE）下行链路系统,在比较了现有几种不同信道估计算法的基础上,提出了一种适用于LTE下行系统的时频二维联合维纳迭代滤波信道估计算法。具体步骤如下：首先,采用基于奇异值分解（SVD）的线性最小均方误差（LMMSE）算法在频域进行维纳迭代滤波;然后,再利用频域估计出来的值在时域进行一次维纳迭代滤波。仿真结果表明,该算法能够有效地改善信道估计的误比特率（BER）性能,与传统的方法相比,性能更加接近于理想信道估计,同时也没有显著增加算法的运算复杂度。     

Unification of MLSE receivers and extension to time-varyingchannels

Forney (1972) and Ungerboeck (1974) have each developed maximum-likelihood sequence estimation (MLSE) receivers for intersymbol interference (ISI) channels. The Forney receiver uses a whitened matched filter, followed by a sequence estimation algorithm using the Euclidean distance metric. The Ungerboeck receiver uses a matched filter, followed by a sequence estimation algorithm using a modified metric. A unified development of both receivers is given, in which each receiver is derived from the other. By deriving the Ungerboeck receiver from the Forney receiver, we show that the whitening operation is cancelled in the Euclidean distance metric, leaving the modified metric. In addition, the Ungerboeck receiver is extended to the case of a time-varying known channel. When the channel is unknown, decision-directed channel estimation is assumed, which requires channel prediction to account for the decision delay. It is shown that the Ungerboeck receiver requires additional channel prediction, degrading performance due to prediction uncertainty. To solve this problem, two alternative receiver forms are developed which do not require additional prediction, though the computational complexity is increased. Performance and complexity of the receiver forms are compared for the IS-136 digital cellular time-division multiple-access (TDMA) standard     

NGB-W广播信道估计实现算法设计

针对下一代广播电视网无线系统(NGB-W)广播接收信道估计,提出了一种基于二次一维维纳滤波的信道估计实用算法.该算法实时估计信道多普勒频偏、时延扩展和噪声方差等参数,并根据参数估计值分别从离线获得的预选集中选择时域和频域的一维维纳滤波系数.通过Cocentric System Studio(CCSS)平台仿真,给出了参数估计对算法性能的影响,以及所提信道估计算法的均方误差和误块率性能.仿真结果表明该算法在不同移动速度下,与采用理想插值系数信道估计相比性能损失不超过0.4 dB,与理想信道估计相比性能损失在1 dB以内.     

基于滤波方法的OFDM信道估计研究

维纳滤波和卡尔曼滤波都是基于最小均方误差准则的滤波方法,本文主要研究这两种滤波方法在OFDM信道估计中的应用。为了跟踪频率选择性信道的变化,采用在OFDM系统中易于实现的梳状导频进行研究。传统的MMSE在统计意义上是最好的线性估计器,但是需要对矩阵求逆,是一种计算量较大,算法较复杂的方法。LMMSE是频域维纳滤波方法,其减小了MMSE的复杂度,但只适用于慢衰落信道,针对时变信道,本文提出卡尔曼滤波的信道估计方法,仿真结果表明,卡尔曼滤波的信道估计方法在时变信道中具有良好的性能。