FIR channel estimation through generalized cumulant slice weighting

We study the problem of linear channel estimation with unknown channel input signals. Our work stems from the weighted slice algorithm, in which cumulant slices of different orders are linearly combined to produce an estimate of a single-input single-output channel. Aiming to improve the reliability of estimation, the new approach incorporates a certain matrix structure constraint into its weight computation criterion. It provides more reliable results for channels with weak leading coefficients. This generalized algorithm maintains the advantage of Fonollosa and Vidal's algorithm in handling the situation with channel order overestimation. Its natural extension to multiple-input multiple-output channel estimation is presented along with the channel identifiability conditions required.     

A cumulant matrix subspace algorithm for blind single FIR channelidentification

Blind identification of discrete-time single-user FIR channels with nonminimum phase is studied here. Exploiting higher order cumulants of output signals of unknown channels, a new closed-form solution to the FIR channel impulse response is derived. The algorithm is simple and fast. It relies only on nullspace decomposition of some cumulant matrices. This method neither involves the difficult task of iterative global minimization of nonunimodal cost functions, nor does it require overparametrization, which poses consistency difficulties. It can be used either as the final channel estimate or as a good initial point in nonlinear cumulant matching techniques. The application of this identification method is broad and not limited to the use of any fixed-order cumulants. Its application in identifying data communication systems shows great potential and promise     

Robust blind channel estimation algorithm for linear STBC systems using fourth order cumulant matrices

A novel blind channel estimation algorithm, based on fourth-order cumulant matrices, is proposed and applied to linear Space–Time Block Coded (STBC) for Multiple Input Multiple Output systems. Contrary to subspace and Second-Order Statistics (SOS) methods, the presented approach estimates the channel matrix without any modification of the transmitter. It takes advantage of the statistical independence of the signals in front of the space–time encoding. In this paper, the presented algorithm estimates the channel matrix by minimizing a cost function based on the higher cumulant matrices after Zero-Forcing equalization to mitigate the computational complexity and improve the performance. We employ the proposed method to the STBC systems including Spatial Multiplexing, Orthogonal, quasi-Orthogonal and Non-Orthogonal STBC systems. Symbol error rate and Normalized Mean Square Error simulations of the proposed algorithm are shown for a different number of users, signal to noise ratios and different number of symbols per user in comparison with subspace and Second-Order Statistics (SOS) methods. The results show that the presented method performs well and outperforms other methods in estimating the channel matrix from the received data. Moreover, the proposed method presents high convergence speed in estimating the channel matrix.     

Wavelet parameter and phase estimation using cumulant slices

A simple, closed-form expression relates one-dimensional output cumulant statistics with the parameters of a known-order moving-average wavelet. Based on this relationship the author obtains unique parameter and phase estimates of autoregressive moving-average seismic wavelets. The input reflectivity sequence is assumed to be non-Gaussian, independent, and identically distributed. The wavelet is not assumed to be minimum phase and is allowed to include all-pass factors. The seismogram is contaminated by additive-colored Gaussian noise. Simulations demonstrate that the algorithm works well for moderate-size data records with a relatively low signal-to-noise ratio     

Blind estimation of a fractionally sampled FIR channel for OFDM transmission using residue polynomials

This paper introduces blind-channel estimation methods using residue polynomials for orthogonal-frequency-division-multiplexing (OFDM) transmission under the assumption that the channel is finite-impulse response (FIR). In terms of z transform, if the received signal is multiplied by the inverse of the transmitted signal, the resulting z transform renders the channel transfer function when additive noise is absent in the channel. For an FIR channel, samples of the recovered impulse response must be zero in the region of zeros of the channel impulse response. Based on this observation, the blind estimation problem is formulated as a solution of linear equations, treating the transmitted symbols as unknown variables. Polynomial residue arithmetic turns out to be very useful for deriving the linear equations. The proposed method is computationally more efficient than subspace methods that are applied for OFDM transmission systems. In addition, unlike subspace methods, the proposed method is deterministic and does not require estimation of the autocorrelation matrix of received signals, which is required in subspace methods.     

Joint optimization of FIR prefilter and channel estimate for sequence estimation

We provide simple analytical results for the coefficients of a finite-impulse response (FIR) prefilter and the effective channel impulse response (IR) for use in cellular communication systems. We show that using a FIR filter with both causal and anticausal filter taps, it is possible to find the jointly optimized impulse response, such that the signal-to-noise ratio is maximized in the least-squares sense. We show via computer simulation for 8-ary phase-shift keying in Enhanced Data rates for Global Evolution (EDGE) that the joint optimization of the prefilter and IR produces results similar to the minimum mean-square error decision-feedback equalizer prefilter in thermal noise, but yields gain in colored noise.     

MA parameter estimation and cumulant enhancement

This paper addresses the problem of estimating the parameters of a moving average (MA) model from either only third- or fourth-order cumulants of the noisy observations of the system output. The system is driven by an independent and identically distributed non-Gaussian sequence that is not observed. The unknown model parameters are obtained using a batch least squares method. Recursive methods are also developed and used to claim the uniqueness of the batch least squares solutions. A novel technique for the enhancement of third-order cumulants of MA processes is introduced. This new technique is based on the concept of composite property mappings and helps reduce the variance of the estimates of third- (or fourth)-order cumulants of MA processes. Simulation results are presented that demonstrate the performance of the new methods and compare them with a range of existing techniques     

Block FIR decision-feedback equalizers for filterbank precodedtransmissions with blind channel estimation capabilities

In block transmission systems, transmitter-induced redundancy using finite-impulse response (FIR) filterbanks can be used to suppress intersymbol interference and equalize FIR channels irrespective of channel zeros. At the receiver end, linear or decision-feedback (DF) FIR filterbanks can be applied to recover the transmitted data. Closed-form expressions are derived for the FIR linear or DF filterbank receivers corresponding to varying amounts of transmission redundancy. Our framework encompasses existing block transmission schemes and offers low implementation-cost equalization techniques both when interblock interference is eliminated, and when IBI is present as, e.g., in orthogonal frequency-division multiplexing with insufficient cyclic prefix. By applying blind channel estimation methods, our filterbank transmitters-receivers (transceivers) dispense with bandwidth consuming training sequences. Extensive simulations illustrate the merits of our designs     

基于叠加训练序列快速信道辨识方法

信道状态信息(CSI)对于无线通信来说至关重要,而叠加训练序列的信道估计方法由于不占用额外的信号带宽和具有较高的估计精度而受到人们的注意.本文从算法的复杂度入手,分析了不同的训练序列对算法复杂度的影响,并且在训练序列为PN序列下,利用循环Toeplitz矩阵的特性,得到了信道估计的快速算法.仿真实验的结果表明,这种算法不需要矩阵求逆、计算量小,估计精度高,有很大的实际应用前景.     

基于训练序列的MIMO信道估计

提出了一种基于隐训练序列的多输入多输出通信系统的信道估计方法,利用训练序列与信息序列的不相关特性,在没有带宽损失的情况下估计出信道参数。给出了信道估计算法,并对估计的均方误差性能进行了分析,讨论了隐训练序列的优化方案。与以往的隐训练序列估计方法比较,本算法不受接收端直流偏移的限制,且有更低的估计均方误差。计算机仿真结果表明了该估计方法的有效性。     

Blind channel estimation using fractional sampling

We consider the problem of blind estimation of a communication channel based on the oversampled channel output. We propose a nonparametric approach that, based on the cyclic spectrum of the output, finds the channel phase response without neither the need of phase unwrapping nor channel length information. For band-limited channels, the cyclic spectrum has limited support. For this case, we propose an approximation for the discretized phase of the cyclic spectrum that, under certain conditions, results in a simpler channel estimation method. The proposed approach is applied to simulated data and real recordings and is compared to existing methods.     

应用加权高斯模型的非理想稀疏信道估计

在高速通信系统中,由于多径信道通常存在一些小的散射体,使得抽头向量不满足理想的稀疏特性,导致经典的稀疏估计算法存在一定的性能损失.针对上述非理想稀疏特性问题,提出了一种基于酉变换近似消息传递(Unitary Transform Approximate Message Passing,UT-AMP)和加权高斯(Weigh...     

Fast DOA estimation algorithm using pseudocovariance matrix

This paper proposes a new direction of arrival (DOA) estimation algorithm that can rapidly estimate the DOAs of incidence signals using a pseudocovariance matrix even under coherent interference environments. The conventional multiple signal classification (MUSIC) algorithm, which should estimate a covariance matrix, cannot perform a DOA estimation until it acquires the covariance matrix. In addition, the MUSIC algorithm cannot be used under rapidly changing or correlated interference environments. In contrast, the proposed algorithm can obtain a bearing response after acquiring the pseudocovariance matrix based on a single snapshot. Signal incidence angles can then be accurately estimated by combining the bearing response and the location of pattern s. Accordingly, the proposed algorithm can rapidly estimate the DOAs of signals even when they are correlated.     

基于伪协方差矩阵的频率和角度联合估计算法

提出了一种计算简单并且具有很好统计性能的色噪声环境下频率和角度联合估计算法。假设噪声的时间相关长度小于信号的时间相关长度来消除噪声的影响，而后利用足够的时间相关来构成伪协方差矩阵，并对其进行奇异值分解获得信号频率，最后利用一一对应的一维搜索方法获得角度估计值。最后数值仿真结果证实了所提方法不仅由于阵列的扩展效应而使得角度估计性能的优越，而且在很小计算负担的条件下频率估计也给出较好的性能。     

四阶累积量稀疏表示的DOA估计方法

针对现有稀疏重构DOA估计算法不能抑制噪声项以及在高斯色噪声背景下不再适用的问题,本文提出了基于四阶累积量稀疏重构的DOA估计方法。首先,利用接收数据的四阶累积量构建了稀疏表示模型,该模型抑制了噪声项;其次对四阶累计量矩阵进行奇异值分解,化简了稀疏表示模型,通过奇异值分解,不仅减小了数据规模,而且进一步抑制了噪声。对于稀疏表示模型的求解,先利用信号子空间与噪声子空间的正交特性选取权值矢量,然后利用加权l1范数法对模型求解实现DOA估计。理论分析和仿真实验表明本文算法在高斯白噪声和色噪声背景下均适用;能够处理非相干和相干信号,且在低信噪比条件下,对相干信号有更高的估计精度;较之同类的稀疏重构算法,本文算法具有较低的算法复杂度和更高的角度分辨力。      

基于自适应投影矩阵的压缩感知超宽带信道估计

超宽带信号由于功率谱密度较低和传输多径复杂,准确的信道估计十分重要。考虑其过高带宽带来的采样难度较高的问题,压缩感知理论提供了一种可行的低速采样方法。而目前常用的随机投影矩阵与超宽带信道稀疏变换矩阵相关度较高,算法必须在降维比较高时才能达到重构要求,采样速率依然较高。针对上述问题,提出使用贝叶斯压缩感知理论中的自适应投影矩阵设计方法进行超宽带信道估计。贝叶斯压缩感知理论给信道向量中的每个值设置受超参数控制的后验概率密度,计算信道向量的统计特性,并根据协方差矩阵计算新的投影向量,该投影向量可以使重构解的微分熵下降最快。通过这种自适应的投影矩阵设计方法,可以利用较少的采样值进一步地提高重构解的可信度,达到进一步降低采样速率的目的。实验结果表明该方法相对于现有的压缩感知重构算法可以在较低的降维比条件下达到较好的重构效果,显著降低了采样速率。     

The fundamental subspace sequences of matrix pencils: A toeplitz matrix unified characterization

The geometric theory of the domain of an ordered pair (F, G) of matrices or the geometry associated with matrix pencils provides a unifying framework for the study of algebraic, dynamic and feedback properties of linear singular systems. The key concepts and tools of the geometry are the notions of the (F, G)-, (G, F)-invariance and a set of subspace sequences. In this paper, an alternative characterization of these sequences is given based on the properties of the partitioned null spaces of appropriate sequences of Toeplitz matrices defined by the (F, G) pair. The results provide a simple procedure for the computation of the limit spaces of these sequences and clearly cover corresponding problems of the singular, implicit systems theory.     

MIMO channel estimation method using ray-tracing propagation model

An analytical method, which estimates MIMO channel characteristics using a 3D ray-tracing propagation model, is presented. This method, based on EM (electromagnetic) theory, is a deterministic one without any measurements and Monte-Carlo simulations, and so shows high efficiency for time and cost.     

利用训练序列进行无线局域网信道估计

研究了基于训练序列的无线局域网信道估计的几种方法，通过Matlab算法仿真分析，总结出适用IEEE802．1la的信道估计方法。并指出了各种算法的优缺点及适用条件，有利于无线局域网信道估计与均衡的硬件实现。     

DMBT系统中利用FWT快速实现信道估计

根据PN码的结构特性,针对DMB-T系统多载波模式,本文提出了一种新的理论上与滑窗相关法等价的信道估计方法,利用FWT(快速沃尔什变换)实现快速多径捕获.通过干扰抵消进一步消除了径间干扰.该方法具有很强的抗多径效应和抗强窄带干扰的能力,将运算复杂度从传统滑动互相关法的N~2降低到(N+1)log_2(N+1).