Joint order detection and blind channel estimation by least squaressmoothing

A joint order detection and blind estimation algorithm for single input multiple output channels is proposed. By exploiting the isomorphic relation between the channel input and output subspaces, it is shown that the channel order and channel impulse response are uniquely determined by finite least squares smoothing error sequences in the absence of noise. The proposed subspace algorithm is shown to have marked improvement over existing algorithms in performance and robustness in simulations     

Recursive total least squares algorithm for single-user blindchannel equalisation

The problem of blind channel identification/equalisation using second-order statistics or equivalent deterministic properties of the oversampled channel output has attracted considerable attention. Deterministic blind subspace algorithms are particularly attractive because of their finite sample convergence property and because their solution can be obtained in closed form. Most subspace algorithms developed up until now, however, are based on block processing and have high computational and memory requirements. In the paper, adaptive techniques are used to lower the computational burden. A single-user direct symbol estimation algorithm is presented. The first step in the algorithm consists of an adaptive matrix singular value decomposition for a (virtual) channel identification-type operation. A recursive total least squares algorithm is then used to recover the input symbols. The algorithm is able to track time-varying channels     

MIMO系统中应用张量分解进行半盲信道估计的算法分析

针对多输入多输出系统半盲信道估计问题,提出一种基于张量分解的半盲联合信号检测和信道估计算法。其思想是利用张量分解的唯一性,对接收信号构造基于张量分解的平行因子模型,并利用正则交替最小二乘算法对信道和发送信号进行联合迭代估计。仿真结果表明:与传统基于导频信道估计方法相比,所提算法只需少量的导频序列即可获得较高的信道估计精度;与已有的交替最小二乘算法相比,所提算法消除了矩阵求伪逆时可能带来的病态问题,收敛速度较快。文章还详细的分析了正则系数和收敛条件等参数对正则交替最小二乘算法性能的影响。      

A globally convergent approach for blind MIMO adaptivedeconvolution

We discuss the blind deconvolution of multiple input/multiple output (MIMO) linear convolutional mixtures and propose a set of hierarchical criteria motivated by the maximum entropy principle. The proposed criteria are based on the constant-modulus (CM) criterion in order to guarantee that all minima achieve perfectly restoration of different sources. The approach is moreover robust to errors in channel order estimation. Practical implementation is addressed by a stochastic adaptive algorithm with a low computational cost. Complete convergence proofs, based on the characterization of all extrema, are provided. The efficiency of the proposed method is illustrated by numerical simulations     

A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis

This paper studies the problem of robust adaptive filtering in impulsive noise environment using a recursive least M-estimate algorithm (RLM). The RLM algorithm minimizes a robust M-estimator-based cost function instead of the conventional mean square error function (MSE). Previous work has showed that the RLM algorithm offers improved robustness to impulses over conventional recursive least squares (RLS) algorithm. In this paper, the mean and mean square convergence behaviors of the RLM algorithm under the contaminated Gaussian impulsive noise model is analyzed. A lattice structure-based fast RLM algorithm, called the Huber Prior Error Feedback-Least Squares Lattice (H-PEF-LSL) algorithm is derived. Part of the H-PEF-LSL algorithm was presented in ICASSP 2001. It has an order O(N) arithmetic complexity, where N is the length of the adaptive filter, and can be viewed as a fast implementation of the RLM algorithm based on the modified Huber M-estimate function and the conventional PEF-LSL adaptive filtering algorithm. Simulation results show that the transversal RLM and the H-PEF-LSL algorithms have better performance than the conventional RLS and other RLS-like robust adaptive algorithms tested when the desired and input signals are corrupted by impulsive noise. Furthermore, the theoretical and simulation results on the convergence behaviors agree very well with each other.     

Adaptive filtering in subbands using a weighted criterion

Transform-domain adaptive algorithms have been proposed to reduce the eigenvalue spread of the matrix governing their convergence, thus improving the convergence rate. However, a classical problem arises from the conflicting requirements between algorithm improvement requiring rather long transforms and the need to keep the input/output delay as small as possible, thus imposing short transforms. This dilemma has been alleviated by the so-called “short-block transform domain algorithms” but is still apparent. This paper proposes an adaptive algorithm compatible with the use of rectangular orthogonal transforms (e.g., critically subsampled, lossless, perfect reconstruction filter banks), thus allowing better tradeoffs between algorithm improvement, arithmetic complexity, and input/output delay. The method proposed makes a direct connection between the minimization of a specific weighted least squares criterion and the convergence rate of the corresponding stochastic gradient algorithm. This method leads to improvements in the convergence rate compared with both LMS and classical frequency domain algorithms     

A blind multichannel identification algorithm robust to orderoverestimation

Active research in blind single input multiple output (SIMO) channel identification has led to a variety of second-order statistics-based algorithms, particularly the subspace (SS) and the linear prediction (LP) approaches. The SS algorithm shows good performance when the channel output is corrupted by noise and available for a finite time duration. However, its performance is subject to exact knowledge of the channel order, which is not guaranteed by current order detection techniques. On the other hand, the linear prediction algorithm is sensitive to observation noise, whereas its robustness to channel order overestimation is not always verified when the channel statistics are estimated. We propose a new second-order statistics-based blind channel identification algorithm that is truly robust to channel order overestimation, i.e., it is able to accurately estimate the channel impulse response from a finite number of noisy channel measurements when the assumed order is arbitrarily greater than the exact channel order. Another interesting feature is that the identification performance can be enhanced by increasing a certain smoothing factor. Moreover, the proposed algorithm proves to clearly outperform the LP algorithm. These facts are justified theoretically and verified through simulations     

基于均衡代价函数的信道阶数盲估计算法

针对信道阶数估计问题,利用单输入多输出(Single-Input Multiple-Output,SIMO)有限冲激响应(Finite Impulse Response,FIR)信道的结构特点和输入/输出信号的统计特征,提出了一种基于均衡代价函数的信道阶数盲估计算法.首先计算了归一化最小二乘均衡(Normalized Least Squares Equalization,NLSE)代价函数在理想条件下的理论渐近值,并指出其拐点与信道阶数之间的对应关系.然后分析了NLSE代价函数在实际条件下的近似值.最后引入了拐点优化因子,提出了一种基于NLSE代价函数拐点检测的信道阶数估计算法.理论分析和仿真结果表明,在信噪比(Signal-to-Noise Ratio,SNR)较低和信道首尾系数较小的情况下,该算法比现有其它方法具有更强的鲁棒性,可以获得更小的接收信号均衡误差.     

A novel variable step size adjustment method based on channel output autocorrelation for the LMS training algorithm

The least mean squares (LMS) algorithm, the most commonly used channel estimation and equalization technique, converges very slowly. The convergence rate of the LMS algorithm is quite sensitive to the adjustment of the step‐size parameter used in the update equation. Therefore, many studies have concentrated on adjusting the step‐size parameter in order to improve the training speed and accuracy of the LMS algorithm. A novel approach in adjusting the step size of the LMS algorithm using the channel output autocorrelation (COA) has been proposed for application to unknown channel estimation or equalization in low‐SNR in this paper. Computer simulations have been performed to illustrate the performance of the proposed method in frequency selective Rayleigh fading channels. The obtained simulation results using HIPERLAN/1 standard have demonstrated that the proposed variable step size LMS (VSS‐LMS) algorithm has considerably better performance than conventional LMS, recursive least squares (RLS), normalized LMS (N‐LMS) and the other VSS‐LMS algorithms. Copyright © 2011 John Wiley & Sons, Ltd.     

A new two-dimensional block adaptive FIR filtering algorithm andits application to image restoration

This paper presents a new two-dimensional (2-D) optimum block stochastic gradient (TDOBSG) algorithm for 2-D adaptive finite impulse response (FIR) filtering. The TDOBSG algorithm employs a space-varying convergence factor for all the filter coefficients, where the convergence factor at each block iteration is optimized in a least squares sense that the squared norm of the a posteriori estimation error vector is minimized. It has the same order of computational complexity as another 2-D optimum block adaptive (TDOBA) algorithm. Computer simulations for image restoration show that the TDOBSG algorithm outperforms the TDOBA algorithm and other related algorithms in terms of objective and/or subjective measures.     

In the optimum design of the block adaptive FIR digital filter

A general optimum block adaptive (GOBA) algorithm for adaptive FIR (finite impulse response) filtering is presented. In this algorithm, the correction terms for the filter coefficients in each block, instead of the convergence factors, are optimized in a least squares sense. There are no constraints on the block length L and the filter tap number N. It is shown that the GOBA algorithm is reduced to the normalized LMS algorithm when L⩾N. The convergence of the GOBA algorithm can be assured if the correlation matrix of the input signal is positive definite. Computer simulations based on an efficient computing procedure confirm that the GOBA algorithm achieves faster convergence with slightly degraded convergence accuracy in stationary environments and better weight tracking capability in nonstationary environments as compared to existing block adaptive algorithms with no constraints on L and N     

广义最大总体相关熵自适应滤波算法

在输入与输出信号都被噪声污染的含误差变量模型( errors-in-variables model, EIV)中,总体最小二乘算法已经得到了广泛地应用。然而在脉冲噪声干扰的情况下,其收敛性能就会恶化。因此为了处理这种被脉冲噪声污染的含误差变量模型的情况,本文将广义最大相关熵准则与总体最小二乘估计方法结合,提出了一种鲁棒的广义最大总体相关熵自适应滤波算法。通过算法仿真比较的结果得出所提出的算法在脉冲噪声环境下能够有效地抑制脉冲噪声的存在,有着较好的收敛性能和鲁棒性。      

Adaptive linearly constrained inverse QRD-RLS beamforming algorithm for moving jammers suppression

A general, linearly constrained (LC) recursive least squares (RLS) array-beamforming algorithm, based on an inverse QR decomposition, is developed for suppressing moving jammers efficiently. In fact, by using the inverse QR decomposition-recursive least squares (QRD-RLS) algorithm approach, the least-squares (LS) weight vector can be computed without back substitution and is suitable for implementation using a systolic array to achieve fast convergence and good numerical properties. The merits of this new constrained algorithm are verified by evaluating the performance, in terms of the learning curve, to investigate the convergence property and numerical efficiency, and the output signal-to-interference-and-noise ratio. We show that our proposed algorithm outperforms the conventional linearly constrained LMS (LCLMS) algorithm, and the one using the fast linear constrained RLS algorithm and its modified version.     

变遗忘因子递推最小二乘Hammerstein系统辨识算法

摘.要:本论文研究了单输入单输出非线性Hammerstein系统的辨识问题,提出了一种具有变遗忘因子的递推最小二乘算法.由于Hammerstein系统模型的非线性特征,传统的递推最小二乘算法无法直接用来解决该系统的辨识问题.为此,论文将Hammerstein系统参数进行了映射变换,使得变换后的系统参数与Hammerst...     

Finite word-length effects in recursive least squares algorithms with application to adaptive equalization

In this paper we provide a summary of recent and new results on finite word length effects in recursive least squares adaptive algorithms. We define the numerical accuracy and numerical stability of adaptive recursive least squares algorithms and show that these two properties are related to each other, but are not equivalent. The numerical stability of adaptive recursive least squares algorithms is analyzed theoretically and the numerical accuracy with finite word length is investigated by computer simulation. It is shown that the conventional recursive least squares algorithm gives poor numerical accuracy when a short word length is used. A new form of a recursive least squares lattice algorithm is presented which is more robust to round-off errors compared to the conventional form. Optimum scaling of recursive least squares algorithms for fixedpoint implementation is also considered.     

Environment-adaptation mobile radio propagation prediction usingradial basis function neural networks

This paper investigates the application of a radial basis function (RBF) neural network to the prediction of field strength based on topographical and morphographical data. The RBF neural network is a two-layer localized receptive field network whose output nodes from a combination of radial activation functions computed by the hidden layer nodes. Appropriate centers and connection weights in the RBF network lead to a network that is capable of forming the best approximation to any continuous nonlinear mapping up to an arbitrary resolution. Such an approximation introduces best nonlinear approximation capability into the prediction model in order to accurately predict propagation loss over an arbitrary environment based on adaptive learning from measurement data. The adaptive learning employs hybrid competitive and recursive least squares algorithms. The unsupervised competitive algorithm adjusts the centers while the recursive least squares (RLS) algorithm estimates the connection weights. Because these two learning rules are both linear, rapid convergence is guaranteed. This hybrid algorithm significantly enhances the real-time or adaptive capability of the RBF-based prediction model. The applications to Okumura's (1968) data are included to demonstrate the effectiveness of the RBF neural network approach     

Low Complexity Linear Channel Estimation for MIMO Communication Systems

Channel estimation is employed to get the current knowledge of channel states for an optimum detection in fading environments. In this paper, a new recursive multiple input multiple output (MIMO) channel estimation is proposed which is based on the recursive least square solution. The proposed recursive algorithm utilizes short training sequence on one hand and requires low computational complexity on the other hand. The algorithm is evaluated on a MIMO communication system through simulations. It is realized that the proposed algorithm provides fast convergence as compared to recursive least square (RLS) and robust variable forgetting factor RLS (RVFF-RLS) adaptive algorithms while utilizing lesser computational cost and provides independency on forgetting factor.     

α噪声环境下整体最小平均P-范数IIR自适应滤波算法

当无限冲激响应(IIR)系统输入和输出信号中都存在α稳定噪声干扰,传统的最小平均P-范数算法(LMP)的解会出现较大偏差,本文提出了一种自适应IIR滤波整体最小平均P-范数(IIR_TLMP)算法,算法中整体考虑输入和输出信号的α稳定噪声干扰,将最小化l_p范数Rayleigh商采用随机梯度法得到自适应IIR滤波方程。通过仿真首先考察了特征指数和步长因子等主要参数对TLMP算法性能的影响,最后分别在时不变和时变系统中,将TLMP算法与LMP算法的性能在进行了比较,结果显示TLMP有更快的收敛速度和更小的误差。      

Adaptive MIMO decision feedback equalization for receivers with time-varying channels

In an attempt to reduce the computational complexity of vertical Bell Labs layered space time (V-BLAST) processing with time-varying channels, an efficient adaptive receiver is developed based on the generalized decision feedback equalizer (GDFE) architecture. The proposed receiver updates the filter weight vectors and detection order using a recursive least squares (RLS)-based time- and order-update algorithm. The convergence of the algorithm is examined by analysis and simulation, and it is shown that the proposed adaptive technique is considerably simpler to implement than a V-BLAST processor with channel tracking, yet the performances are almost comparable.     

An adaptive algorithm for array processing

A mathematical summary of the joint complex least squares lattice (JCLSL) adaptive algorithm is presented. The algorithm has as inputs two scalar discrete time series (primary. and reference channels). Output consists of the filtered reference channel subtracted from the primary channel. The convergence characteristics of the algorithm are illustrated experimentally for a problem where the reference channel time series consists of dual constant frequency sinusoids which undergo an instantaneous step in frequency. The primary channel time series consists of dual constant frequency sinusoids whose frequencies coincide with those of the reference channel after the step. Lastly, an application of the JCLSL algorithm to the rejection of ocean acoustic boundary reverberation is described.