基于盲源分离的水声信道盲均衡处理方法

提出了一种基于盲源分离的水声信道讯均衡处理方法,通过对接收信号过采样构成源信号,采用了基于信息最大化原理(Infomax)在线分离算法进行了水声信道的盲均衡,并研究了时变水声信道条件下算法的均衡情况,仿真实验结果表明,该处理方法对多径水声信道具有较好的均衡效果,同时不受最小相位的条件限制。     

Adaptive blind channel identification algorithm based on linearprediction for SIMO FIR systems

An adaptive algorithm for blind identification of single-input multiple-output (SIMO) FIR systems is proposed. It is based on the one-step forward linear prediction (LP) technique and can be implemented by an RLS adaptation. Unlike most second-order statistics (SOS)-based approaches, the proposed solution does not require the computation of the correlation matrix or its inverse explicitly. The obtained results demonstrate that the proposed approach is able to deliver better performance compared with the typical batch algorithm. It is also observed that the proposed algorithm can tolerate the appearance of near common zeros among the subchannels     

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。     

基于自适应Volterra滤波器的非线性系统辨识

为了识别非线性系统的参数,本文提出了一种基于自适应Volterra滤波器的非线性系统辨识方法。给出了自适应Volterra滤波器的LMS和RLS算法。数值仿真结果表明,该方法对于非线性定常和时变系统都有效。     

Spectral inversion of second order Volterra models based on the blind identification of Wiener models

In this paper, we develop two main results. The first one is a theorem proving that a second order Wiener model can be blindly identified, i.e. using only the mean, the third and fourth order cumulants of the output data. The second result is the application of this theorem to spectral inversion (i.e. the recovering of the power spectrum density) of the input signal of a second order Volterra model to which usual inversion schemes cannot be applied, in particular when the linear kernel has a strong attenuation in frequency range. Numerical results are discussed with respect to the nonlinear energy amount of the output, the time series length and the SNR values.     

Blind equalization of MIMO systems based on orthogonal Constant Modulus Algorithm

This paper investigates adaptive blind source separation and equalization for Multiple Input Multiple Output （MIMO） systems. To effectively recover input signals, remove Inter-Symbol Interference （ISI） and suppress Inter-User Interference （IUI）, the array input is first transformed into the signal subspace, then with the derived orthogonality between weight vectors of different input signals, a new orthogonal Constant Modulus Algorithm （CMA） is proposed. Computer simulation results illustrate the promising performance of the proposed method. Without channel identification, the proposed method can recover all the system inputs simultaneously and can be adaptive to channel changes without prior knowledge about signals.     

On Jacobi-type methods for blind equalization of paraunitary channels

In this paper a study of the cumulant-based blind equalization algorithms PAJOD and PAFA is conducted. Both algorithms assume that the data have been pre-whitened and hence the problem reduces to the estimation of paraunitary channels. The main contribution of this paper is an efficient implementation of the PAJOD algorithm called PAJOD2. Second, a performance comparison between the PAJOD, PAJOD2 and PAFA algorithms is reported.     

SUBSPACE METHOD FOR BLIND IDENTIFICATION OF CDMA TIME—VARYING CHANNELS

A new blind method is proposed for identification of CDMA Time-Varyin(TV) channels in this paper.By representing the TV channel‘s impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant(TI) coordingates is proposed.Unlike existing basis expansion methods, this new algorithm does not require estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols.The algorithm offers definite explanation of the expansion coordinates.Simulation demonstrates the effectiveness of the algorithm.     

Decision directed adaptive blind equalization based on the constant modulus algorithm

In this paper, new decision directed algorithms for blind equalization of communication channels are presented. These algorithms use informations about the last decided symbol to improve the performance of the constant modulus algorithm (CMA). The main proposed technique, the so called decision directed modulus algorithm (DDMA), extends the CMA to non-CM modulations. Assuming correct decisions, it is proved that the decision directed modulus (DDM) cost function has no local minima in the combined channel-equalizer system impulse response. Additionally, a relationship between the Wiener and DDM minima is established. The other proposed algorithms can be viewed as modifications of the DDMA. They are divided into two families: stochastic gradient algorithms and recursive least squares (RLS) algorithms. Simulation results allow to compare the performance of the proposed algorithms and to conclude that they outperform well-known methods.     

Blind equalization in spatial multiplexing MIMO-OFDM systems based on vector CMA and decorrelation criteria

In this paper we address the problem of blind recovery of multiple OFDM data streams in a Multiple-Input Multiple-Output (MIMO) system. We propose an equalization algorithm which is based on a combined criterion designed to cancel both inter-symbol interference (ISI) and co-channel interference (CCI). ISI is minimized by using a modified Vector Constant Modulus criterion while CCI is minimized by a decorrelation criterion. We establish conditions for the existence of the stable minima corresponding to the zero forcing receiver which performs the joint blind equalization and the co-channel signal cancellation. The local convergence properties of the algorithm are proved under the assumption that the balance parameter weighting the two criteria is set appropriately. We also provide the optimal value for this parameter. Reliable performance is achieved with relatively fast convergence and small steady-state error. The implementation of the blind equalizer requires low-computational cost, without any matrix inversions or other expensive operations.     

Subspace projection based blind channel order estimation of MIMO systems

In this paper, a novel algorithm based on subspace projections is developed for blindly estimating the discrete orders of a linear finite-impulse-response (FIR) multiple-input multiple-output (MIMO) system, the number of subsystems that attain each order as well as the total number of inputs. Furthermore, the proposed algorithm applies to single-input multiple-output (SIMO) system order estimation. Simulations in the context of blind channel order estimation show good performance in comparison to existing schemes developed for SIMO systems.     

Computationally efficient methods for blind decision feedback equalization of QAM signals

This paper investigates computationally efficient methods for blind decision feedback equalization (DFE) that reduce the complexity and power requirements of blind equalization algorithms while maintaining their steady-state characteristics for quadrature amplitude modulation (QAM) signals. These include the power-of-two error (POT), selective coefficient update (SCU), and frequency-domain block (FDB) methods. A novel radius-directed stop-and-go (RSG) method is introduced, which selectively adjusts the equalizer tap coefficients based on the equalizer output radius. In addition, a new activation/de-activation method based on the equalizer output radius is utilized to control the feedback equalizer (FBE) of the DFEs. Simulation studies and analysis are provided for empirically derived cable and microwave channels and Ricean fading channels.     

Blind identification of a class of nonlinear systems with cyclostationary input

This letter deals with blind identification of nonlinear discrete Hammerstein system under the input signal that is cyclostationary. The first-order moment of the specific input as well as the inverse nonlinear mapping of the Hammerstein model are combined to establish a relationship between the system output and the system parameters, which implies an approach to identifying the system blindly. Simulation results demonstrate the effectiveness of this approach to blind identification of a class of nonlinear systems.     

Volterra级数模型辨识算法的改进研究

本文研究了 Volterra级数模型全解耦辨识算法,提出Volterra级数模型并行辨识算法的设计思想,设计了并行算法.实验和仿真结果表明,该算法能有效减小模型的在线辨识时间,能有效克服Volterra级数模型辨识中的维数灾难问题,且收敛速度快、稳态精度高,有利于实现工程应用.     

Baud-spaced constant modulus blind equalization via hybrid genetic algorithm and generalized pattern search optimization

The present paper deals with the formulation of the baud-spaced constant modulus blind equalization in the presence of Gaussian noise as an unconstrained optimization problem via the use of genetic algorithm and generalized pattern search (GPS) algorithm. Simulation results show that the proposed approach is more robust to the adopted initialization strategy and much more likely to outperform the classical constant modulus algorithm in terms of minimum-squared error, intersymbol interference and error vector magnitude quantities.     

Low-complexity adaptive decision-feedback equalization of MIMO channels

A new adaptive MIMO channel equalizer is proposed based on adaptive generalized decision-feedback equalization and ordered-successive interference cancellation. The proposed equalizer comprises equal-length subequalizers, enabling any adaptive filtering algorithm to be employed for coefficient updates. A recently proposed computationally efficient recursive least squares algorithm based on dichotomous coordinate descents is utilized to solve the normal equations associated with the adaptation of the new equalizer. Convergence of the proposed algorithm is examined analytically and simulations show that the proposed equalizer is superior to the previously proposed adaptive MIMO channel equalizers by providing both enhanced bit error rate performance and reduced computational complexity. Furthermore, the proposed algorithm exhibits stable numerical behavior and can deliver a trade-off between performance and complexity.     

Exploiting non-Gaussianity in blind identification and equalisation of MIMO FIR channels

The problem of blind identification and equalisation (BIE) of finite impulse response (FIR) channels in multiuser digital communications is investigated. The non-Gaussian nature and statistical independence of the users' data streams is exploited by resorting to blind signal separation (BSS) based on higher-order statistics (HOS). Two such techniques are put forward. The first technique is composed of an extension to the multiuser case of a second-order BIE method, followed by a BSS-based space-equalisation step. The second technique achieves joint space-time equalisation through the direct application of a HOS-based BSS method followed by a blind identification algorithm. In a number of numerical experiments, the first procedure proves less costly and more effective for short data records. Despite their computational complexity, interesting features such as constellation-independent channel identification and symbol recovery, and robustness to ill-conditioned channels in high SNR environments render HOS-BSS based BIE methods an effective alternative to BIE techniques exploiting other spatio-temporal structures.     

MIMO非线性系统辨识：Volterra级数法

本文讨论了一般ＭＩＭＯ非线性系统的Ｖｏｌｔｅｒｒａ级数表示,给出并证明了Ｖｏｌｔｅｒｒａ核矩阵存在的条件。提出了两种辨识Ｖｏｌｔｅｒｒａ核矩阵的方法,即随机响应法与脉冲响应法。给出了利用系统的输入输出信号计算Ｖｏｌｔｅｒｒａ核矩阵的公式。最后,利用仿真实例验证上述方法的有效性。     

A robust adaptive weighted constant modulus algorithm for blind equalization of wireless communications systems under impulsive noise environment

A robust adaptive weighted constant modulus algorithm is proposed for blind equalization of wireless communication systems under impulsive noise environment. The influence of the impulsive noise is analyzed based on numerical analysis method. Then an adaptive weighted constant modulus algorithm is constructed to adaptively suppress impulsive noise. Theoretical analysis is provided to illustrate that the proposed algorithm has a robust equalization performance since the impulsive noise is adaptively suppressed. Moreover, the proposed algorithm has stable and quick convergence due to avoidance of large misadjuntment and adoption of large step size. Simulation results are presented to show the robust equalization performance and the fast convergence speed of the proposed algorithm under both impulsive noise and Gaussian noise environments.     

Blind methods of system identification

This paper reviews the basic identifiability conditions and identification methods for blind system identification. This review focuses on the exploitation of the second-order statistics of the system output. The blind methods vary significantly according to the categories of the systems: i.e., single-input, single-output (SISO) systems, single-input, multiple-output (SIMO) systems, or multiple-input, multiple-output (MIMO) systems. For SISO systems, the blind methods require white input and minimum phase frequency response. For SIMO systems, the blind methods can generally yield the exact identification up to a scalar using a finite set of data. For MIMO systems, the blind identifiability conditions and the blind methods are much more involved.