Linear prediction error method for blind identification of periodically time-varying channels

Blind channel estimation for single-input multiple-output (SIMO) periodically time-varying channels is considered using only the second-order statistics of the data. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CE-BEM). The linear prediction error method for blind identification of time-invariant channels is extended to time-varying channels represented by a CE-BEM. Sufficient conditions for identifiability are investigated. The cyclostationary nature of the received signal is exploited to consistently estimate the time-varying correlation function of the data from a single observation record. The proposed method requires the knowledge of the active basis functions but not the channel length (an upper bound suffices). Several existing methods require precise knowledge of the channel length. Equalization of the time-varying channel, given the estimated channel, is investigated. Computer simulation examples are presented to illustrate the approach and to compare it with two existing approaches.     

A second-order blind source separation method for bilinear mixtures

In this paper, we are interested in the problem of Blind Source Separation using a Second-order Statistics (SOS) method in order to separate autocorrelated and mutually independent sources mixed according to a bilinear (BL) model. In this context, we propose a new approach called Bilinear Second-order Blind Source Separation, which is an extension of linear SOS methods, devoted to separate sources present in BL mixtures. These sources, called extended sources, include the actual sources and their products. We first study the statistical properties of the different extended sources, in order to verify the assumption of identifiability when the actual sources are zero-mean and when they are not. Then, we present the different steps performed in order to estimate these actual centred sources and to extract the actual mixing parameters. The obtained results using artificial mixtures of synthetic and real sources confirm the effectiveness of the new proposed approach.     

α-repetition/modulation and blind second-order identification

In the context of redundant filter-bank precoders for blind second-order equalization, we consider the α-repetition/modulation scheme. Although it is theoretically possible, the identification of a bandlimited communication channel suffers from numerical problems if α is beyond a bound. If α is below this bound, simulation examples illustrate the robustness of the channel estimate     

Matrix outer-product decomposition method for blind multiplechannel identification

Blind channel identification and equalization have attracted a great deal of attention due to their potential application in mobile communications and digital TV systems. In this paper, we present a new algorithm that utilizes second-order statistics for multichannel parameter estimation. The algorithm is simple and relies on an outer-product decomposition. Its implementation requires no adjustment for single- or multiple-user systems. This new algorithm can be viewed as a generalization of a linear prediction algorithm. It is capable of generating more accurate channel estimates and is more robust to overmodeling errors in channel order estimates. The superior performance of this new algorithm is demonstrated through simulation examples     

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.     

一种基于二阶锥约束克服信道不匹配的盲分离方法

针对多输入多输出（MIMO）系统信道不匹配造成的系统性能受损问题,结合二阶锥规划约束最优化理论,提出了一种二阶锥条件约束的盲源分离算法,用于提高信道不匹配条件下的MIMO系统性能。该算法首先利用信道不匹配问题得到的二阶锥约束条件与负熵最大化的独立分量分析形成一个约束的最优化代价函数;其次,约束的代价函数借助牛顿迭代原则最优化得到分离向量。仿真分析验证了该算法在信道不匹配条件下可以有效提升MIMO系统的性能。     

A semi-blind channel estimation technique based on second-order blind method for CDMA systems

This paper aims at studying a semi-blind channel estimation scheme based on the subspace method or a carefully weighted linear prediction approach. The corresponding (composite) semi-blind cost functions result from a linear combination of the training-based cost function and a blind cost function. For each blind method, we show how to calculate the asymptotic estimation error. Therefore, by minimizing this error, we can properly tune the K-dimensional regularizing vector introduced in the composite semi-blind criterion (for K active users in the uplink). The asymptotic estimation error minimization is a K-variable minimization problem, which is a complex issue with which to deal. We explicitly show under what conditions this problem boils down to K single-variable minimization problems. Our discussion is not limited to theoretical analyses. Simulation results performed in a realistic context [Universal Mobile Telecommunication System-time division duplex (UMTS-TDD) mode] are provided. In particular, we conclude about the potential of the proposed approach in real communication systems.     

A moment-based blind method for identification of FIR down-linkDS-CDMA channels

In order to achieve lower bit error rates (BERs), multiuser detection techniques have been proposed. Generally, multiuser detectors can provide excellent performance only when channel parameters are precisely known. In this paper, we propose a moment-based blind method to estimate down-link channels precisely. The mean square error (MSE) expression of the channel vector estimate has been derived. Through simulations, we show the effect of the channel estimation error on the performance of a multiuser detector. In this paper, we also show that finite impulse response (FIR) channels can be blindly identified by using the proposed method and the spreading operation. Since the spreading operation of the code division multiple access (CDMA) can induce cyclostationarity, any FIR channel can be identified by the proposed method. Of course, in order to guarantee identifiability, some conditions on the spreading codes will be satisfied. In this paper, these conditions are also found     

A robust off-line output error method for system identification

The model reference technique and Huber's minimax principle have been successfully used to develop an offline output error method for robust identification of systems. This method is named the robust iterative output error method with modified residuals. A convergence analysis of the proposed method has been included as well as some simulation results. In the presence of a small number of large errors (called outliers) in the input-output data, the presented method has demonstrated its distinctive advantages over not only the nonrobust methods but also previously developed robust methods. The main advantages are a fast convergence speed and satisfactory robustness. It is concluded that the method developed here is much superior to the other methods and therefore can be widely used in many real-time applications     

一种新颖的抗阶数过估计的增强子空间盲辨识算法

研究了子空间盲辨识算法在信道阶数过估计时失效的问题,提出一种新的鲁棒性强的增强算法。首先针对过估计模型,提出了"加权矩阵定理",理论证明通过构造一个加权矩阵来修正子空间算法,可消除阶数过估计的影响;然后,给出一种用指数序列构造近似的加权矩阵的增强算法。与子空间盲辨识算法相比,增强算法复杂度相当,对阶数过估计鲁棒性强。仿真实验验证了该算法的有效性。     

Penetration test method using blind SQL injection based on second-order fragment and reassembly

How to get rid of the blindness of current SQL injection penetration test,produce the optimized attack pattern of SQL injection,enhance the effectiveness in the phase of attack generation,and improve the accuracy of vulnerability detection of SQL injection using penetration test,is a big challenge.In order to resolve these problems,a new penetration test method using blind SQL injection was proposed based on second-order fragment and reassembly.In this method,the SQL injection attack model was built firstly and then the multiform and multi-type attack patterns of SQL injection penetration test driven by the SQL injection attack model was produced,which can reduce the blindness of SQL injection penetration test and improve the accuracy of SQL injection vulnerability detection.The experiments of SQL injection vulnerability detection was conducted through the actual Web applications by using proposed method in comparison with current methods.The analysis results of test show the proposed method is better compared with other methods,which not only proves the effectiveness of proposed method,but also improve the accuracy of SQL injection vulnerability detection by reducing false negative in the defensive environment.     

New criteria for blind source separation using second-order cyclic statistics

This paper addresses the problem of blind separation of cyclostationary sources. By using the cyclostationarity property of the source signals, new criteria based on second-order cyclic statistics (SOCS) are established, from which two algorithms for blind source separation are proposed. Compared with the existing higher-order statistics-based approaches, our new approach requires few data samples and does not impose any restrictions on the probability distributions of the source signals. Simulation results are given to demonstrate the effectiveness of this new approach.     

An analytical derivation for second-order blind separation of two signals

In this paper, we propose analytical formulas that involve second-order statistics for separating two signals. The method utilizes source decorrelation and correlation function diversity. In particular, the proposed SOBAS (second-order blind analytical separation) algorithm differs from the ASOBI (analytical second-order blind identification) algorithm in that it does not require prior knowledge or estimation of the noise variance. Computer simulations demonstrate the effectiveness of the proposed method.     

An iterative method using conditional second-order statistics applied to the blind source separation problem

This paper is concerned with the problem of blind separation of an instantaneous mixture of sources (BSS), which has been addressed in many ways. When power spectral densities of the sources are different, methods using second-order statistics are sufficient to solve this problem. Otherwise, these methods fail and others (higher order statistics, etc.) must be used. In this paper, we propose an iterative method to process the case of sources with the same power spectral density. This method is based on an evaluation of conditional first and second-order statistics only. Restrictions on characteristics of sources are given to reach a solution, and proofs of convergence of the algorithm are provided for particular cases of probability density functions. Robustness of this algorithm with respect to the number of sources is shown through computer simulations. A particular case of sources that have a probability density function with unbounded domain of definition is described; here, the algorithm does not lead directly to a separation state but to an a priori known mixture state. Finally, prospects of links with contrast functions are mentioned, with a possible generalization of them based on results obtained with particular sources.     

A new blind identification method for DS-CDMA systems with antennaarray under multipath fading

In recent years, some blind adaptive multiuser detectors have been presented to implement multiuser detection with only the knowledge of the desired user's spreading code and propagation delay. We find, in this paper, that with the aid of the antenna array, multiuser detection can be achieved even without the desired user's spreading code and propagation delay, provided that the direction of arrival of every user is different from each other. Propagation delay and array response estimation methods are also proposed in this paper     

Accelerated gradient with optimal step size for second-order blind signal separation

This paper proposes an algorithm for the second-order blind signal separation problem with convolutive mixtures. An iterative first order gradient method based on the accelerated gradient is developed for solving the optimization problem. For each search direction, the question becomes how to effectively calculate the optimal step size in each iteration. Here, we propose an efficient algorithm for obtaining the step size by first reformulating the objective function as a fourth order polynomial in terms of the step size, where the polynomial coefficients are required to be calculated only once per iteration. An optimal step size search procedure using the Newton’s method is developed with the step size is efficiently obtained for each iteration. Simulation results in a simulated room environment and a real environment show that the proposed algorithm converges faster than the existing methods with a lower number of iterations and a lower computational complexity. In addition, the proposed algorithm can separate the speech signals and reduce the background noise simultaneously.     

A blind source separation technique using second-order statistics

Separation of sources consists of recovering a set of signals of which only instantaneous linear mixtures are observed. In many situations, no a priori information on the mixing matrix is available: The linear mixture should be “blindly” processed. This typically occurs in narrowband array processing applications when the array manifold is unknown or distorted. This paper introduces a new source separation technique exploiting the time coherence of the source signals. In contrast with other previously reported techniques, the proposed approach relies only on stationary second-order statistics that are based on a joint diagonalization of a set of covariance matrices. Asymptotic performance analysis of this method is carried out; some numerical simulations are provided to illustrate the effectiveness of the proposed method     

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。     

Analytical blind channel identification

A novel analytical blind single-input single-output (SISO) identification algorithm is presented, based on the noncircular second-order statistics of the output. It is shown that statistics of order higher than two are not mandatory to restore identifiability. Our approach is valid, for instance, when the channel is excited by phase shift keying (PSK) inputs. It is shown that the channel taps need to satisfy a polynomial system of degree 2 and that identification amounts to solving the system. We describe the algorithm that is able to solve this particular system entirely analytically, thus avoiding local minima. Computer results eventually show the robustness with respect to noise and to channel length overdetermination. Identifiability issues are also addressed.     

一种基于二阶统计量的盲均衡算法

对于多输入多输出(MIMO)信道,使用有色输入的二阶统计量进行盲均衡是一种有效的方法.相对于原有的、基于自相关匹配原理的盲均衡算法,本文给出了一个在时域上不必先知道用户自相关序列值就能实现均衡的条件,并提出了相应的的时域均衡器训练算法,对含白噪声的情况也进行了讨论.