Blind two-input-two-output FIR channel identification based onfrequency domain second-order statistics

We present an analytical solution to the two-input-two-output blind crosswise mixture identification based on eigenvalue decomposition of second-order spectra correlations. The sources are independent and non-white, but otherwise, we consider their statistics to be unknown. We show that the cross channels cannot be uniquely determined by the analysis of the frequency domain covariance alone due to the unknown eigenvector permutations. However, the problem can be attacked with the help of two invariant indices that are immune to these permutations. Using these indices together with standard reconstruction-from-phase techniques, we show that the channels can be uniquely determined. Our theoretical results lead to a novel frequency domain second-order algorithm that identifies the unknown channels     

Blind identification of FIR systems driven by Markov-like inputsignals

We propose a new algorithm for the blind identification and equalization of finite impulse response (FIR) systems using the second-order statistics of the received signal. The new algorithm is set in the same context as the algorithms of Tong et al. (1994) and Moulines et al. (1995), however, unlike those earlier approaches it is designed to allow correlated input signals. Specifically, the algorithm accommodates finite memory sources and sources whose autocorrelation function decays exponentially. Numerical simulations compare the equalization performance of the new algorithm to those of Tong and Moulines. It is shown that our algorithm yields consistently lower bit-error rates at a wide variety of signal-to-noise ratios and at various equalizer lengths. Moreover, the algorithm maintains this advantage even if it has no a priori information of source correlation or if source symbols are uncorrelated     

Blind MIMO FIR channel identification based on second-order spectra correlations

We consider a problem of identifying a multiple-input multiple-output (MIMO) finite impulse response (FIR) system excited by colored inputs with known statistics. We propose a new, nonlinear optimization-based method that involves the power spectra and cross-spectra of the system output. The proposed algorithm is tested for the case of cyclostationary inputs (CDMA scenario) and stationary inputs (SDMA scenario). Simulation results indicate that the proposed scheme works well, even for large order systems, and is robust to noise and channel length mismatch.     

Blind channel identification based on second-order statistics: afrequency-domain approach

In this communication, necessary and sufficient conditions are presented for the unique blind identification of possibly nonminimum phase channels driven by cyclostationary processes. Using a frequency domain formulation, it is first shown that a channel can be identified by the second-order statistics of the observation if and only if the channel transfer function does not have special uniformly spaced zeros. This condition leads to several necessary and sufficient conditions on the observation spectra and the channel impulse response. Based on the frequency-domain formulation, a new identification algorithm is proposed     

Blind identification of FIR MIMO channels by decorrelating subchannels

We study blind identification and equalization of finite impulse response (FIR) and multi-input and multi-output (MIMO) channels driven by colored signals. We first show a sufficient condition for an FIR MIMO channel to be identifiable up to a scaling and permutation using the second-order statistics of the channel output. This condition is that the channel matrix is irreducible (but not necessarily column-reduced), and the input signals are mutually uncorrelated and of distinct power spectra. We also show that this condition is necessary in the sense that no single part of the condition can be further weakened without another part being strengthened. While the above condition is a strong result that sets a fundamental limit of blind identification, there does not yet exist a working algorithm under that condition. In the second part of this paper, we show that a method called blind identification via decorrelating subchannels (BIDS) can uniquely identify an FIR MIMO channel if a) the channel matrix is nonsingular (almost everywhere) and column-wise coprime and b) the input signals are mutually uncorrelated and of sufficiently diverse power spectra. The BIDS method requires a weaker condition on the channel matrix than that required by most existing methods for the same problem.     

Blind identification of FIR systems excited by discrete-alphabetinputs

An algorithm for the identification of finite-impulse-response (FIR) system parameters from output measurements, for systems excited by discrete-alphabet inputs, is described. The approach taken is algebraic. It does not rely directly on the statistical properties of the measurements, but rather it essentially solves the nonlinear equations appearing in the problem by converting them to equivalent linear equations, using the discrete-alphabet property of the input signal. The proposed algorithm was tested by computer simulations and some of these simulations are illustrated     

Blind channel estimation using the second-order statistics:algorithms

Most second-order moment-based blind channel estimators belong to two categories: (i) optimal correlation/spectral fitting techniques and (ii) eigenstructure-based techniques. These two classes of algorithms have complementary advantages and disadvantages. A new optimization criterion referred to as the joint optimization with subspace constraints (JOSC) is proposed to unify the two types of approaches. Based on this criterion, a new algorithm is developed to combine the strength of the two classes of blind channel estimators. Among a number of attractive features, the JOSC algorithm does not require the accurate detection of the channel order. When compared with existing eigenstructure-based techniques, the JOSC performs better, especially when the channel is close to being unidentifiable. When compared with correlation/spectral fitting schemes, the JOSC is less affected by the presence of local minima     

多输入多输出正交频分复用中基于频域的信干噪比估计

终端的信干噪比(SINR)信息是多输入多输出正交频分复用(MIMO OFDM)系统进行编码调制方式选择和资源分配的重要依据之一。在频率选择性衰落信道中,针对MIMO OFDM的链路特征提出一种低复杂度的SINR估计方法:利用子载波之间的相关性构造降秩矩阵,提取其协方差矩阵的最小特征值来估计SINR,并分析了SINR估计误差。遵循LTE标准规范,进行了计算机仿真。结果表明:在SINR小于30 dB时,该方法只需要利用连续4个参考信号载波,便可实现估计误差小于0.2 dB,且该方法对最大多普勒频移不敏感。     

Blind MIMO channel identification from second order statistics using rank deficient channel convolution matrix

For multiuser systems, several direct blind identification algorithms require that the linear multiple-input multiple-output (MIMO) system have a full rank convolution matrix. This condition requires that the system transfer function be irreducible and column reduced. We show that this restrictive identification condition can be relaxed for some direct blind identification methods to accommodate more practical scenarios. Algorithms such as the outer-product decomposition algorithm only require minor length adjustment to its processing window without the column-reduced condition. This result allows direct blind identification methods to be applicable to MIMO without requiring a full-rank channel convolution matrix.     

Blind identification of multichannel FIR systems based on linearprediction

A blind identification algorithm for multichannel FIR systems is proposed. In the approach, the identification of each channel is decoupled, and channel responses are estimated separately without having to solve for the augmented channel responses. The algorithm can be implemented using linear prediction techniques. It is computationally efficient and suitable for real-time applications. Computer simulations are used to demonstrate the effectiveness of the proposed algorithm     

Group decorrelation enhanced subspace method for identifying FIR MIMO channels driven by unknown uncorrelated colored sources

Identification of finite-impulse-response (FIR) and multiple-input multiple-output (MIMO) channels driven by unknown uncorrelated colored sources is a challenging problem. In this paper, a group decorrelation enhanced subspace (GDES) method is presented. The GDES method uses the idea of subspace decomposition and signal decorrelation more effectively than the joint diagonalization enhanced subspace (JDES) method previously reported in the literature. The GDES method has a much better performance than the JDES method. The correctness of the GDES method is proved assuming that 1) the channel matrix is irreducible and column reduced and 2) the source spectral matrix has distinct diagonal functions. However, the GDES method has an inherent ability to trade off between the required condition on the channel matrix and that on the source spectral matrix. Simulations show that the GDES method yields good results even when the channel matrix is not irreducible, which is not possible at all for the JDES method.     

Blind channel estimation using the second-order statistics:asymptotic performance and limitations

We consider the asymptotic performance and fundamental limitations of the class of blind estimators that use second-order statistics. An achievable lower bound of the asymptotic normalized mean-square error (ANMSE) is derived. It is shown that the achievable ANMSE is lower bounded by the condition number of the Jacobian matrix of the correlation function with respect to the channel parameters. It is shown next that the Jacobian matrix is singular if and only if the subchannels share common conjugate reciprocal zeros. This condition is different from the existing channel identification conditions. Asymptotic performance of some existing eigenstructure-based algorithms is analyzed. Closed-form expressions of ANMSE and their lower bounds are derived for the least-squares (LS) and the subspace (SS) blind channel estimators when there are two subchannels. Asymptotic efficiency of LS/SS algorithms is also evaluated, showing that significant performance improvement is possible when the information of the source correlation is exploited     

Blind channel equalization with colored sources based onsecond-order statistics: a linear prediction approach

We consider the blind equalization and estimation of single-user, multichannel models from the second-order statistics of the channel output when the channel input statistics are colored but known. By exploiting certain results from linear prediction theory, we generalize the algorithm of Tong et al. (1994), which was derived under the assumption of a white transmitted sequence. In particular, we show that all one needs to estimate the channel to within an unitary scaling constant, and thus to find its equalizers, is (a) that a standard channel matrix have full column rank, and (b) a vector of the input signal and its delays have positive definite lag zero autocorrelation. An algorithm is provided to determine the equalizer under these conditions. We argue that because this algorithm makes explicit use of the input statistics, the equalizers thus obtained should outperform those obtained by other methods that neither require, nor exploit, the knowledge of the input statistics. Simulation results are provided to verify this fact     

Blind identification and equalization based on second-orderstatistics: a time domain approach

A new blind channel identification and equalization method is proposed that exploits the cyclostationarity of oversampled communication signals to achieve identification and equalization of possibly nonminimum phase (multipath) channels without using training signals. Unlike most adaptive blind equalization methods for which the convergence properties are often problematic, the channel estimation algorithm proposed here is asymptotically ex-set. Moreover, since it is based on second-order statistics, the new approach may achieve equalization with fewer symbols than most techniques based only on higher-order statistics. Simulations have demonstrated promising performance of the proposed algorithm for the blind equalization of a three-ray multipath channel     

Blind identification and deconvolution of linear systems driven bybinary random sequences

The problem of blind identification and deconvolution of linear systems with independent binary inputs is addressed. To solve the problem, a linear system is applied to the observed data and adjusted so as to produce binary outputs. It is proved that the system coincides with the inverse of the unknown system (with scale and shift ambiguities), whether it is minimum or nonminimum phase. These results are derived for nonstationary independent binary inputs of infinite or finite length. Based on these results, an identification method is proposed for parametric linear systems. It is shown that under some mild conditions, a consistent estimator of the parameter can be obtained by minimizing a binariness criterion for the output data. Unlike many other blind identification and deconvolution methods, this criterion handles nonstationary signals and does not utilize any moment information of the inputs. Three numerical examples are presented to demonstrate the effectiveness of the proposed method     

Blind identification of an autoregressive system using a nonlineardynamical approach

The problem of identifying an autoregressive (AR) system with arbitrary driven noise is considered here. Using an abstract dynamical system to represent both chaotic and stochastic processes in a unified framework, a dynamic-based complexity measure called phase space volume (PSV), which has its origins in chaos theory, can be applied to identify an AR model in chaotic as well as stochastic noise environments. It is shown that the PSV of the output signal of an inverse filter applied to identify an AR model is always larger than the PSV of the input signal of the AR model. Therefore, by minimizing the PSV of the inverse filter output, one can estimate the coefficients and the order of the AR system. A major advantage of this minimum-phase space volume (MPSV) identification technique is that it works like a universal estimator that does not require precise statistical information about the AR input signal. Because the theoretical PSV is so difficult to compute, two approximations of PSV are also considered: the e-PSV and nearest neighbor PSV. Both approximations are shown to approach the ideal PSV asymptotically. The identification performance based on these two approximations are evaluated using Monte Carlo simulations. Both approximations are found to generate relatively good results in identifying an AR system in various noise environments, including chaotic, non-Gaussian, and colored noise     

Subspace based blind identification of LTI FIR MIMO systems and equalization of finite memory SIMO Volterra systems

In this paper a novel algorithm based on subspace projections is developed for the blind kernel identification of LTI FIR multiple input multiple output (MIMO) systems, as well as blind equalization of finite memory SIMO Volterra systems. In addition, for Volterra systems, the algorithm computes the memory lengths of the nonlinearities involved. Simulations in the context of blind channel equalization and identification demonstrate good performance in comparison to existing schemes.     

利用时频域稀疏性的LFM信号盲源分离方法

信号分离是雷达电子对抗的重要环节。考虑到雷达信号在时频域具有稀疏性的特点,在独立分量分析的基础上,提出了一种基于时频域稀疏性的线性调频雷达信号盲源分离方法。首先对混合信号进行短时傅里叶变换,在每个频点利用自然梯度算法分离信号,由分离信号幅度的比值作为对源信号后验概率的估计;然后根据相邻频点后验概率序列的相关性进行排序,确保各个频点的分离信号属于同一个源信号;最后设计时频掩码分离信号。进行了线性调频雷达信号卷积混合的盲分离实验,所提方法分离结果明显优于传统独立分量分析方法的分离结果,验证了该方法的有效性。     

Blind identification with periodic modulation: a time-domain approach

We propose a method for blind identification of finite impulse response (FIR) channels with periodic modulation. The time-domain formulation in terms of block signals is simple compared with existing frequency-domain formulations. It is shown that the linear equations relating the products of channel coefficients and the autocorrelation matrix of the received signal can be further arranged into decoupled groups. The arrangement reduces computations and improves accuracy of the solution; it also leads to very simple identifiability conditions and a very natural formulation of the optimal modulating sequence selection problem. The proposed optimal selection minimizes the effects of channel noise and error in autocorrelation matrix estimation; it results in a consistent channel estimate when the channel noise is white. Simulation results show that the method yields good performance. It compares favorably with an existing subspace modulation-induced-cyclostationarity method, and it is robust with respect to channel order overestimation. The effect of modulation period and threshold of the modulating sequence are also discussed.     

Semi-blind identification of ARMA systems using a dynamic-based approach

A novel dynamic-based semi-blind approach is proposed to identify an autoregressive and moving average (ARMA) system in this paper. By using a chaotic driving signal, an ARMA system can be identified accurately by a dynamic-based estimation method called the ergodic-based minimum phase space volume (EMPSV). A maximum-likelihood formulation of EMPSV is provided to certify its unbiasedness and asymptotical efficiency. Monte Carlo simulations show that the EMPSV approach has a smaller mean-square error performance than the minimum phase space volume method and the conventional identification approach based on least-squares estimation with white Gaussian probing signals. The proposed approach is then applied to blind deconvolution of real audio signals and semi-blind channel equalization for chaos communications. It is shown that the EMPSV approach has improved deconvolution and equalization performances compared to conventional techniques in both applications.