Blind identification of MIMO channels with periodic precoders

In this paper, we show a new blind identification algorithm which is based on second order statistics and exploits a single-input double-output (SIDO) model. It is suitable for a real-time processing system because of lower operation and high-speed convergence. The proposed blind identification algorithm is superior to conventional algorithms in view of simple structure and the uniqueness of solution. We also verify its efficiency by computer simulation.

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.

Pulse shaping OFDM systems based on offset QAM (OFDM/OQAM) are particularly attractive for wireless high-data-rate applications. Since OFDM/OQAM schemes do not employ a cyclic prefix, equalization has to be performed. In this paper, we introduce an algorithm for blind channel identification in pulse shaping OFDM/OQAM systems. Our approach assumes that the receiver knows the transmitter pulse shaping filter. Exploiting cyclostationarity of the received OFDM signal blind channel identification is accomplished using second-order statistics only. The proposed method exhibits low noise sensitivity, is computationally efficient, and does not require knowledge of the channel order. Finally, we provide simulation results demonstrating the performance of the new algorithm.

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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A Khatri–Rao subspace approach to blind identification of mixtures of quasi-stationary sources

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Blind identification is a crucial subtask in signal processing problems such as blind signal separation (BSS) and direction-of-arrival (DOA) estimation. This paper presents a procedure for <emphasis emphasistype="boldital">multiple-input multiple-output instantaneous blind identification</emphasis> based on second-order temporal properties of the signals, such as coloredness and nonstationarity. The procedure consists of two stages. First, based on assumptions on the second-order temporal structure (SOTS) of the source and noise signals, and using subspace techniques, the problem is reformulated in a particular way such that each column of the unknown mixing matrix satisfies a system of multivariate homogeneous polynomial equations. Then, this nonlinear system of equations is solved by means of a so-called homotopy method, which provides a general tool for solving (possibly nonexact) systems of nonlinear equations by smoothly deforming the known solutions of a simple start system into the desired solutions of the target system. Our blind identification procedure allows to estimate the mixing matrix for scenarios with more sources than sensors without resorting to sparsity assumptions, something that is often believed to be impossible when using only second-order statistics. In addition, since our algorithm does not require <emphasis emphasistype="boldital">any</emphasis> assumption on the mixing matrix, also mixing matrices that are rank-deficient or even have identical columns can be identified. Finally, we give examples and performance results for speech source signals. </para>

We present a blind closed-form consistent channel estimator for multiple-input multiple-output (MIMO) systems that uses only second order statistics. We spectrally modulate the output of each source by correlative coding it with a distinct filter. The correlative filters are designed to meet the following desirable characteristics: no additional power or bandwidth is required; no synchronization between the sources is needed; the original data rate is maintained. We first prove an identifiability theorem: under a simple spectral condition on the transmitted random processes, the MIMO channel is uniquely determined, up to a phase offset per user, from the second order statistics of the received data. We then develop the closed-form algorithm that attains this identifiability bound. We show that minimum-phase finite impulse response filters with arbitrary memory satisfy our sufficient spectral identifiability condition. This results in a computationally attractive scheme for retrieving the data information sequences after the MIMO channel has been identified. We assess the performance of the proposed algorithms by computer simulations. In particular, the results show that our technique outperforms the previously introduced transmitter-induced conjugate cyclostationarity approach when there are carrier frequency misadjustments.

Blind channel identification/estimation is very important for object detection, trace, localization in the ocean acoustics. Time domain blind identification algorithm requiring exact length of the channel being identification. Due to the characteristics of the shallow-water channel, the length of channel impulse response sequence is uncertain, Hence a frequency domain method for the blind MIMO (Multiple-Input Multiple-Output) underwater identification based on higher order statistics (HOS) is used to estimate the original acoustic channel from received signals on hydrophones only, with the low signal to noise ratio (SNR). The simulation results in the acoustic environment proved this work is effective and efficient for blind identification of the shallow-water acoustic channel.

Blind multichannel identification was first introduced in the mid 1970s and initially studied in the communication society with the intention of designing more-efficient communication systems by avoiding a training phase. Recently this idea has become increasingly interesting for acoustics and speech processing research, driven by the fact that in most acoustic applications for speech processing and communication very little or nothing is known about the source signals. Since human ears have an extremely wide dynamic range and are much more sensitive to weak tails of the acoustic impulse responses, these impulse responses need to be modeled using fairly long filters. Attempting to identify such a multichannel system blindly with a batch method involves intensive computational complexity. This is not just bad system design, but technically rather implausible, particularly for real-time systems. Therefore, adaptive blind multichannel identification algorithms are favorable and pragmatically useful. This chapter describes some fundamental issues in blind multichannel identification and reviews a number of state-of-the-art adaptive algorithms.

Recent results have pointed out the importance of inducing cyclostationarity at the transmitter for blind identification and equalization of communication channels. This paper addresses blind channel identification and equalization relying on the modulation-induced cyclostationarity, without introducing redundancy at the transmitter. It is shown that single-input single-output channels can be identified uniquely from output second-order cyclic statistics, irrespective of the location of channel zeros, color of additive stationary noise, or channel order overestimation errors, provided that the period of modulation-induced cyclostationarity is greater than half the channel length. Linear, closed-form, nonlinear correlation matching, and subspace-based approaches are developed for channel estimation and are tested using simulations. Necessary and sufficient blind channel identifiability conditions are presented. A Wiener cyclic equalizer is also proposed.

In this paper, we propose a new structure of blind system identification and its cost function. The proposed cost function is a quadratic form and has the unique solution. In addition, the proposed scheme can employ iterative algorithms which achieve less computational complexity and can be easily realized in real time processing. In order to verify the effectiveness of the proposed scheme, a computer simulation has been shown.

In this study, an improved image blind identification algorithm based on inconsistency in light source direction was proposed. And a new method defined as "neighborhood method" was presented, which was used to calculate surface normal matrix of image in the blind identification algorithm. For an image, there is an error function between its actual light intensity and calculated light intensity, and for different light source models, there are different constraint functions of light. Light source direction which makes both error function and constraint function get the minimum is the one we want to seek. On this basis, according to the error function and the corresponding constraint function, search means and the Hestenes---Powell multiplier method were used in the improved algorithm to calculate the light source direction for local and infinite light source images, respectively. Further, the authenticity of image can be determined by the inconsistency in light source direction of different areas in the image. Experimental results showed that the light source direction of different areas in an image could be calculated accurately, and then the image tampering can be detected effectively by the improved algorithm. Moreover, the performance of the improved algorithm of the proposed blind identification is superior to that of the existing one in terms of detection rate and time complexity.

A new method is proposed for the blind subspace-based identification of the coefficients of time-varying (TV) single-input multiple-output (SIMO) FIR channels. The TV channel coefficients are represented via a finite basis expansion model, i.e. linear combination of known basis functions. In contrast to earlier related works, the basis functions need not be limited to complex exponentials, and therefore do not necessitate the a priori estimation of frequency parameters. This considerably simplifies the implementation of the proposed method and provides added flexibility in applications. The merits of the proposed technique, including asymptotic consistency, are demonstrated by numerical simulations.

Blind parameter estimation of the non-minimum phase (NMP) channel with finite impulse response (FIR) is studied. An efficient and reliable blind estimation algorithm is proposed which is based on the combination of second order statistics (SOS) of the received signal and finite alphabet (FA) property of the transmitted signal. Second order statistics based methods provide efficient estimation of channel zeros from a very small number of samples. But SOS based methods are phase blind. The key idea proposed is to use the FA property of the signals resulting from an inverse filter, in conjunction with SOS, to locate the channel zeros, i.e., the FA property is employed to resolve the ambiguity of the system zero location. The equalizer output signal could be exploited recursively at the receiver to improve the estimation accuracy using the FA property. The superior behavior of the proposed method is demonstrated by simulation examples and comparison with some recent works.

We address the problem of the blind joint identification and order estimation of a non-minimum phase FIR communication channel by exploiting the cyclo-stationarity of the received signal sampled at a rate greater than the symbol rate. We show that the identification can be formulated as a fitting problem; this allows for using the subspace distance as a test statistic to detect the correct channel length (among different hypotheses). Moreover, mimicking estimation procedures proposed in the framework of DOA estimation, an asymptotically efficient procedure is proposed which obtains the channel estimate in two steps: the channel estimate obtained in the first step determines optimal weights which are then employed in the second step to refine the channel estimate. The accuracy of the identification is comparable with other methods described in the literature (e.g. the subspace method) while the test for order detection performs quite well also in the presence of channel disparity outperforming commonly used tests based on the eigenvalues of the covariance matrix.