Blind Channel Identification Methods Research Articles

Subspace-Based Blind Channel Identification for Cyclic Prefix Systems Using Few Received Blocks

In this paper, a novel generalization of subspace-based blind channel identification methods in cyclic prefix (CP) systems is proposed. For the generalization, a new system parameter called repetition index is introduced whose value is unity for previously reported special cases. By choosing a repetition index larger than unity, the number of received blocks needed for blind identification is significantly reduced compared to all previously reported methods. This feature makes the method more realistic especially in wireless environments where the channel state is usually fast-varying. Given the number of received blocks available, the minimum value of repetition index is derived. Theoretical limit allows the proposed method to perform blind identification using only three received blocks in absence of noise. In practice, the number of received blocks needed to yield a satisfactory bit-error-rate (BER) performance is usually on the order of half the block size. Simulation results not only demonstrate the capability of the algorithm to perform blind identification using fewer received blocks, but also show that in some cases system performance can be improved by choosing a repetition index larger than needed. Simulation of the proposed method over time-varying channels clearly demonstrates the improvement over previously reported methods.

Subspace algorithm for blind channel identification and synchronization in single-carrier block transmission systems

A blind channel identification and synchronization method for single-carrier block transmission system over frequency-selective fading channel is proposed. We employ cyclic prefix (CP) insertion to introduce the redundancy at the transmitter side. As a result, the channel impulse response (CIR) could be estimated in a blind fashion by means of subspace algorithm based on second-order statistics through the exploitation of the redundancy. The condition for identifying CIR along with the proof of uniqueness of CIR estimate is provided regardless of CP length relative to channel order as well as data frame size. We further develop a sorting approach to avert timing offset issue in block synchronization. Numerical results presented performance comparison with orthogonal frequency-division multiplexing (OFDM) systems in terms of bit error rate (BER) and channel estimation error. We also compare with traditional zero-padding OFDM systems in terms of the failure rate of block synchronization.

Blind FSR-LPTV equalization and interference rejection

We address the problem of synthesizing blind channel identification and equalization methods for digital communications systems, aimed at counteracting the presence of cochannel or adjacent-channel interference. Owing to the presence of the interfering signal, the minimum mean-square error equalizer turns out to be linear periodically time-varying, which is implemented by resorting to its Fourier series representation. Moreover, by exploiting the cyclic conjugate second-order statistics of the channel output, we propose a new weighted subspace-based channel identification method, which is asymptotically immune to the presence of high-level interference. Computer simulation results confirm the effectiveness of the proposed identification/equalization technique.

Blind channel approximation: effective channel order determination

A common assumption of blind channel identification methods is that the order of the true channel is known. This information is not available in practice, and we are obliged to estimate the channel order by applying a rank detection procedure to an overmodeled data covariance matrix. Information theoretic criteria have been widely suggested approaches for this task. We check the quality of their estimates in the context of order estimation of measured microwave radio channels and confirm that they are very sensitive to variations in the SNR and the number of data samples. This fact has prohibited their successful application for channel order estimation and hits created some confusion concerning the classification into under- and over-modeled cases. Recently, it has been shown that blind channel approximation methods should attempt to model only the significant part of the channel composed of the large impulse response terms because efforts toward modeling small leading and/or trailing terms lead to effective overmodeling, which is generically ill-conditioned and, thus, should be avoided. This can be achieved by applying blind identification methods with model order equal to the order of the significant part of the true channel called the effective channel order. Toward developing an efficient approach for the detection of the effective channel order, we use numerical analysis arguments. The derived criterion provides a maximally stable decomposition of the range space of an overmodeled data covariance matrix into signal and noise subspaces. It is shown to be robust to variations in the SNR and the number of data samples. Furthermore, it provides useful effective channel order estimates, leading to sufficiently good blind approximation/equalization of measured real-world microwave radio channels.

The application of blind channel identification techniques to prestack seismic deconvolution

One objective of seismic signal processing is to identify the layered subsurface structure by sending seismic wavelets into the ground. This is a blind deconvolution process since the seismic wavelets are usually not measurable and therefore, the subsurface face layers are identified only by the reflected seismic signals. Conventional methods often approach this problem by making assumptions about the subsurface structures and/or the seismic wavelets. In this paper an alternative technique is presented. It applies blind channel identification methods to prestack seismic deconvolution. A unique feature of this proposed method is that no such assumptions are needed. In addition, it fits into the structure of current seismic data acquisition techniques, thus no extra cost is involved. Simulations on both synthetic and field seismic data demonstrate that it is a promising new method for seismic signal processing.

Prediction error method for second-order blind identification

Blind channel identification methods based on the oversampled channel output are a problem of current theoretical and practical interest. In this paper, we introduce a second-order blind identification technique based on a linear prediction approach. In contrast to eigenstructure-based methods, it will be shown that the linear prediction error method is robust to order overdetermination. An asymptotic performance analysis of the proposed estimation method is carried out, consistency and asymptotic normality of the estimates is established. A closed-form expression for the asymptotic covariance of the estimates is given. Numerical simulations and investigations are finally presented to demonstrate the potential and the robustness of the proposed method.

Source correlation compensation for blind channel identification based on second-order statistics

Presents a source correlation compensation approach for the blind channel identification method proposed by Tong et al. [1991, 1994]. Instead of assuming independent and identically distributed (i.i.d.) source symbols as assumed by Tong et al., the present authors consider the case where each source symbol is weakly correlated to its nearest neighbors with an unknown correlation coefficient. They show how Tong's method can be simply modified so that the unknown correlation coefficient can be estimated and then compensated, which leads to an improved performance. >

Blind identification and equalization based on second-order statistics: 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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>