A cumulant matrix subspace algorithm for blind single FIR channel identification

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.

Most proposed space-time codec schemes focus on the single-user scenario, which cannot be applied directly in mobile communication systems. Hence, the paper focuses on a multiuser system with multi-transmit-multi-receive antennas in the frequency-selective fading environment. A vertical Bell Labs layered space-time (V-BLAST) coded direct-sequence code-division multiple-access (DS-CDMA) scheme and its blind channel identification algorithm are proposed. By exploiting an ESPRIT-like method and singular value decomposition, the channels between every transmit antenna of every user and every receive antenna of the base station can be estimated with a closed-form solution. The channel identification algorithm includes two steps and reduces the computational complexity dramatically.

This paper studies the blind identification problem for multiple-input multiple-output (MIMO) FIR channels, in which the source signals are modulated by high order periodic precoders. A set of new design criteria of the periodic precoders is proposed from the channel blind identification viewpoint for multiuser channels. It is shown that, due to the fact that high order periodic precoders are used in the modelling problem, certain simple orthogonality among user’s signals can be introduced by a set of proper designed periodic precoders. This results in a simple blind identification algorithm and the impulse response of every subchannel can also be determined uniquely up to a unitary scalar based on the received signals’ second order statistics (SOS) and a priori information of the periodic precoders. Simulation results illustrate that the developed blind identification algorithm is efficient.

This paper addresses the problem radio mobile channel identification and equalization by blind algorithms. For this, we have proposed two algorithms, which are compared with: cumulants based algorithms, and adaptive filter algorithms (such us the constant modulus algorithm (CMA), Fractional Space CMA (FSCMA) and Sign Kurtosis Maximization Adaptive Algorithm (SKMAA)). However, The simulation results in noisy environment and for different SNR values with QPSK or 4QAM modulation shows that the proposed algorithms gives a good identification and equalization versus to the cumulants and the adaptive filter algorithms, for radio mobile channel.

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.

A class of adaptive algorithms for acoustic echo cancellation (AEC) and speech dereverberation have been developed and analyzed in this thesis. The starting point of this work is the affine projection (AP) based non-blind channel identification algorithms for AEC. The proposed. sparseness constrained improved proportionate AP algorithms (SC-IPAPA-I and SC-IPAPA-II) exploit the sparseness of the estimated channel and allocate different effective step sizes accordingly. The performance of these algorithms has been studied in the context of single-channel AEC and tracking capability. The performance of a blind channel identification algorithm with additive noise is studied by providing reasons for the degradation in perforrnance of the rnultichannel least-rnean-square (MCLMS) algorithm in the presence of additive noise. Subsequently, it is shown mathematically, through a cross-correlation based cost function, that minimizing the power of a filtered version of the received signals can suppress the noise effect. The performance of MCLMS and improved lVICLlVIS (LVICLMS) are evaluated using the normalized projection rnisalignment via lVlonte Carlo simulations. It is shown through simulations that the well-known normalized rnultichannel frequency-domain least-mean-square (NMCFLMS) algorithrn, originally developed for acoustic impulse response (AIR) identification in the frequency domain, also suffers from the noise robustness problem. A noise robust blind AIR estima.tion algorithm is then proposed. Inspired by the trivial solution achieved by NMCFLJVIS in the presence of noise, the proposed direct-path NMCFLMS algorithm with power constraint (DP-NMCFLMS-PC) jointly applies direct-path and power constraint to improve its robustness against noise. The DP-NMCLFMS-PC not only addresses the noise robustness issue but also achieves fast convergence compared to NMCFLMS. The adaptive multiple input/output inversion theorem (A-MINT) algorithm has been developed for channel equalization with application to speech dereverberation. In order to increase its convergence rate, the proposed algorithm suppresses any undesired non-zero coefficients in the estimated Kronecker delta function iteratively. This is achieved by applying the sparseness measure of the estimated Kronecker delta function and using it as an additional constraint to A-MINT. Unlike existing channel equalization systems, the proposed auto-relation aided MINT (A-RAM) algorithrn, which achieves good equalization performance, takes into account how the received signals are generated during its adaptation process. The differential output signals from two sub-systems are then utilized in the cost function during equalization. Sirnulation results have shown that the proposed A-RAM algorithm can achieve a higher rate of convergence leading to better dereverberated speech signal cornpared to existing MINT-based equalization algorithms.

Relying upon the diversity introduced by fractional-sampling or by multiple sensors, two different approaches for blind adaptive channel identification are proposed based on existing batch algorithms. We show that the adaptive approach allows tracking of time-variations in the channel and provides robustness to nearly common channel roots. Existing second-order adaptive procedures typically employ suboptimal linear equalizers whereas the adaptive channel estimators we propose can be used in an optimal maximum likelihood sequence estimator. Simulations are provided that demonstrate the performance of the proposed channel estimators when used in the Viterbi algorithm for symbol detection.

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.

We propose a subspace blind channel identification algorithm based on CGM (conjugate gradient method). The algorithm estimates (1) the channel order, (2) the noise variance, (3) the noise subspace, and then identifies (4) channel impulse response without using the eigenvalue decomposition. The special features of the proposed algorithm are (1) accurate channel order estimation and (2) the reduction of computational complexity using CGM. Numerical examples show the effectiveness of the proposed 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>

We investigate a new approach for blind identification of multiple FIR channels that appears to be robust to channel length overestimation. The linear prediction approach proposed in Slock (1994) constitutes a robust approach since it provides a consistent channel estimate if the channel order is overestimated. We focus here on methods that are parameterized by the channel directly such as the (signal) subspace fitting technique. To make the optimization criterion in these approaches well defined, a constraint on the channel coefficients has to be added. Typically, the unit norm constraint is used. It is the use of this constraint that leads to order overestimation problems. In our approach we replace this constraint by a unit norm for only the first vector coefficient of the vector channel. Our simulations demonstrate that the channel estimate obtained in this way is robust to order overestimation. Furthermore, if the exact quantities are used in the optimization criterion, the proposed channel estimate is the correct channel (up to the usual scaling factor) even if the order is overestimated. Hence, our channel estimate is consistent even with order overestimation.

We propose a blind FIR channel identification method based on the parallel factor (Parafac) analysis of a 3rd-order tensor composed of the 4-th order output cumulants. Our algorithm is based on a single-step least squares (LS) minimization procedure instead of using classical three-step alternating least squares (ALS) methods. Using a Parafac-based decomposition, we avoid any kind of pre-processing such as the prewhitening operation, which is mandatory in most methods using higher-order statistics. Our method retrieves the channel vector without any permutation or scaling ambiguities. In addition, we establish a link between the cumulant tensor decomposition and the joint-diagonalization approach. Computer simulations illustrate the performance gains that our method provides with respect to other classical solutions. Initialization and convergence issues are also addressed.

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.

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

This paper describes a blind crosstalk channel identification method and its application to discrete multitone (DMT)-based digital subscriber line (DSL) systems. Cyclostationarity inherited in these systems is exploited to isolate effects of different sources, which allows separate selection and identification of crosstalk channels. Second-order statistics-based source separation is performed by controlling part of the cyclic-extension in the transmitted DMT block. The minimum changes required by this method facilitate its implementation in current DSL systems. All mentioned theoretical derivations are illustrated by numerical results obtained with measured DSL channels.