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19
Multichannel Blind Identification: From Subspace to Maximum Likelihood Methods
 Proc. IEEE
, 1998
"... this paper is to review developments in blind channel identification and estimation within the estimation theoretical framework. We have paid special attention to the issue of identifiability, which is at the center of all blind channel estimation problems. Various existing algorithms are classified ..."
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Cited by 79 (2 self)
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this paper is to review developments in blind channel identification and estimation within the estimation theoretical framework. We have paid special attention to the issue of identifiability, which is at the center of all blind channel estimation problems. Various existing algorithms are classified into the momentbased and the maximum likelihood (ML) methods. We further divide these algorithms based on the modeling of the input signal. If input is assumed to be random with prescribed statistics (or distributions), the corresponding blind channel estimation schemes are considered to be statistical. On the other hand, if the source does not have a statistical description, or although the source is random but the statistical properties of the source are not exploited, the corresponding estimation algorithms are classified as deterministic. Fig. 2 shows a map for different classes of algorithms and the organization of the paper.
Multichannel Blind Deconvolution: Fir Matrix Algebra And Separation Of Multipath Mixtures
, 1996
"... A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and mat ..."
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Cited by 74 (0 self)
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A general tool for multichannel and multipath problems is given in FIR matrix algebra. With Finite Impulse Response (FIR) filters (or polynomials) assuming the role played by complex scalars in traditional matrix algebra, we adapt standard eigenvalue routines, factorizations, decompositions, and matrix algorithms for use in multichannel /multipath problems. Using abstract algebra/group theoretic concepts, information theoretic principles, and the Bussgang property, methods of single channel filtering and source separation of multipath mixtures are merged into a general FIR matrix framework. Techniques developed for equalization may be applied to source separation and vice versa. Potential applications of these results lie in neural networks with feedforward memory connections, wideband array processing, and in problems with a multiinput, multioutput network having channels between each source and sensor, such as source separation. Particular applications of FIR polynomial matrix alg...
Blind System Identification
, 1997
"... Blind system identification is a fundamental signal processing technology aimed to retrieve unknown information of a system from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation and blind image restoration. Th ..."
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Cited by 22 (1 self)
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Blind system identification is a fundamental signal processing technology aimed to retrieve unknown information of a system from its output only. This technology has a wide range of possible applications such as mobile communications, speech reverberation cancellation and blind image restoration. This paper reviews a number of recently developed concepts and techniques for blind system identification which include the concept of blind system identifiability in a deterministic framework, the blind techniques of maximum likelihood and subspace for estimating the system's impulse response, and other techniques for direct estimation of the system input. Keywords: System identification, Blind techniques, Multichannels, Equalization, Source separation. This work has been supported by the Australian Research Council and the Australian Cooperative Research Center for Sensor Signal and Information Processing. y Currently with Motorola Australian Research Centre, 12 Lord Street, Botany 2019, ...
Blind Channel Estimation and Data Detection Using Hidden Markov Models
 IEEE Trans. Signal Processing
, 1997
"... In this correspondence, we propose applying the hidden Markov models (HMM) theory to the problem of blind channel estimation and data detection. The BaumWelch (BW) algorithm, which is able to estimate all the parameters of the model, is enriched by introducing some linear constraints emerging from ..."
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Cited by 14 (1 self)
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In this correspondence, we propose applying the hidden Markov models (HMM) theory to the problem of blind channel estimation and data detection. The BaumWelch (BW) algorithm, which is able to estimate all the parameters of the model, is enriched by introducing some linear constraints emerging from a linear FIR hypothesis on the channel. Additionally, a version of the algorithm that is suitable for timevarying channels is also presented. Performance is analyzed in a GSM environment using standard test channels and is found to be close to that obtained with a nonblind receiver.
Maximum likelihood joint channel and data estimation using genetic algorithms
 IEEE Trans. Signal Processing
, 1998
"... Abstract — A batch blind equalization scheme is developed based on maximum likelihood joint channel and data estimation. In this scheme, the joint maximum likelihood optimization is decomposed into a twolevel optimization loop. A micro genetic algorithm is employed at the upper level to identify the ..."
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Cited by 9 (5 self)
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Abstract — A batch blind equalization scheme is developed based on maximum likelihood joint channel and data estimation. In this scheme, the joint maximum likelihood optimization is decomposed into a twolevel optimization loop. A micro genetic algorithm is employed at the upper level to identify the unknown channel model, and the Viterbi algorithm is used at the lower level to provide the maximum likelihood sequence estimation of the transmitted data sequence. As is demonstrated in simulation, the proposed method is much more accurate compared with existing algorithms for joint channel and data estimation. Index Terms—Blind equalization, genetic algorithms, maximum likelihood estimation. I.
EM Algorithm for Sequence Estimation over GaussMarkov ISI Channels
, 2000
"... This paper presents a new algorithm, based on an EM (ExpectationMaximization) formulation, for ML (maximum likelihood) sequence estimation over unknown ISI (intersymbol interference) channels with random channel coefficients which have a GaussMarkov fast timevarying distribution. By using the EM ..."
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Cited by 5 (4 self)
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This paper presents a new algorithm, based on an EM (ExpectationMaximization) formulation, for ML (maximum likelihood) sequence estimation over unknown ISI (intersymbol interference) channels with random channel coefficients which have a GaussMarkov fast timevarying distribution. By using the EM formulation to marginalize over the channel coefficient distribution, maximumlikelihood estimates of the transmitted sequence are obtained. This EM algorithm is shown to perform better, in terms of BER, than existing algorithms which perform jointlyoptimal sequence and channel estimation, or which do not take into account fast timevarying channel effects. I. Introduction Maximum Likelihood Sequence Estimation (MLSE) over an FIR channel with unknown coefficients can be formulated as either a channel estimation problem (followed by MLSE), a joint sequence/channel estimation problem, or a direct sequence estimation problem. Since MLSE is our primary concern, we consider only the latter two...
Optimal Joint Detection/Estimation in Fading Channels with Polynomial Complexity
 IEEE Trans. Inform. Theory
, 2003
"... The problem of sequence detection in frequencynonselective/timeselective fading channels, when channel state information (CSI) is not available at the transmitter and receiver, is considered in this paper. The traditional belief is that exact maximum likelihood sequence detection (MLSD) of an ..."
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Cited by 5 (1 self)
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The problem of sequence detection in frequencynonselective/timeselective fading channels, when channel state information (CSI) is not available at the transmitter and receiver, is considered in this paper. The traditional belief is that exact maximum likelihood sequence detection (MLSD) of an uncoded sequence over this channel has exponential complexity in the channel coherence time.
DecisionFeedback Equalization and Identification of Linear Channels Using Blind Algorithms of the Bussgang Type
 In Proc. 29th Asilomar Conference on Signals, Systems and Computers, volume II
, 1995
"... We consider the problem of blind equalization of linear communication channels. Some recent results indicate that the performance of Bussgang blind equalization algorithms can be improved by using diversity such as fractional spacing or antenna array reception. In this work we examine the performanc ..."
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Cited by 4 (1 self)
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We consider the problem of blind equalization of linear communication channels. Some recent results indicate that the performance of Bussgang blind equalization algorithms can be improved by using diversity such as fractional spacing or antenna array reception. In this work we examine the performance of such algorithms (especially of the popular CMA 22), when used in a decisionfeedback setup. It turns out that such a simple structure may help avoiding the common problems of "zeros on the unit circle" (symbolrate case) and of "zeros in common" (fractionallyspaced case). Theoretical analysis as well as computer simulations are provided in order to demonstrate this fact. 1 Introduction Blind equalization (BE) and channel identification is a field that has been receiving increased interest during the last years. Among several classes of methods, in this paper we are interested in the socalled Bussgang BE methods. These methods use a classical linear equalization scheme: the channel ...
On the Inclusion of Channel's Time Dependence in a Hidden Markov Model for Blind Channel Estimation
 in Proc. IEEE Statist. Signal Array Process. Workshop, Corfú
, 2001
"... In this paper, the theory of hidden Markov models (HMM) is applied to the problem of blind (without training sequences) channel estimation and data detection. Within a HMM framework, the BaumWelch (BW) identification algorithm is frequently used to find out maximumlikelihood (ML) estimates of the ..."
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Cited by 4 (1 self)
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In this paper, the theory of hidden Markov models (HMM) is applied to the problem of blind (without training sequences) channel estimation and data detection. Within a HMM framework, the BaumWelch (BW) identification algorithm is frequently used to find out maximumlikelihood (ML) estimates of the corresponding model. However, such a procedure assumes the model (i.e., the channel response) to be static throughout the observation sequence. By means of introducing a parametric model for timevarying channel responses, a version of the algorithm, which is more appropriate for mobile channels [timedependent BaumWelch (TDBW)] is derived. Aiming to compare algorithm behavior, a set of computer simulations for a GSM scenario is provided. Results indicate that, in comparison to other BaumWelch (BW) versions of the algorithm, the TDBW approach attains a remarkable enhancement in performance. For that purpose, only a moderate increase in computational complexity is needed.
EM Algorithms for Sequence Estimation over Random ISI Channels
, 1999
"... This paper presents a new approach using EM (ExpectationMaximization) algorithms for ML (maximum likelihood) sequence estimation over unknown ISI (intersymbol interference) channels with random channel coefficients. By using the EM formulation to marginalize over the channel coefficient distributio ..."
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Cited by 4 (2 self)
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This paper presents a new approach using EM (ExpectationMaximization) algorithms for ML (maximum likelihood) sequence estimation over unknown ISI (intersymbol interference) channels with random channel coefficients. By using the EM formulation to marginalize over the channel coefficient distribution, maximumlikelihood estimates of the transmitted sequenceare obtained. The EM algorithms are shown to perform better, in terms of BER, than existing algorithms which perform jointlyoptimal sequence and channel estimation. 1 Introduction In this paper we address the problem of estimation of a sequence of digital communication symbols transmitted over random ISI channels. For a known FIR channel, it is well known that the Viterbi algorithm [1] solves the ML sequence estimation (MLSE) problem. Although the computationally efficiency of this algorithm has lead to its broad use, the Viterbi algorithm requires knowledge of the channel (e.g. its impulse response) [2]. Here we are concerned wit...