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ABSTRACT: Blind channel estimation techniques were developed and usually evaluated for a given channel realization, i.e. with a deterministic channel model. On the other hand, in wireless communications the channel is typically modeled as Rayleigh fading, i.e. with a Gaussian (prior) distribution expressing variances of and correlations between channel coefficients. In this paper we explore a Bayesian approach to blind channel estimation, exploiting a priori information on fading channels. We mainly focus on joint ML/MAP estimation of channels and symbols on one hand, and on ML/MAP estimation of channels with elimination of symbols on the other hand. As a consequence, a unified framework in addition to three new Bayesian estimators are introduced where their performance is compared by simulations to three existing non-Bayesian estimators. In the same context, we provide an insightful discussion of the accurate way of deriving the Bayesian Cramer Rao bound (BCRB) with an emphasis on its singularity.
Statistical Signal Processing Workshop (SSP), 2011 IEEE; 07/2011
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ABSTRACT: Traditionally, the performance of blind SIMO channel estimates has been characterized in a deterministic fashion, by identifying those channel realizations that are not blindly identifiable. In this paper, we focus instead on the performance of Linear Equalizers for fading channels when they are based on blind channel estimates. Our analysis shows that with Zero Forcing Linear Equalizer (ZF-LE) at least one order of the diversity is lost depending on the way by which the scalar ambiguity that results from the blind channel estimation is resolved. However, in some Tx scenarios we are able to recover the diversity with MMSE-LE. Various Tx scenarios are considered in detail.
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on; 04/2010 · 4.63 Impact Factor
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ABSTRACT: Blind and semiblind channel estimation is a topic that enjoyed explosive developments throughout the nineties, and then came to a standstill, probably because of perceived unsatisfactory performance. Blind channel estimation techniques were developed and usually evaluated for a given channel realization, i.e. with a deterministic channel model. Such blind channel estimates, especially those based on subspaces in the data, are often only partial and ill-conditioned. On the other hand, in wireless communications the channel is typically modeled as Rayleigh fading, i.e. with a Gaussian (prior) distribution expressing variances of and correlations between channel coefficients. In recent years, such prior information on the channel has started to get exploited in pilot-based channel estimation, since often the pure pilot-based (deterministic) channel estimate is of limited quality due to limited pilots. In this paper we explore a Bayesian approach to (semi-)blind channel estimation, exploiting a priori information on fading channels. In the case of deterministic unknown input symbols, it suffices to augment the classical blind (quadratic) channel criterion with a quadratic criterion reflecting the Rayleigh fading prior. In the case of a Gaussian symbol model the blind criterion is more involved. The joint ML/MAP estimation of channels, deterministic unknown symbols, and channel profile parameters can be conveniently carried out using Variational Bayesian techniques. Variational Bayesian techniques correspond to alternating maximization of a likelihood w.r.t. subsets of parameters, but taking into account the estimation errors on the other parameters. To simplify exposition, we elaborate the details for the case of MIMO OFDM systems.
Communications, Control and Signal Processing (ISCCSP), 2010 4th International Symposium on; 04/2010
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ABSTRACT: Traditionally, the performance of blind SIMO channel estimates has been characterized in a deterministic fashion, by identifying those channel realizations that are not blindly identifiable. In this paper, we focus instead on the performance of Zero-Forcing (ZF) Linear Equalizers (LEs) or Decision-Feedback Equalizers (DFEs) for fading channels when they are based on (semi-)blind channel estimates. Although it has been known that various (semi-)blind channel estimation techniques have a receiver counterpart that is matched in terms of symbol knowledge hypotheses, we show here that these (semi-)blind techniques and corresponding receivers also match in terms of diversity order: the channel becomes (semi-)blindly unidentifiable whenever its corresponding receiver structure goes in outage. In the case of mismatched receiver and (semi-blind) channel estimation technique, the lower diversity order dominates. Various cases of (semi-)blind channel estimation and corresponding receivers are considered in detail. To be complete however, the actual combination of receiver and (semi-)blind channel estimation lowers somewhat the diversity order w.r.t. the ideal picture.
Communications, Control and Signal Processing (ISCCSP), 2010 4th International Symposium on; 04/2010
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ABSTRACT: Multichannel aspect allows the introduction of blind channel estimation techniques. Most existing such techniques for frequency-selective channels are quite complex. In this paper, we consider the blind channel estimation problem for Single Input Multi Output (SIMO) cyclic prefix (CP) systems. We have shown before that blind channel estimation becomes computationally much more attractive and more straight forward to analyze in terms of performance in CP systems. Inspired by the iterative sample covariance matrix (SCM) structure enhancement techniques of Cadzow and others, we propose here an algorithm to structure the sample block circulant covariance matrix by enforcing two essential properties: rank and FIR structure. These two properties are exhibited by the true covariance matrix in the case of FIR SIMO channels with spatially white noise and CP transmission. The proposed enhancement procedure leads to an interesting enhanced SCM, even for the single CP symbol case. A simulation study for some classical channel estimators that depend on the SCM (with and without structuring) is presented, indicating that structuring allows for considerable performance gain in terms of the channel normalized mean square error (NMSE) over a wide SNR range.
Signal Processing Advances in Wireless Communications, 2009. SPAWC '09. IEEE 10th Workshop on; 07/2009
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ABSTRACT: We consider the blind multichannel dereverberation problem for a single source. We have shown before [5] that the single-input multi-output (SIMO) reverberation filter can be equalized blindly by applying MIMO Linear Prediction (LP) to its output (after SISO input pre-whitening). In this paper, we investigate the LP-based dereverberation in a noisy environment, and/or under acoustic channel length underestimation. Considering ambient noise and late reverberation as additive noises, we propose to introduce a postfilter that transforms the MIMO prediction filter into a somewhat longer equalizer. The postfilter allows to equalize to non-zero delay. Both MMSE-ZF and MMSE design criteria are considered here for the postfilter.We also focus here on computationally efficient (FFT based) block Toeplitz covariance matrix enhancement that enforces the SIMO filtered source plus white noise structure before applying MIMO LP. A second suggested refinement is an iterative refinement between SISO and MIMO LP. Simulations show that the proposed scheme is robust in noisy environments, and performs better compared to the classic Delay-&-Predict equalizer and the Delay-&-Sum beamformer.
Multimedia Signal Processing, 2008 IEEE 10th Workshop on; 11/2008
