Victor Barroso - Profile on Academia.edu (original) (raw)
Papers by Victor Barroso
A state space model for non directional Random fields
Dans la theorie du processement optimal d'antennes on représente généralement les champs aléa... more Dans la theorie du processement optimal d'antennes on représente généralement les champs aléatoires comme des processus stochastiques à deux indices stationnaires et homogènes. Avec ces représentations, l'antenne processe la fonction de covariance spatial et temporelle du champ réçu ou sa transformée de Eourier,c'est à dire, la fonction de fréquence/numéro d'onde. Pour obtenir des algorithmes récurrents, on modèle les champs réçus comme des sorties des systèmes stochastiques distribués. Dans le cas où on a des champs aléatoires directionels, modèles d'état on été fréquentment usés. Dans cet article, on considère le problème du modelage des champs aléatoires non stationnaires, homogènes et non directionels. Avec l'hypothèse de connaissance de la fonction de covariance espace/tenp, on demande que la représentation soit valide sur une antenne linéaire de longueur L. Pour représenter le champ aléatoire non directionel, on recourt à une série de Fourier spatial tronquée. En faisant usage du fait que cette série converge en moyenne quadratique sur la ligne dont la longueur est L, on mesure le degré d'approximation par l'erreur quadratique moyen. Les coefficients de la série sont des processus stochastiques temporels non stationnaires et correlés; ce processus vectoriel peut être interprété comme la sortie d'un système dynamique linéaire, à paramètres variables, avec des entrées stochastiques
Doppler-Free Digital Communications
| The existence of doppler has always been an inconvenience for digital communications. Several s... more | The existence of doppler has always been an inconvenience for digital communications. Several schemes have been proposed to deal with the problem, often involving some kind of doppler tracking and/or compensation algorithm. All these schemes do, however, increase the complexity of the receiver. Furthermore, the need for exactness in the doppler tracking scheme adds an extra dimension to the space of potential failures. In this article, we will follow a di erent approach. By using time-frequency methods in the detection/decoding stage, we will be able to design receivers which are, in fact, doppler insensitive. The existence or non-existence of doppler e ect becomes irrelevant as far as the receiver performance
Blind Multi-channel Identification
We address the problem of joint source symbol detection and multi-channel estimation in time-sele... more We address the problem of joint source symbol detection and multi-channel estimation in time-selective digital communication scenarios. Our approach is based on a statistical model which decouples the time dynamics of the multi-channel vector in amplitude and direction. We compute the most probable emitted symbol sequence and channel realization for this statistical model, given the set of array observations. Our maximum a posterior (MAP) receiver consists of a bank of parallel processors. Each processor finds the most probable channel realization for a given symbol sequence via a relaxed semidefinite programming (SDP) re-formulation of the original estimation problem. Computer simulations are included to assess the capability of our technique in acquiring fast-changing flat-fading channels.
The squared distance function is one of the standard functions on which an optimization algorithm... more The squared distance function is one of the standard functions on which an optimization algorithm is commonly run, whether it is used directly or chained with other functions. Illustrative examples include center of mass computation, implementation of k-means algorithm and robot positioning. This function can have a simple expression (as in the Euclidean case), or it might not even have a closed form expression. Nonetheless, when used in an optimization problem formulated on non-Euclidean manifolds, the appropriate (intrinsic) version must be used and depending on the algorithm, its gradient and/or Hessian must be computed. For many commonly used manifolds a way to compute the intrinsic distance is available as well as its gradient, the Hessian however is usually a much more involved process, rendering Newton methods unusable on many standard manifolds. This article presents a way of computing the Hessian on connected locally-symmetric spaces on which standard Riemannian operations ...
Blind equalization using a radial basis function neural network
'Challenges of Our Changing Global Environment'. Conference Proceedings. OCEANS '95 MTS/IEEE
Nonlinear filters based on neural networks can be used for adaptive signal processing in a wide r... more Nonlinear filters based on neural networks can be used for adaptive signal processing in a wide range of applications, e.g. underwater acoustic communications. In this paper, a radial basis function (RBF) neural network is used for blind adaptive equalization with higher order statistics. The RBF network proposed in this paper has several features which make it a suitable structure for
Abstract—We address the problem of joint source symbol detection and multi-channel estimation in ... more Abstract—We address the problem of joint source symbol detection and multi-channel estimation in time-selective digital communication scenarios. Our approach is based on a statistical model which decouples the time dy-namics of the multi-channel vector in amplitude and direction. We com-pute the most probable emitted symbol sequence and channel realization for this statistical model, given the set of array observations. Our maxi-mum a posterior (MAP) receiver consists of a bank of parallel processors. Each processor finds the most probable channel realization for a given sym-bol sequence via a relaxed semidefinite programming (SDP) re-formulation of the original estimation problem. Computer simulations are included to assess the capability of our technique in acquiring fast-changing flat-fading channels. I. PROBLEM FORMULATION C ONSIDER a wireless communication scenario in which a multiple antenna receiver observes a mobile digital source, as depicted in figure 1. The source transmi...
Blind Channel Identification and Source Separation in Space Division Multiple Access Systems
We address the problem of space-time codebook design for non-coherent communications in multiple-... more We address the problem of space-time codebook design for non-coherent communications in multiple-antenna wireless systems. In contrast with other approaches, the channel matrix is modeled as an unknown deterministic parameter at both the receiver and the transmitter, and the Gaussian observation noise is allowed to have an arbitrary correlation structure, known by the transmitter and the receiver. In order to handle the unknown deterministic space-time channel, a generalized likelihood ratio test (GLRT) receiver is considered. A new methodology for space-time codebook design under this non-coherent setup is proposed. It optimizes the probability of error of the GLRT receiver’s detector in the high signal-to-noise ratio (SNR) regime by solving a high-dimensional nonlinear non-smooth optimization problem in a two-step approach. (i) Firstly, a convex semidefinite programming (SDP) relaxation of the codebook design problem yields a rough estimate of the optimal codebook. (ii) This is th...
Using an RBF Network for Blind Equalization: Design and Performance Evaluation
The design of adaptive equalizers is an important topic for practical implementation of ecient di... more The design of adaptive equalizers is an important topic for practical implementation of ecient digital communica-tions. In this paper, the application of a radial basis func-tion neural network (RBF) for blind channel equalization is analysed. This architecture is well suited for equalization of nite impulse response (FIR) channels partly because the network model closely matches the data model. This al-lows a rather straightforward design of an optimal receiver, in a Bayesian sense. It also provides a simple framework for data classication, in which more complex nonlinear distor-tions can be accomodated with virtually no modications. A clustering algorithm for dynamic creation and combina-tion of local units is proposed, which eliminates the need for channel order estimation. An initialization procedure for the output linear layer is also presented. The network performance is illustrated with Monte Carlo simulations for a family of random channels. 1.
2 DETERMINISTIC METHODS 9 2.1 INSTANTANEOUS MIXTURES . . . . . . . . . . . . . . . . . . . . . . ... more 2 DETERMINISTIC METHODS 9 2.1 INSTANTANEOUS MIXTURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.1 ILSP and ILSE algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.2 Analytical constant modulus algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.3 Closed form solution based on linear coding . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2 SUBSPACE METHOD FOR ISI CANCELLATION . . . . . . . . . . . . . . . . . . . . . . . . . . 22
This paper examines an extension of QR-RLS lattice filtering to the case of multiple input signal... more This paper examines an extension of QR-RLS lattice filtering to the case of multiple input signals. The proposed adaptation algorithm is based on a compact square-root array formulation that is amenable to hardware implementation due to its scalar-only nature, parallelizability and numerical robustness. A technique that provides increased design flexibility by allowing unequal filter lengths to be specified for different input channels is also presented.
Journal of Communication and Information Systems, 2003
We study how ¾nd order statistics (SOS) can be exploited in two signal processing problems, blind... more We study how ¾nd order statistics (SOS) can be exploited in two signal processing problems, blind separation of binary sources and trained-based multiuser channel identification, in a Bayesian context where a prior on the mixing channel matrix is available. It is well known that the SOS of the received data permit to resolve the unknown mixing matrix, up to an orthogonal factor. In a Bayesian framework, this residual orthogonal mixing matrix becomes a random object in its own right, with an associated distribution over the group of orthogonal matrices. This distribution is induced by the prior on the mixing matrix, and must be known for optimum statistical processing. We rely on a previous theoretical work to provide these answers, and discuss applications for this induced probability density function (pdf) over the orthogonal group, in the two aforementioned signal processing problems. Preliminary results, obtained through computer simulations, demonstrate the effectiveness of incorporating this induced distribution associated with the residual orthogonal matrix into the design of several estimators.
This report describes the ISR-IST contribution for the high-speed acoustic data link that was dev... more This report describes the ISR-IST contribution for the high-speed acoustic data link that was developed as part of the ASIMOV project. This contribution consists of several C software modules that implement most of the functionality of a modem, from physicallevel synchronization and filtering to top-level data framing. This software was integrated with ORCA Instrumentation driver modules that interact with a custom-developed board based on a low-power Texas Instruments fixed-point TMS320C54x DSP. As the modem software is relatively complex, documenting it from the strict perspective of software engineering is somewhat inadequate. Accordingly, this report addresses several issues, such as the transmitter and receiver signal processing structure, data format, software organization, and software parametrization. Section 1.3 provides an outline of this report.
We study how ¾nd order statistics (SOS) can be exploited in two signal processing problems, blind... more We study how ¾nd order statistics (SOS) can be exploited in two signal processing problems, blind separation of binary sources and trained-based multiuser channel identification, in a Bayesian context where a prior on the mixing channel matrix is available. It is well known that the SOS of the received data permit to resolve the unknown mixing matrix, up to an orthogonal factor. In a Bayesian framework, this residual orthogonal mixing matrix becomes a random object in its own right, with an associated distribution over the group of orthogonal matrices. This distribution is induced by the prior on the mixing matrix, and must be known for optimum statistical processing. We rely on a previous theoretical work to provide these answers, and discuss applications for this induced probability density function (pdf) over the orthogonal group, in the two aforementioned signal processing problems. Preliminary results, obtained through computer simulations, demonstrate the effectiveness of incorporating this induced distribution associated with the residual orthogonal matrix into the design of several estimators.
— We study how 2nd order statistics (SOS) can be exploited in two signal processing problems, bli... more — We study how 2nd order statistics (SOS) can be exploited in two signal processing problems, blind separation of binary sources and trained-based multi-user channel identification, in a Bayesian context where a prior on the mixing channel matrix is available. It is well known that the SOS of the received data permit to resolve the unknown mixing matrix, up to an orthogonal factor. In a Bayesian framework, this residual orthogonal mixing matrix becomes a random object in its own right, with an associated distribution over the group of orthogonal matrices. This distri-bution is induced by the prior on the mixing matrix, and must be known for optimum statistical processing. We rely on a previous theoretical work to provide these answers, and discuss applications for this induced probabil-ity density function (pdf) over the orthogonal group, in the two aforemen-tioned signal processing problems. Preliminary results, obtained through computer simulations, demonstrate the effectiveness o...
Ieee Transactions on Signal Processing, Jun 1, 2001
We present a blind closed-form consistent channel estimator for multiple-input multiple-output (M... more 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 recently introduced transmitter-induced conjugate cyclostationarity approach when there are carrier frequency misadjustments.
An Analytical Solution for 2nd Order Statistics Based Blind MIMO Channel Identification
ABSTRACT
Source independent blind equalization with fractionally-spaced sampling
1996 8th European Signal Processing Conference, Sep 1, 1996
Array-Based QR-RLS Multichannel Lattice Filtering
Ieee Transactions on Signal Processing, Aug 1, 2008
AbstractAn array-based algorithm for multichannel lattice filtering is proposed. The filter is f... more AbstractAn array-based algorithm for multichannel lattice filtering is proposed. The filter is formed by a set of units that are adapted locally and concurrently using recursions that closely match those for single-channel lattice filters. The design, based on a known modular ...
A state space model for non directional Random fields
Dans la theorie du processement optimal d'antennes on représente généralement les champs aléa... more Dans la theorie du processement optimal d'antennes on représente généralement les champs aléatoires comme des processus stochastiques à deux indices stationnaires et homogènes. Avec ces représentations, l'antenne processe la fonction de covariance spatial et temporelle du champ réçu ou sa transformée de Eourier,c'est à dire, la fonction de fréquence/numéro d'onde. Pour obtenir des algorithmes récurrents, on modèle les champs réçus comme des sorties des systèmes stochastiques distribués. Dans le cas où on a des champs aléatoires directionels, modèles d'état on été fréquentment usés. Dans cet article, on considère le problème du modelage des champs aléatoires non stationnaires, homogènes et non directionels. Avec l'hypothèse de connaissance de la fonction de covariance espace/tenp, on demande que la représentation soit valide sur une antenne linéaire de longueur L. Pour représenter le champ aléatoire non directionel, on recourt à une série de Fourier spatial tronquée. En faisant usage du fait que cette série converge en moyenne quadratique sur la ligne dont la longueur est L, on mesure le degré d'approximation par l'erreur quadratique moyen. Les coefficients de la série sont des processus stochastiques temporels non stationnaires et correlés; ce processus vectoriel peut être interprété comme la sortie d'un système dynamique linéaire, à paramètres variables, avec des entrées stochastiques
Doppler-Free Digital Communications
| The existence of doppler has always been an inconvenience for digital communications. Several s... more | The existence of doppler has always been an inconvenience for digital communications. Several schemes have been proposed to deal with the problem, often involving some kind of doppler tracking and/or compensation algorithm. All these schemes do, however, increase the complexity of the receiver. Furthermore, the need for exactness in the doppler tracking scheme adds an extra dimension to the space of potential failures. In this article, we will follow a di erent approach. By using time-frequency methods in the detection/decoding stage, we will be able to design receivers which are, in fact, doppler insensitive. The existence or non-existence of doppler e ect becomes irrelevant as far as the receiver performance
Blind Multi-channel Identification
We address the problem of joint source symbol detection and multi-channel estimation in time-sele... more We address the problem of joint source symbol detection and multi-channel estimation in time-selective digital communication scenarios. Our approach is based on a statistical model which decouples the time dynamics of the multi-channel vector in amplitude and direction. We compute the most probable emitted symbol sequence and channel realization for this statistical model, given the set of array observations. Our maximum a posterior (MAP) receiver consists of a bank of parallel processors. Each processor finds the most probable channel realization for a given symbol sequence via a relaxed semidefinite programming (SDP) re-formulation of the original estimation problem. Computer simulations are included to assess the capability of our technique in acquiring fast-changing flat-fading channels.
The squared distance function is one of the standard functions on which an optimization algorithm... more The squared distance function is one of the standard functions on which an optimization algorithm is commonly run, whether it is used directly or chained with other functions. Illustrative examples include center of mass computation, implementation of k-means algorithm and robot positioning. This function can have a simple expression (as in the Euclidean case), or it might not even have a closed form expression. Nonetheless, when used in an optimization problem formulated on non-Euclidean manifolds, the appropriate (intrinsic) version must be used and depending on the algorithm, its gradient and/or Hessian must be computed. For many commonly used manifolds a way to compute the intrinsic distance is available as well as its gradient, the Hessian however is usually a much more involved process, rendering Newton methods unusable on many standard manifolds. This article presents a way of computing the Hessian on connected locally-symmetric spaces on which standard Riemannian operations ...
Blind equalization using a radial basis function neural network
'Challenges of Our Changing Global Environment'. Conference Proceedings. OCEANS '95 MTS/IEEE
Nonlinear filters based on neural networks can be used for adaptive signal processing in a wide r... more Nonlinear filters based on neural networks can be used for adaptive signal processing in a wide range of applications, e.g. underwater acoustic communications. In this paper, a radial basis function (RBF) neural network is used for blind adaptive equalization with higher order statistics. The RBF network proposed in this paper has several features which make it a suitable structure for
Abstract—We address the problem of joint source symbol detection and multi-channel estimation in ... more Abstract—We address the problem of joint source symbol detection and multi-channel estimation in time-selective digital communication scenarios. Our approach is based on a statistical model which decouples the time dy-namics of the multi-channel vector in amplitude and direction. We com-pute the most probable emitted symbol sequence and channel realization for this statistical model, given the set of array observations. Our maxi-mum a posterior (MAP) receiver consists of a bank of parallel processors. Each processor finds the most probable channel realization for a given sym-bol sequence via a relaxed semidefinite programming (SDP) re-formulation of the original estimation problem. Computer simulations are included to assess the capability of our technique in acquiring fast-changing flat-fading channels. I. PROBLEM FORMULATION C ONSIDER a wireless communication scenario in which a multiple antenna receiver observes a mobile digital source, as depicted in figure 1. The source transmi...
Blind Channel Identification and Source Separation in Space Division Multiple Access Systems
We address the problem of space-time codebook design for non-coherent communications in multiple-... more We address the problem of space-time codebook design for non-coherent communications in multiple-antenna wireless systems. In contrast with other approaches, the channel matrix is modeled as an unknown deterministic parameter at both the receiver and the transmitter, and the Gaussian observation noise is allowed to have an arbitrary correlation structure, known by the transmitter and the receiver. In order to handle the unknown deterministic space-time channel, a generalized likelihood ratio test (GLRT) receiver is considered. A new methodology for space-time codebook design under this non-coherent setup is proposed. It optimizes the probability of error of the GLRT receiver’s detector in the high signal-to-noise ratio (SNR) regime by solving a high-dimensional nonlinear non-smooth optimization problem in a two-step approach. (i) Firstly, a convex semidefinite programming (SDP) relaxation of the codebook design problem yields a rough estimate of the optimal codebook. (ii) This is th...
Using an RBF Network for Blind Equalization: Design and Performance Evaluation
The design of adaptive equalizers is an important topic for practical implementation of ecient di... more The design of adaptive equalizers is an important topic for practical implementation of ecient digital communica-tions. In this paper, the application of a radial basis func-tion neural network (RBF) for blind channel equalization is analysed. This architecture is well suited for equalization of nite impulse response (FIR) channels partly because the network model closely matches the data model. This al-lows a rather straightforward design of an optimal receiver, in a Bayesian sense. It also provides a simple framework for data classication, in which more complex nonlinear distor-tions can be accomodated with virtually no modications. A clustering algorithm for dynamic creation and combina-tion of local units is proposed, which eliminates the need for channel order estimation. An initialization procedure for the output linear layer is also presented. The network performance is illustrated with Monte Carlo simulations for a family of random channels. 1.
2 DETERMINISTIC METHODS 9 2.1 INSTANTANEOUS MIXTURES . . . . . . . . . . . . . . . . . . . . . . ... more 2 DETERMINISTIC METHODS 9 2.1 INSTANTANEOUS MIXTURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.1 ILSP and ILSE algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.2 Analytical constant modulus algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.3 Closed form solution based on linear coding . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2 SUBSPACE METHOD FOR ISI CANCELLATION . . . . . . . . . . . . . . . . . . . . . . . . . . 22
This paper examines an extension of QR-RLS lattice filtering to the case of multiple input signal... more This paper examines an extension of QR-RLS lattice filtering to the case of multiple input signals. The proposed adaptation algorithm is based on a compact square-root array formulation that is amenable to hardware implementation due to its scalar-only nature, parallelizability and numerical robustness. A technique that provides increased design flexibility by allowing unequal filter lengths to be specified for different input channels is also presented.
Journal of Communication and Information Systems, 2003
We study how ¾nd order statistics (SOS) can be exploited in two signal processing problems, blind... more We study how ¾nd order statistics (SOS) can be exploited in two signal processing problems, blind separation of binary sources and trained-based multiuser channel identification, in a Bayesian context where a prior on the mixing channel matrix is available. It is well known that the SOS of the received data permit to resolve the unknown mixing matrix, up to an orthogonal factor. In a Bayesian framework, this residual orthogonal mixing matrix becomes a random object in its own right, with an associated distribution over the group of orthogonal matrices. This distribution is induced by the prior on the mixing matrix, and must be known for optimum statistical processing. We rely on a previous theoretical work to provide these answers, and discuss applications for this induced probability density function (pdf) over the orthogonal group, in the two aforementioned signal processing problems. Preliminary results, obtained through computer simulations, demonstrate the effectiveness of incorporating this induced distribution associated with the residual orthogonal matrix into the design of several estimators.
This report describes the ISR-IST contribution for the high-speed acoustic data link that was dev... more This report describes the ISR-IST contribution for the high-speed acoustic data link that was developed as part of the ASIMOV project. This contribution consists of several C software modules that implement most of the functionality of a modem, from physicallevel synchronization and filtering to top-level data framing. This software was integrated with ORCA Instrumentation driver modules that interact with a custom-developed board based on a low-power Texas Instruments fixed-point TMS320C54x DSP. As the modem software is relatively complex, documenting it from the strict perspective of software engineering is somewhat inadequate. Accordingly, this report addresses several issues, such as the transmitter and receiver signal processing structure, data format, software organization, and software parametrization. Section 1.3 provides an outline of this report.
We study how ¾nd order statistics (SOS) can be exploited in two signal processing problems, blind... more We study how ¾nd order statistics (SOS) can be exploited in two signal processing problems, blind separation of binary sources and trained-based multiuser channel identification, in a Bayesian context where a prior on the mixing channel matrix is available. It is well known that the SOS of the received data permit to resolve the unknown mixing matrix, up to an orthogonal factor. In a Bayesian framework, this residual orthogonal mixing matrix becomes a random object in its own right, with an associated distribution over the group of orthogonal matrices. This distribution is induced by the prior on the mixing matrix, and must be known for optimum statistical processing. We rely on a previous theoretical work to provide these answers, and discuss applications for this induced probability density function (pdf) over the orthogonal group, in the two aforementioned signal processing problems. Preliminary results, obtained through computer simulations, demonstrate the effectiveness of incorporating this induced distribution associated with the residual orthogonal matrix into the design of several estimators.
— We study how 2nd order statistics (SOS) can be exploited in two signal processing problems, bli... more — We study how 2nd order statistics (SOS) can be exploited in two signal processing problems, blind separation of binary sources and trained-based multi-user channel identification, in a Bayesian context where a prior on the mixing channel matrix is available. It is well known that the SOS of the received data permit to resolve the unknown mixing matrix, up to an orthogonal factor. In a Bayesian framework, this residual orthogonal mixing matrix becomes a random object in its own right, with an associated distribution over the group of orthogonal matrices. This distri-bution is induced by the prior on the mixing matrix, and must be known for optimum statistical processing. We rely on a previous theoretical work to provide these answers, and discuss applications for this induced probabil-ity density function (pdf) over the orthogonal group, in the two aforemen-tioned signal processing problems. Preliminary results, obtained through computer simulations, demonstrate the effectiveness o...
Ieee Transactions on Signal Processing, Jun 1, 2001
We present a blind closed-form consistent channel estimator for multiple-input multiple-output (M... more 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 recently introduced transmitter-induced conjugate cyclostationarity approach when there are carrier frequency misadjustments.
An Analytical Solution for 2nd Order Statistics Based Blind MIMO Channel Identification
ABSTRACT
Source independent blind equalization with fractionally-spaced sampling
1996 8th European Signal Processing Conference, Sep 1, 1996
Array-Based QR-RLS Multichannel Lattice Filtering
Ieee Transactions on Signal Processing, Aug 1, 2008
AbstractAn array-based algorithm for multichannel lattice filtering is proposed. The filter is f... more AbstractAn array-based algorithm for multichannel lattice filtering is proposed. The filter is formed by a set of units that are adapted locally and concurrently using recursions that closely match those for single-channel lattice filters. The design, based on a known modular ...