The Experts below are selected from a list of 7737 Experts worldwide ranked by ideXlab platform
Faouzi Bellili - One of the best experts on this subject based on the ideXlab platform.
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generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior
arXiv: Information Theory, 2019Co-Authors: Faouzi Bellili, Foad SohrabiAbstract:This paper tackles the problem of millimeter-Wave (mmWave) channel estimation in massive MIMO communication systems. A new Bayes-optimal channel estimator is derived using recent advances in the approximate belief propagation (BP) Bayesian inference paradigm. By leveraging the inherent sparsity of the mmWave MIMO channel in the angular domain, we recast the underlying channel estimation problem into that of reconstructing a compressible signal from a set of noisy linear measurements. Then, the generalized approximate message passing (GAMP) algorithm is used to find the entries of the unknown mmWave MIMO channel matrix. Unlike all the existing works on the same topic, we model the angular-domain channel coefficients by Laplacian distributed random variables. Further, we establish the closed-form expressions for the various statistical quantities that need to be updated iteratively by GAMP. To render the proposed algorithm fully automated, we also develop an expectation-maximization (EM) based procedure that can be easily embedded within GAMP's Iteration Loop in order to learn all the unknown parameters of the underlying Bayesian inference problem. Computer simulations show that the proposed combined EM-GAMP algorithm under a Laplacian prior exhibits improvements both in terms of channel estimation accuracy, achievable rate, and computational complexity as compared to the Gaussian mixture prior that has been advocated in the recent literature. In addition, it is found that the Laplacian prior speeds up the convergence time of GAMP over the entire signal-to-noise ratio (SNR) range.
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generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior
IEEE Transactions on Communications, 2019Co-Authors: Faouzi Bellili, Foad SohrabiAbstract:This paper tackles the problem of millimeter-wave (mmWave) channel estimation in massive MIMO communication systems. A new Bayes-optimal channel estimator is derived using recent advances in the approximate belief propagation Bayesian inference paradigm. By leveraging the inherent sparsity of the mmWave MIMO channel in the angular domain, we recast the underlying channel estimation problem into that of reconstructing a compressible signal from a set of noisy linear measurements. Then, the generalized approximate message passing (GAMP) algorithm is used to find the entries of the unknown mmWave MIMO channel matrix. Unlike all the existing works on the same topic, we model the angular-domain channel coefficients by Laplacian distributed random variables. Furthermore, we establish the closed-form expressions for the various statistical quantities that need to be updated iteratively by GAMP. To render the proposed algorithm fully automated, we also develop an expectation-maximization (EM) based procedure that can be easily embedded within GAMP’s Iteration Loop in order to learn all the unknown parameters of the underlying Bayesian inference problem. The computer simulations show that the proposed combined EM-GAMP algorithm under a Laplacian prior exhibits improvements both in terms of channel estimation accuracy, achievable rate, and computational complexity, as compared to the Gaussian mixture prior that has been advocated in the recent literature. In addition, it is found that the Laplacian prior speeds up the convergence time of GAMP over the entire signal-to-noise ratio range.
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massive mimo mmwave channel estimation using approximate message passing and laplacian prior
International Workshop on Signal Processing Advances in Wireless Communications, 2018Co-Authors: Faouzi Bellili, Foad SohrabiAbstract:This paper tackles the problem of channel estimation in mmWave large-scale communication systems. To leverage the sparsity of mmWave MIMO channels in the beam domain, we use discrete Fourier transform (DFT) precoding and combining and recast the channel estimation problem as a compressed sensing (CS) problem. The generalized approximate message passing (GAMP) algorithm is then used to find the minimum mean square estimate (MMSE) of each entry of the unknown mmWave MIMO channel matrix. Unlike the existing works, this paper models the angular-domain channel coefficients by a Laplacian prior and accordingly establishes the closed-form expressions for all the statistical quantities that need to be updated iteratively by GAMP. Further, to render the proposed algorithm fully automated, we develop an expectation-maximization (EM)-based procedure which can be readily embedded within GAMP's Iteration Loop in order to learn the unknown scale parameter of the underlying Laplacian prior along with the noise variance. Numerical results indicate that the proposed EM-GAMP algorithm under a Laplacian prior yields substantial improvements both in terms of channel estimation accuracy and computational complexity as compared to the existing methods that advocate a Gaussian mixture (GM) prior.
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time synchronization of turbo coded square qam modulated transmissions code aided ml estimator and closed form cramer rao lower bounds
IEEE Transactions on Vehicular Technology, 2017Co-Authors: Faouzi Bellili, Achref Methenni, Souheib Ben Amor, Sofiene Affes, Alex StephenneAbstract:This paper introduces a new maximum likelihood (ML) solution for the code-aided (CA) timing recovery problem in square-quadrature amplitude modulation (QAM) transmissions and derives, for the very first time, its CA Cramer–Rao lower bounds (CRLBs) in closed-form expressions. The channel is assumed to be slowly time varying so that it can be considered as constant over the observation interval. By exploiting the full symmetry of square-QAM constellations and further scrutinizing the Gray-coding mechanism, we express the likelihood function of the system explicitly in terms of the code bits’ a priori log-likelihood ratios (LLRs). The timing recovery task is then embedded in the turbo Iteration Loop, wherein increasingly accurate estimates for such LLRs are computed from the output of the soft-input soft-output decoders and exploited at a per-turbo-Iteration basis in order to refine the ML time delay estimate. The latter is then used to better resynchronize the system, through feedback to the matched filter, so as to obtain more reliable symbol-rate samples for the next turbo Iteration. In order to properly benchmark the new CA ML estimator, we also derive for the very first time the closed-form expressions for the exact CRLBs of the underlying turbo synchronization problem. Computer simulations will show that the new closed-form CRLBs coincide exactly with their empirical counterparts evaluated previously using exhaustive Monte Carlo simulations. They will also show unambiguously the remarkable performance improvements of CA estimation against the traditional nondata-aided scheme, thereby highlighting the potential performance gains in time synchronization that can be achieved owing to the decoder assistance. Over a wide range of practical signal-to-noise ratios (SNRs), CA estimation becomes even equivalent to the completely data-aided scheme in which all the transmitted symbols are perfectly known to the receiver. Moreover, the new CA ML estimator almost reaches the underlying CA CRLBs, even for small SNRs, thereby confirming its statistical efficiency in practice. It also enjoys significant improvements in computational complexity as compared to the most powerful existing ML solution, namely the combined sum-product and expectation-maximization algorithm.
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time synchronization of turbo coded square qam modulated transmissions code aided ml estimator and closed form cram er rao lower bounds
arXiv: Information Theory, 2015Co-Authors: Faouzi Bellili, Achref Methenni, Souheib Ben Amor, Sofiene Affes, Alex StephenneAbstract:This paper introduces a new maximum likelihood (ML) solution for the code-aided (CA) timing recovery problem in square-QAM transmissions and derives, for the very first time, its CA Cram\'er-Rao lower bounds (CRLBs) in closed-form expressions. By exploiting the full symmetry of square-QAM constellations and further scrutinizing the Gray-coding mechanism, we express the likelihood function (LF) of the system explicitly in terms of the code bits' \textit{a priori} log-likelihood ratios (LLRs). The timing recovery task is then embedded in the turbo Iteration Loop wherein increasingly accurate estimates for such LLRs are computed from the output of the soft-input soft-output (SISO) decoders and exploited at a per-turbo-Iteration basis in order to refine the ML time delay estimate. The latter is then used to better re-synchronize the system, through feedback to the matched filter (MF), so as to obtain more reliable symbol-rate samples for the next turbo Iteration. In order to properly benchmark the new CA ML estimator, we also derive for the very first time the closed-form expressions for the exact CRLBs of the underlying turbo synchronization problem. Computer simulations will show that the new closed-form CRLBs coincide exactly with their empirical counterparts evaluated previously using exhaustive Monte-Carlo simulations. They will also show unambiguously the potential performance gains in time synchronization that can be achieved owing to the decoder assistance. Moreover, the new CA ML estimator almost reaches the underlying CA CRLBs, even for small SNRs, thereby confirming its statistical efficiency in practice. It also enjoys significant improvements in computational complexity as compared to the most powerful existing ML solution, namely the combined sum-product and expectation-maximization (SP-EM) algorithm.
Foad Sohrabi - One of the best experts on this subject based on the ideXlab platform.
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generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior
arXiv: Information Theory, 2019Co-Authors: Faouzi Bellili, Foad SohrabiAbstract:This paper tackles the problem of millimeter-Wave (mmWave) channel estimation in massive MIMO communication systems. A new Bayes-optimal channel estimator is derived using recent advances in the approximate belief propagation (BP) Bayesian inference paradigm. By leveraging the inherent sparsity of the mmWave MIMO channel in the angular domain, we recast the underlying channel estimation problem into that of reconstructing a compressible signal from a set of noisy linear measurements. Then, the generalized approximate message passing (GAMP) algorithm is used to find the entries of the unknown mmWave MIMO channel matrix. Unlike all the existing works on the same topic, we model the angular-domain channel coefficients by Laplacian distributed random variables. Further, we establish the closed-form expressions for the various statistical quantities that need to be updated iteratively by GAMP. To render the proposed algorithm fully automated, we also develop an expectation-maximization (EM) based procedure that can be easily embedded within GAMP's Iteration Loop in order to learn all the unknown parameters of the underlying Bayesian inference problem. Computer simulations show that the proposed combined EM-GAMP algorithm under a Laplacian prior exhibits improvements both in terms of channel estimation accuracy, achievable rate, and computational complexity as compared to the Gaussian mixture prior that has been advocated in the recent literature. In addition, it is found that the Laplacian prior speeds up the convergence time of GAMP over the entire signal-to-noise ratio (SNR) range.
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generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior
IEEE Transactions on Communications, 2019Co-Authors: Faouzi Bellili, Foad SohrabiAbstract:This paper tackles the problem of millimeter-wave (mmWave) channel estimation in massive MIMO communication systems. A new Bayes-optimal channel estimator is derived using recent advances in the approximate belief propagation Bayesian inference paradigm. By leveraging the inherent sparsity of the mmWave MIMO channel in the angular domain, we recast the underlying channel estimation problem into that of reconstructing a compressible signal from a set of noisy linear measurements. Then, the generalized approximate message passing (GAMP) algorithm is used to find the entries of the unknown mmWave MIMO channel matrix. Unlike all the existing works on the same topic, we model the angular-domain channel coefficients by Laplacian distributed random variables. Furthermore, we establish the closed-form expressions for the various statistical quantities that need to be updated iteratively by GAMP. To render the proposed algorithm fully automated, we also develop an expectation-maximization (EM) based procedure that can be easily embedded within GAMP’s Iteration Loop in order to learn all the unknown parameters of the underlying Bayesian inference problem. The computer simulations show that the proposed combined EM-GAMP algorithm under a Laplacian prior exhibits improvements both in terms of channel estimation accuracy, achievable rate, and computational complexity, as compared to the Gaussian mixture prior that has been advocated in the recent literature. In addition, it is found that the Laplacian prior speeds up the convergence time of GAMP over the entire signal-to-noise ratio range.
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massive mimo mmwave channel estimation using approximate message passing and laplacian prior
International Workshop on Signal Processing Advances in Wireless Communications, 2018Co-Authors: Faouzi Bellili, Foad SohrabiAbstract:This paper tackles the problem of channel estimation in mmWave large-scale communication systems. To leverage the sparsity of mmWave MIMO channels in the beam domain, we use discrete Fourier transform (DFT) precoding and combining and recast the channel estimation problem as a compressed sensing (CS) problem. The generalized approximate message passing (GAMP) algorithm is then used to find the minimum mean square estimate (MMSE) of each entry of the unknown mmWave MIMO channel matrix. Unlike the existing works, this paper models the angular-domain channel coefficients by a Laplacian prior and accordingly establishes the closed-form expressions for all the statistical quantities that need to be updated iteratively by GAMP. Further, to render the proposed algorithm fully automated, we develop an expectation-maximization (EM)-based procedure which can be readily embedded within GAMP's Iteration Loop in order to learn the unknown scale parameter of the underlying Laplacian prior along with the noise variance. Numerical results indicate that the proposed EM-GAMP algorithm under a Laplacian prior yields substantial improvements both in terms of channel estimation accuracy and computational complexity as compared to the existing methods that advocate a Gaussian mixture (GM) prior.
Alex Stephenne - One of the best experts on this subject based on the ideXlab platform.
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time synchronization of turbo coded square qam modulated transmissions code aided ml estimator and closed form cramer rao lower bounds
IEEE Transactions on Vehicular Technology, 2017Co-Authors: Faouzi Bellili, Achref Methenni, Souheib Ben Amor, Sofiene Affes, Alex StephenneAbstract:This paper introduces a new maximum likelihood (ML) solution for the code-aided (CA) timing recovery problem in square-quadrature amplitude modulation (QAM) transmissions and derives, for the very first time, its CA Cramer–Rao lower bounds (CRLBs) in closed-form expressions. The channel is assumed to be slowly time varying so that it can be considered as constant over the observation interval. By exploiting the full symmetry of square-QAM constellations and further scrutinizing the Gray-coding mechanism, we express the likelihood function of the system explicitly in terms of the code bits’ a priori log-likelihood ratios (LLRs). The timing recovery task is then embedded in the turbo Iteration Loop, wherein increasingly accurate estimates for such LLRs are computed from the output of the soft-input soft-output decoders and exploited at a per-turbo-Iteration basis in order to refine the ML time delay estimate. The latter is then used to better resynchronize the system, through feedback to the matched filter, so as to obtain more reliable symbol-rate samples for the next turbo Iteration. In order to properly benchmark the new CA ML estimator, we also derive for the very first time the closed-form expressions for the exact CRLBs of the underlying turbo synchronization problem. Computer simulations will show that the new closed-form CRLBs coincide exactly with their empirical counterparts evaluated previously using exhaustive Monte Carlo simulations. They will also show unambiguously the remarkable performance improvements of CA estimation against the traditional nondata-aided scheme, thereby highlighting the potential performance gains in time synchronization that can be achieved owing to the decoder assistance. Over a wide range of practical signal-to-noise ratios (SNRs), CA estimation becomes even equivalent to the completely data-aided scheme in which all the transmitted symbols are perfectly known to the receiver. Moreover, the new CA ML estimator almost reaches the underlying CA CRLBs, even for small SNRs, thereby confirming its statistical efficiency in practice. It also enjoys significant improvements in computational complexity as compared to the most powerful existing ML solution, namely the combined sum-product and expectation-maximization algorithm.
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time synchronization of turbo coded square qam modulated transmissions code aided ml estimator and closed form cram er rao lower bounds
arXiv: Information Theory, 2015Co-Authors: Faouzi Bellili, Achref Methenni, Souheib Ben Amor, Sofiene Affes, Alex StephenneAbstract:This paper introduces a new maximum likelihood (ML) solution for the code-aided (CA) timing recovery problem in square-QAM transmissions and derives, for the very first time, its CA Cram\'er-Rao lower bounds (CRLBs) in closed-form expressions. By exploiting the full symmetry of square-QAM constellations and further scrutinizing the Gray-coding mechanism, we express the likelihood function (LF) of the system explicitly in terms of the code bits' \textit{a priori} log-likelihood ratios (LLRs). The timing recovery task is then embedded in the turbo Iteration Loop wherein increasingly accurate estimates for such LLRs are computed from the output of the soft-input soft-output (SISO) decoders and exploited at a per-turbo-Iteration basis in order to refine the ML time delay estimate. The latter is then used to better re-synchronize the system, through feedback to the matched filter (MF), so as to obtain more reliable symbol-rate samples for the next turbo Iteration. In order to properly benchmark the new CA ML estimator, we also derive for the very first time the closed-form expressions for the exact CRLBs of the underlying turbo synchronization problem. Computer simulations will show that the new closed-form CRLBs coincide exactly with their empirical counterparts evaluated previously using exhaustive Monte-Carlo simulations. They will also show unambiguously the potential performance gains in time synchronization that can be achieved owing to the decoder assistance. Moreover, the new CA ML estimator almost reaches the underlying CA CRLBs, even for small SNRs, thereby confirming its statistical efficiency in practice. It also enjoys significant improvements in computational complexity as compared to the most powerful existing ML solution, namely the combined sum-product and expectation-maximization (SP-EM) algorithm.
Souheib Ben Amor - One of the best experts on this subject based on the ideXlab platform.
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time synchronization of turbo coded square qam modulated transmissions code aided ml estimator and closed form cramer rao lower bounds
IEEE Transactions on Vehicular Technology, 2017Co-Authors: Faouzi Bellili, Achref Methenni, Souheib Ben Amor, Sofiene Affes, Alex StephenneAbstract:This paper introduces a new maximum likelihood (ML) solution for the code-aided (CA) timing recovery problem in square-quadrature amplitude modulation (QAM) transmissions and derives, for the very first time, its CA Cramer–Rao lower bounds (CRLBs) in closed-form expressions. The channel is assumed to be slowly time varying so that it can be considered as constant over the observation interval. By exploiting the full symmetry of square-QAM constellations and further scrutinizing the Gray-coding mechanism, we express the likelihood function of the system explicitly in terms of the code bits’ a priori log-likelihood ratios (LLRs). The timing recovery task is then embedded in the turbo Iteration Loop, wherein increasingly accurate estimates for such LLRs are computed from the output of the soft-input soft-output decoders and exploited at a per-turbo-Iteration basis in order to refine the ML time delay estimate. The latter is then used to better resynchronize the system, through feedback to the matched filter, so as to obtain more reliable symbol-rate samples for the next turbo Iteration. In order to properly benchmark the new CA ML estimator, we also derive for the very first time the closed-form expressions for the exact CRLBs of the underlying turbo synchronization problem. Computer simulations will show that the new closed-form CRLBs coincide exactly with their empirical counterparts evaluated previously using exhaustive Monte Carlo simulations. They will also show unambiguously the remarkable performance improvements of CA estimation against the traditional nondata-aided scheme, thereby highlighting the potential performance gains in time synchronization that can be achieved owing to the decoder assistance. Over a wide range of practical signal-to-noise ratios (SNRs), CA estimation becomes even equivalent to the completely data-aided scheme in which all the transmitted symbols are perfectly known to the receiver. Moreover, the new CA ML estimator almost reaches the underlying CA CRLBs, even for small SNRs, thereby confirming its statistical efficiency in practice. It also enjoys significant improvements in computational complexity as compared to the most powerful existing ML solution, namely the combined sum-product and expectation-maximization algorithm.
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time synchronization of turbo coded square qam modulated transmissions code aided ml estimator and closed form cram er rao lower bounds
arXiv: Information Theory, 2015Co-Authors: Faouzi Bellili, Achref Methenni, Souheib Ben Amor, Sofiene Affes, Alex StephenneAbstract:This paper introduces a new maximum likelihood (ML) solution for the code-aided (CA) timing recovery problem in square-QAM transmissions and derives, for the very first time, its CA Cram\'er-Rao lower bounds (CRLBs) in closed-form expressions. By exploiting the full symmetry of square-QAM constellations and further scrutinizing the Gray-coding mechanism, we express the likelihood function (LF) of the system explicitly in terms of the code bits' \textit{a priori} log-likelihood ratios (LLRs). The timing recovery task is then embedded in the turbo Iteration Loop wherein increasingly accurate estimates for such LLRs are computed from the output of the soft-input soft-output (SISO) decoders and exploited at a per-turbo-Iteration basis in order to refine the ML time delay estimate. The latter is then used to better re-synchronize the system, through feedback to the matched filter (MF), so as to obtain more reliable symbol-rate samples for the next turbo Iteration. In order to properly benchmark the new CA ML estimator, we also derive for the very first time the closed-form expressions for the exact CRLBs of the underlying turbo synchronization problem. Computer simulations will show that the new closed-form CRLBs coincide exactly with their empirical counterparts evaluated previously using exhaustive Monte-Carlo simulations. They will also show unambiguously the potential performance gains in time synchronization that can be achieved owing to the decoder assistance. Moreover, the new CA ML estimator almost reaches the underlying CA CRLBs, even for small SNRs, thereby confirming its statistical efficiency in practice. It also enjoys significant improvements in computational complexity as compared to the most powerful existing ML solution, namely the combined sum-product and expectation-maximization (SP-EM) algorithm.
Achref Methenni - One of the best experts on this subject based on the ideXlab platform.
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time synchronization of turbo coded square qam modulated transmissions code aided ml estimator and closed form cramer rao lower bounds
IEEE Transactions on Vehicular Technology, 2017Co-Authors: Faouzi Bellili, Achref Methenni, Souheib Ben Amor, Sofiene Affes, Alex StephenneAbstract:This paper introduces a new maximum likelihood (ML) solution for the code-aided (CA) timing recovery problem in square-quadrature amplitude modulation (QAM) transmissions and derives, for the very first time, its CA Cramer–Rao lower bounds (CRLBs) in closed-form expressions. The channel is assumed to be slowly time varying so that it can be considered as constant over the observation interval. By exploiting the full symmetry of square-QAM constellations and further scrutinizing the Gray-coding mechanism, we express the likelihood function of the system explicitly in terms of the code bits’ a priori log-likelihood ratios (LLRs). The timing recovery task is then embedded in the turbo Iteration Loop, wherein increasingly accurate estimates for such LLRs are computed from the output of the soft-input soft-output decoders and exploited at a per-turbo-Iteration basis in order to refine the ML time delay estimate. The latter is then used to better resynchronize the system, through feedback to the matched filter, so as to obtain more reliable symbol-rate samples for the next turbo Iteration. In order to properly benchmark the new CA ML estimator, we also derive for the very first time the closed-form expressions for the exact CRLBs of the underlying turbo synchronization problem. Computer simulations will show that the new closed-form CRLBs coincide exactly with their empirical counterparts evaluated previously using exhaustive Monte Carlo simulations. They will also show unambiguously the remarkable performance improvements of CA estimation against the traditional nondata-aided scheme, thereby highlighting the potential performance gains in time synchronization that can be achieved owing to the decoder assistance. Over a wide range of practical signal-to-noise ratios (SNRs), CA estimation becomes even equivalent to the completely data-aided scheme in which all the transmitted symbols are perfectly known to the receiver. Moreover, the new CA ML estimator almost reaches the underlying CA CRLBs, even for small SNRs, thereby confirming its statistical efficiency in practice. It also enjoys significant improvements in computational complexity as compared to the most powerful existing ML solution, namely the combined sum-product and expectation-maximization algorithm.
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time synchronization of turbo coded square qam modulated transmissions code aided ml estimator and closed form cram er rao lower bounds
arXiv: Information Theory, 2015Co-Authors: Faouzi Bellili, Achref Methenni, Souheib Ben Amor, Sofiene Affes, Alex StephenneAbstract:This paper introduces a new maximum likelihood (ML) solution for the code-aided (CA) timing recovery problem in square-QAM transmissions and derives, for the very first time, its CA Cram\'er-Rao lower bounds (CRLBs) in closed-form expressions. By exploiting the full symmetry of square-QAM constellations and further scrutinizing the Gray-coding mechanism, we express the likelihood function (LF) of the system explicitly in terms of the code bits' \textit{a priori} log-likelihood ratios (LLRs). The timing recovery task is then embedded in the turbo Iteration Loop wherein increasingly accurate estimates for such LLRs are computed from the output of the soft-input soft-output (SISO) decoders and exploited at a per-turbo-Iteration basis in order to refine the ML time delay estimate. The latter is then used to better re-synchronize the system, through feedback to the matched filter (MF), so as to obtain more reliable symbol-rate samples for the next turbo Iteration. In order to properly benchmark the new CA ML estimator, we also derive for the very first time the closed-form expressions for the exact CRLBs of the underlying turbo synchronization problem. Computer simulations will show that the new closed-form CRLBs coincide exactly with their empirical counterparts evaluated previously using exhaustive Monte-Carlo simulations. They will also show unambiguously the potential performance gains in time synchronization that can be achieved owing to the decoder assistance. Moreover, the new CA ML estimator almost reaches the underlying CA CRLBs, even for small SNRs, thereby confirming its statistical efficiency in practice. It also enjoys significant improvements in computational complexity as compared to the most powerful existing ML solution, namely the combined sum-product and expectation-maximization (SP-EM) algorithm.
