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Dive into the research topics where Carles Navarro Manchón is active.

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Featured researches published by Carles Navarro Manchón.


IEEE Transactions on Information Theory | 2013

Merging Belief Propagation and the Mean Field Approximation: A Free Energy Approach

Erwin Riegler; Gunvor Elisabeth Kirkelund; Carles Navarro Manchón; Mihai Alin Badiu; Bernard Henri Fleury

We present a joint message passing approach that combines belief propagation and the mean field approximation. Our analysis is based on the region-based free energy approximation method proposed by Yedidia et al. We show that the message passing fixed-point equations obtained with this combination correspond to stationary points of a constrained region-based free energy approximation. Moreover, we present a convergent implementation of these message passing fixed-point equations provided that the underlying factor graph fulfills certain technical conditions. In addition, we show how to include hard constraints in the part of the factor graph corresponding to belief propagation. Finally, we demonstrate an application of our method to iterative channel estimation and decoding in an orthogonal frequency division multiplexing system.


global communications conference | 2010

Variational Message-Passing for Joint Channel Estimation and Decoding in MIMO-OFDM

Gunvor Elisabeth Kirkelund; Carles Navarro Manchón; Lars P. B. Christensen; Erwin Riegler; Bernard Henri Fleury

In this contribution, a multi-user receiver for M- QAM MIMO-OFDM operating in time-varying and frequency-selective channels is derived. The proposed architecture jointly performs semi-blind estimation of the channel weights and noise inverse variance, serial interference cancellation and decoding in an iterative manner. The scheme relies on a variational message-passing approach, which enables a joint design of all these functionalities or blocks but the last one. Decoding is performed using the sum-product algorithm. This is in contrast to nowadays proposed approaches in which all these blocks are designed and optimized individually. Simulation results show that the proposed receiver outperforms in coded bit-error-rate a state-of-the- art iterative receiver of same complexity, in which all blocks are designed independently. Joint block design and, as a result, the fact that the uncertainty in the channel estimation is accounted for in the proposed receiver explain this better performance.


vehicular technology conference | 2009

Turbo Receivers for Single User MIMO LTE-A Uplink

Gilberto Berardinelli; Carles Navarro Manchón; Luc Deneire; Troels Bundgaard Sørensen; Preben Mogensen; Kari Pajukoski

The paper deals with turbo detection techniques for Single User Multiple-Input-Multiple-Output (SU MIMO) antenna schemes. The context is on the uplink of the upcoming Long Term Evolution - Advanced (LTE-A) systems. Iterative approaches based on Parallel Interference Cancellation (PIC) and Successive Interference Cancellation (SIC) are investigated, and a low-complexity solution allowing to combine interstream interference cancellation and noise enhancement reduction is proposed. Performance is evaluated for Orthogonal Frequency Division Multiplexing (OFDM) and Single Carrier Frequency Division Multiplexing (SC-FDM) as candidate uplink modulation schemes for LTE-A. Simulation results show that, in a 2times2 antenna configuration, the turbo processing allows a consistent improvement of the link performance, being SC-FDM the one having higher relative gain with respect to linear detection. The turbo receivers impact is however much reduced for both modulation schemes in a 2times4 configuration, due to the higher diversity gain provided by the additional receive antennas.


Signal Processing | 2015

Sparse estimation using Bayesian hierarchical prior modeling for real and complex linear models

Niels Lovmand Pedersen; Carles Navarro Manchón; Mihai Alin Badiu; Dmitriy Shutin; Bernard Henri Fleury

In sparse Bayesian learning (SBL), Gaussian scale mixtures (GSMs) have been used to model sparsity-inducing priors that realize a class of concave penalty functions for the regression task in real-valued signal models. Motivated by the relative scarcity of formal tools for SBL in complex-valued models, this paper proposes a GSM model - the Bessel K model - that induces concave penalty functions for the estimation of complex sparse signals. The properties of the Bessel K model are analyzed when it is applied to Type I and Type II estimation. This analysis reveals that, by tuning the parameters of the mixing pdf different penalty functions are invoked depending on the estimation type used, the value of the noise variance, and whether real or complex signals are estimated. Using the Bessel K model, we derive sparse estimators based on a modification of the expectation-maximization algorithm formulated for Type II estimation. The estimators include as special instances the algorithms proposed by Tipping and Faul 1] and Babacan et al. 2]. Numerical results show the superiority of the proposed estimators over these state-of-the-art algorithms in terms of convergence speed, sparseness, reconstruction error, and robustness in low and medium signal-to-noise ratio regimes. HighlightsA GSM is proposed to model sparsity-inducing priors for real and complex signal models.By using the GSM in combination with a novel modification of the EM algorithm, sparse estimators are devised.The sparsity-inducing property of the GSM depends on whether the signal model is real or complex.The proposed sparse estimators encompass other existing estimators.The proposed estimators outperform these sparse estimators in low and moderate SNR regimes.


international conference on communications | 2012

Application of Bayesian hierarchical prior modeling to sparse channel estimation

Niels Lovmand Pedersen; Carles Navarro Manchón; Dmitriy Shutin; Bernard Henri Fleury

Existing methods for sparse channel estimation typically provide an estimate computed as the solution maximizing an objective function defined as the sum of the log-likelihood function and a penalization term proportional to the ℓ1-norm of the parameter of interest. However, other penalization terms have proven to have strong sparsity-inducing properties. In this work, we design pilot-assisted channel estimators for OFDM wireless receivers within the framework of sparse Bayesian learning by defining hierarchical Bayesian prior models that lead to sparsity-inducing penalization terms. The estimators result as an application of the variational message-passing algorithm on the factor graph representing the signal model extended with the hierarchical prior models. Numerical results demonstrate the superior performance of our channel estimators as compared to traditional and state-of-the-art sparse methods.


international symposium on information theory | 2012

Message-passing algorithms for channel estimation and decoding using approximate inference

Mihai Alin Badiu; Gunvor Elisabeth Kirkelund; Carles Navarro Manchón; Erwin Riegler; Bernard Henri Fleury

We design iterative receiver schemes for a generic communication system by treating channel estimation and information decoding as an inference problem in graphical models. We introduce a recently proposed inference framework that combines belief propagation (BP) and the mean field (MF) approximation and includes these algorithms as special cases. We also show that the expectation propagation and expectation maximization (EM) algorithms can be embedded in the BP-MF framework with slight modifications. By applying the considered inference algorithms to our probabilistic model, we derive four different message-passing receiver schemes. Our numerical evaluation in a wireless scenario demonstrates that the receiver based on the BP-MF framework and its variant based on BP-EM yield the best compromise between performance, computational complexity and numerical stability among all candidate algorithms.


IEEE Communications Letters | 2013

Message-Passing Receiver Architecture with Reduced-Complexity Channel Estimation

Mihai Alin Badiu; Carles Navarro Manchón; Bernard Henri Fleury

We propose an iterative receiver architecture which allows for adjusting the complexity of estimating the channel frequency response in OFDM systems. This is achieved by approximating the exact Gaussian channel model assumed in the system with a Markov model whose state-space dimension is a design parameter. We apply an inference framework combining belief propagation and the mean field approximation to a probabilistic model of the system which includes the approximate channel model. By doing so, we obtain a receiver algorithm with adjustable complexity which jointly performs channel and noise precision estimation, equalization and decoding. Simulation results show that low-complexity versions of the algorithm - obtained by selecting low state-space dimensions - can closely attain the performance of a receiver devised based on the exact channel model.


international symposium on turbo codes and iterative information processing | 2010

Merging belief propagation and the mean field approximation: A free energy approach

Erwin Riegler; Gunvor Elisabeth Kirkelund; Carles Navarro Manchón; Bernard Henri Fleury

We present a joint message passing approach that combines belief propagation and the mean field approximation. Our analysis is based on the region-based free energy approximation method proposed by Yedidia et al., which allows to use the same objective function (Kullback-Leibler divergence) as a starting point. In this method message passing fixed point equations (which correspond to the update rules in a message passing algorithm) are then obtained by imposing different region-based approximations and constraints on the mean field and belief propagation parts of the corresponding factor graph. Our results can be applied, for example, to algorithms that perform joint channel estimation and decoding in iterative receivers. This is demonstrated in a simple example.


vehicular technology conference | 2008

On the Design of a MIMO-SIC Receiver for LTE Downlink

Carles Navarro Manchón; Luc Deneire; Preben Mogensen; Troels Bundgaard Sørensen

In this paper, we investigate different multiple-input multiple-output (MIMO) receiver structures based on MMSE filtering and sequential interference cancellation (SIC) for the downlink of the 3GPP long term evolution (LTE) system. We divide them into two approaches: symbol-SIC receivers, in which the detection and interference cancellation is done independently for each subcarrier, and codeword-SIC structures, in which the processing is carried out for each independently-coded stream by including the turbo-decoder inside the feedback loop. The results show that symbol-SIC receivers need to take into account the propagation of errors in the interference cancellation to provide the turbo decoder with reliable soft bit values. However, these are clearly outperformed by codeword-SIC schemes, due to the error correction capabilities of the turbo-decoder inside the feedback loop. We show that the best tradeoff between computational complexity and receiver performance is achieved by only cancelling the interference of a codeword when this has been successfully decoded.


IEEE Signal Processing Letters | 2015

Iterative Receiver Design for ISI Channels Using Combined Belief- and Expectation-Propagation

Peng Sun; Chuanzong Zhang; Zhongyong Wang; Carles Navarro Manchón; Bernard Henri Fleury

In this letter, a message-passing algorithm that combines belief propagation and expectation propagation is applied to design an iterative receiver for intersymbol interference channels. We detail the derivation of the messages passed along the nodes of a vector-form factor graph representing the underlying probabilistic model. We also present a simple but efficient method to cope with the “negative variance” problem of expectation propagation. Simulation results show that the proposed algorithm outperforms, in terms of bit-error-rate and convergence rate, a LMMSE turbo-equalizer based on Gaussian message passing with the same order of computational complexity.

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Oana-Elena Barbu

Intel Mobile Communications

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Erwin Riegler

Vienna University of Technology

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Luc Deneire

University of Nice Sophia Antipolis

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