Yves Delignon

A low complexity suboptimal MIMO receiver: the combined ZF-MLD algorithm

Recently, wireless technologies have known an increasing success, thus becoming an interesting field for study. One of the topics of research are multiple antenna arrays. Signal recovery in that case is not obvious. Different methods with different performances have been suggested. From a statistical analysis of errors at the output of zero-forcing, we have constructed a method, which we present in this article, that combines zero-forcing and maximum likelihood. We will demonstrate that it provides both a low complexity and nearly optimal results regarding binary error rate (BER). Furthermore, we will show its relevancy compared to the VBLAST algorithm.

Monte Carlo Methods for Channel, Phase Noise, and Frequency Offset Estimation With Unknown Noise Variances in OFDM Systems

In this paper, we address the problem of orthogonal frequency-division multiplexing (OFDM) channel estimation in the presence of phase noise (PHN) and carrier frequency offset (CFO). In OFDM systems, PHN and CFO cause two effects: the common phase error (CPE) and the intercarrier interference (ICI) which severely degrade the accuracy of the channel estimate. In literature, several algorithms have been proposed to solve this problem. Nevertheless, in all these existing schemes, both the PHN and the additive white Gaussian noise (AWGN) powers are assumed to be known. Because no a priori knowledge of PHN and AWGN powers is available at the receiver, we propose different strategies for the estimation of channel impulse response (CIR), CFO, PHN, and also the PHN and the AWGN powers. Based on Monte Carlo methods, the proposed approaches estimate these many unknowns in the time domain from a training OFDM symbol using either offline or online estimators. In the online case, we propose sequential Monte Carlo algorithms and especially an original maximization step of the joint a posteriori probability density function for the unknown parameters. Simulation results are provided to illustrate the efficiency of the proposed algorithms in terms of mean square error (MSE) on channel, phase distortions, and also noise power estimation.

Non-Pilot-Aided Sequential Monte Carlo Method to Joint Signal, Phase Noise, and Frequency Offset Estimation in Multicarrier Systems

We address the problem of phase noise (PHN) and carrier frequency offset (CFO) mitigation in multicarrier receivers. In multicarrier systems, phase distortions cause two effects: the common phase error (CPE) and the intercarrier interference (ICI) which severely degrade the accuracy of the symbol detection stage. Here, we propose a non-pilot-aided scheme to jointly estimate PHN, CFO, and multicarrier signal in time domain. Unlike existing methods, non-pilot-based estimation is performed without any decision-directed scheme. Our approach to the problem is based on Bayesian estimation using sequential Monte Carlo filtering commonly referred to as particle filtering. The particle filter is efficiently implemented by combining the principles of the Rao-Blackwellization technique and an approximate optimal importance function for phase distortion sampling. Moreover, in order to fully benefit from time-domain processing, we propose a multicarrier signal model which includes the redundancy information induced by the cyclic prefix, thus leading to a significant performance improvement. Simulation results are provided in terms of bit error rate (BER) and mean square error (MSE) to illustrate the efficiency and the robustness of the proposed algorithm.

One-dimensional barcode reading: an information theoretic approach

In the convergence context of identification technology and information-data transmission, the barcode found its place as the simplest and the most pervasive solution for new uses, especially within mobile commerce, bringing youth to this long-lived technology. From a communication theory point of view, a barcode is a singular coding based on a graphical representation of the information to be transmitted. We present an information theoretic approach for 1D image-based barcode reading analysis. With a barcode facing the camera, distortions and acquisition are modeled as a communication channel. The performance of the system is evaluated by means of the average mutual information quantity. On the basis of this theoretical criterion for a reliable transmission, we introduce two new measures: the theoretical depth of field and the theoretical resolution. Simulations illustrate the gain of this approach.

Robust visual tracking via MCMC-based particle filtering

We present in this paper a new visual tracking framework based on the MCMC-based particle algorithm. Firstly, in order to obtain a more informative likelihood, we propose to combine the color-based observation model with a detection confidence density obtained from the Histograms of Oriented Gradients (HOG) descriptor. The MCMC-based particle algorithm is then employed to estimate the posterior distribution of the target state to solve the tracking problem. The global system has been tested on different real datasets. Experimental results demonstrate the robustness of the proposed system in several difficult scenarios.

Multiple access for 60 GHz mobile ad hoc network

This paper analyzes the suitability of various multiple access schemes for a 60 GHz mobile ad hoc network. The investigated techniques include multi-carrier modulation (OFDM-TDMA), the spectrum spreading method (DS-CDMA) and the combined schemes (MC-CDMA, MC-DS-CDMA) benefiting from the advantages of both techniques. The comparison between these different multiple access schemes is focussed on two aspects: (i) sensitivity to phase noise; the large available bandwidth around 60 GHz is very suitable for transmission of high data rate in indoor environments, but one issue in the 60 GHz band is the design of oscillators with moderate phase noise; (ii) robustness to imperfect power control; ad hoc networks consist of a set of mobile terminals communicating among themselves without any central controller; radio resource management has to be conducted in a distributed way and the power control is inherently imperfect.

Blind Detection of Severely Blurred 1D Barcode

In this paper, we present a joint blind channel estimation and symbol detection for decoding a blurred and noisy 1D barcode captured image. From an information transmission point of view, we show that the channel impulse response, the noise power and the symbols can be efficiently estimated by taking into account the signal structure such as the cyclostationary property of the hidden Markov process to estimate. Based on the Expectation- Maximisation method, we show that the new algorithm offers significative performance gain compared to classical ones pushing back the frontiers of the barcode technology.

A Bayesian Perspective on Multiple Source Localization in Wireless Sensor Networks

In this paper, we address the challenging problem of multiple source localization in wireless sensor networks (WSN). We develop an efficient statistical algorithm, based on the novel application of sequential Monte Carlo (SMC) sampler methodology, that is able to deal with an unknown number of sources given quantized data obtained at the fusion center from different sensors with imperfect wireless channels. We also derive the posterior Cramér-Rao bound (PCRB) of the source location estimate. The PCRB is used to analyze the accuracy of the proposed SMC sampler algorithm and the impact that quantization has on the accuracy of location estimates of the sources. Extensive experiments show the benefits of the proposed scheme in terms of the accuracy of the estimation method that is required for model selection (i.e., the number of sources) and the estimation of the source characteristics compared to the classical importance sampling method.

MCMC sampling for joint estimation of phase distortions and transmitted symbols in OFDM systems

In this paper, we address the challenging problem of the OFDM reception in the presence of phase distortions. Phase noise and carrier frequency offset seriously degrade the performances of OFDM systems by destroying orthogonality of the subcarriers. Based on a Markov Chain Monte Carlo sampling mechanization, our approach consists in jointly estimating the phase noise, the frequency offset and the transmitted symbols. The proposed algorithm is implemented in the time domain in order to benefit from the redundancy information induced by the cyclic prefix and from the time correlation of the OFDM signal owing to the presence of virtual and/or pilot subcarriers. The algorithms efficiency is enhanced by incorporating the Rao-Blackwellization technique as well as various sampling improvement strategies. Simulation results, provided in terms of bit error rate (BER) and mean square error (MSE), clearly illustrate the efficiency and the robustness of the proposed estimator.

Pilot-aided sequential monte carlo estimation of phase distortions and transmitted symbols in multicarrier systems

We address the challenging problem of the joint estimation of transmitted symbols and phase distortions in standardized multicarrier systems, including pilot or virtual subcarriers. These subcarriers create time correlation on the useful transmitted OFDM signal that we propose to take into account by an autoregressive model. Because the phase distortions are nonlinear, we set the joint estimation algorithm on the framework of the Sequential Monte Carlo methods. Simulation results are provided in terms of bit error rate (BER) and mean square error (MSE); they highlight the efficiency and the robustness of the estimator.