This paper tackles the intricate challenge of channel estimation (CE) and symbol detection in high-mobility scenarios for orthogonal time frequency space (OTFS) systems. We introduce a novel partially superimposed pilots (PSP) pattern, strategically balancing computational complexity and CE accuracy by partially superimposing pilots and data symbols in the delay-Doppler (DD) domain. Additionally, we propose a CE and symbol detection (CESD) algorithm, employing an iterative approach that combines low complexity minimum mean squared error (LMMSE) for CE and simplicity-based detection for data symbols. Key contributions encompass optimal pilot pattern determination, a comprehensive iterative algorithm, and a thorough performance evaluation. Comparative analyses with state-of-the-art methods underscore the superior trade-off achieved by CESD in terms of normalized mean square error for CE, bit error rate, spectral efficiency, and computational complexity.

In around the corner radar, particle filter tracking can help to improve the estimation accuracy of a Non-Line of Sight target position. However, in case of using one single receiving antenna, the measurement model only contains multipath delay information. Due to the lack of information in the model, particles corresponding to ambiguous positions can be assigned high weight values. This can leads the particle cloud to diverge from the actual target trajectory and requires several time steps to converge on it again. In this work, our proposed solution consists in exploiting additional Direction-of Arrival information provided by an array of receiving antennas in the particle filter framework. By simulation and experimentation, we will show that the proposed solution allows to solve the aforementioned problem, and thus achieves better localization results compared to single antenna-based tracking as well as non-tracking localization algorithm.

In network monitoring, delays are of great use when it comes to QoS or content distributed services. However, it is often impossible to have access to all the delay measurements within a network. This can be due to network failures or to established measurement policies. For these reasons, delay matrix completion techniques are important for an optimal network monitoring service. In this paper, we formulate the completion problem as a neural collaborative filtering problem by testing two different architectures, generalized matrix factorization and multi-layer perceptron. We evaluate these methods on two different datasets: a synthetic one generated by an autonomous system simulator, and a real-world dataset from Ripe Atlas platform. Finally, a comparative study is conducted between these neural collaborative filtering methods and standard approaches.

In this paper, we design an iterative channel estimation and data detection algorithm in delay-Doppler domain for orthogonal time frequency space (OTFS) system by taking advantage of the sparse nature of the channel in this domain. The proposed algorithm iterates between message-passing-aided data detection and data-aided channel estimation. This sparse channel estimation is reformulated as a specific marginalization of maximum a posteriori (MAP) problem. To deal with the intractability of this problem, we provide a Bayesian approach based on the variational mean-field approximation via the variational Bayesian expectation maximization (VB-EM) algorithm. Finally, we compare the complexity and performance in term of Bit Error Rate (BER) and Normalized Mean Square Error (NMSE) of the proposed solution to a reference solution in the literature (SP-I).

Accurate estimation of delays in a network is crucial for its management. In real-world applications, it is not always possible to conduct on-demand measurements regularly on the overall network. Doing so is costly and time-consuming, and it is also possible that not all the equipments respond to the probes sent in the network. In this paper, we formulate the network delay prediction problem as a non-negative matrix factorization problem with piecewise constant coefficients of the approximate instantaneous representation of data. We choose this approach to utilize the strong spatial and temporal correlation that appear in network delay data. To solve this factorization problem, we consider two different algorithms: an alternating projected gradient algorithm and the NeNMF algorithm. We finally study the efficiency of our approach on two datasets. The first dataset is a synthetic dataset produced by a simulator that we have designed, and the second one is composed of RTT measurements from RIPE Atlas.

Abstract Orthogonal time‐frequency space (OTFS) modulation, which has recently been proposed in the literature, is one of the promising techniques designed in the 2D Delay‐Doppler domain adapted to combat high Doppler fading channels. However, channel estimation in high Doppler scenarios in advanced mobile‐communication systems is still a challenging task. In this paper, the problem of channel estimation in the Delay‐Doppler domain of the OTFS is focused on. First, a simple adaptation of the generalized orthogonal matching pursuit procedure, which will serve as a baseline method in this work, is proposed. Then, iterative algorithms are derived beneficiating from the sparsity of the channel. The unknown channel vector is separated into an unknown sparse support vector corresponding to the delay and Doppler taps, and an unknown vector of channel gains. These algorithms involve ℓ 1 ‐norm minimization and a two‐stage iterative procedure to recover alternatively the channel support and its coefficients. The estimation problem is also addressed from a Bayesian point of view. The sparse representation is reformulated as a specific marginalization of the maximum a posteriori problem on the support of the channel. To deal with the intractability of this problem, two existing techniques are adapted to this context, namely: The Monte Carlo Markov chain with the Gibbs sampler and variational mean‐field approximation with the variational Bayesian expectation‐maximization procedure. Finally, to assess the performance of the proposed algorithms, their complexity and performance are compared against existing methods. Experimental tests, conducted in high‐mobility scenarios and low‐latency applications, show that the proposed schemes are slightly more expensive in terms of complexity load but perform significantly better in terms of normalized mean square error and bit error rate.

In this paper, we describe an iterative scheme which is able to estimate the Fiedler value of a network when the topology is initially unknown. The only available information is the one obtained through a random walk process over the network. Our algorithm is based on the Rayleigh quotient optimization problem and the theory of stochastic approximation. We explain the different tools used to construct our algorithm and we describe our iterative scheme. Finally, we illustrate its performance through a numerical study.

Considering low-frequency acoustic sources, shallow-water environments act as modal dispersive waveguides. In this context, the signal can be described as a sum of a few modal components, each of them propagating with its own wavenumber. When dealing with broadband sources, wavenumber-frequency (f-k) diagrams constitute popular representations naturally enabling modal separation. Based on a Fourier transform, they require however a large number of sensors to resolve wavenumbers with a high-resolution. This limitation can be overcame by adding some physical priors to the processing method. In the continuation of previous works, we propose here a new grid-free algorithm allowing a super-resolution of the (f-k) diagram by benifiting from the sparse nature of the wavenumber spectrum and embedding the broadband behavior of the wavenumbers within the algorithm. The method is validated on simulated data.

This article examines the problem of tracking NLOS targets by exploiting multipath information measured by an urban around-the-corner radar. Due to the non linearity relationship between target position and multipath delays and the complexity of the multipath measurement model, a particle filter is considered. It samples separately the target state (po-sition and velocities) and the multipath amplitudes thanks to an appropriate importance density function. GLRT particle filter, a special case of the proposed particle filter, is shown to provide the most efficient localization results. Simulations demonstrate the ability of the particle filter to correctly estimate the target location and the improvement of localization performance compared to a multipath localization algorithm without tracking.

In this paper a simulations-based evaluation of the energy efficiency performance of relay Power Line Communication (PLC) networks is reported. Our system model is based on two PLC modems as source/destination and one relay modem with time division Decode-and-Forward (DF) scheme. In addition to the transmission power, we consider the static power of PLC modems in our energy consumption model and find the best energy efficiency performance for DF strategy by optimizing the transmission time of each link. Through extensive numerical results and two representative scenarios, we show that the use of PLC relaying with optimal time allocation allows for better performance than direct transmission, yet achieving the same rate of the direct transmission.

This paper presents a detailed measurement study of the electrical energy consumption of commercial Multiple Input Multiple Output (MIMO) Power Line Communications (PLC) modems. Based on these measurements, we propose a linear model that can be used to predict the energy consumed for different communication states and different operation modes of MIMO PLC modems (idle, standby, transmission, reception, variable data rates and variable Ethernet frame sizes). A multiple regression analysis is used to estimate the model parameters. Such an experimental campaign provides valuable data for benchmarking the energy consumption of MIMO PLC modems and gives insights on which energy improvements can be evaluated.

In this paper, a blind equalizer based on probability density function (pdf) fitting is proposed. It does not require any prior information about the transmission channel or the emitted constellation. We also investigate Automatic Modulation Classification (AMC) for Quadrature Amplitude Modulation (QAM) based on the pdf of the equalized signal. We propose three new approaches for AMC. The first employs maximum likelihood functions (ML) of the modulus of real and imaginary parts of the equalized signal. The second is based on the lowest quadratic or Bhattacharyya distance between the estimated pdf of the real and imaginary parts of the equalizer output and the theoretical pdfs of M-QAM modulations. The third approach is based on theoretical pdf dictionnary learning. The performance of the identification scheme is investigated through simulations.

This paper considers bit-loading algorithms to maximize throughput under total power and spectral mask constraints in interference-free OFDM systems. The contribution is twofold. First, we propose a simple criterion to switch between two well-known algorithms from the literature: the conventional Greedy and Greedy-based bit-removing (with maximum allowable bit loading initialization) algorithms. Second, we present a new low-complexity loading algorithm that exploits the bit vector obtained by rounding the water-filling algorithm solution to the associated continuous-input rate maximization problem as an efficient initial bit vector of the Greedy algorithm. We theoretically prove that this bit vector has two interesting properties. The first one states that it is an efficient bit vector, i.e., there is no movement of a bit from one subcarrier to another that reduces the total used power. The second one states that the optimized throughput, starting from this initial bit vector, is achieved by adding or removing bits on each subcarrier at most once. Simulation results show the efficiency of the proposed algorithm, i.e., the achievable throughput is maximized with significant reduction of computation cost as compared to many algorithms in the literature.

In this paper, we propose a novel model-based approach for 3D underwater scene reconstruction, i.e., bathymetry, for side scan sonar arrays in complex and highly reverberating environments like shallow water areas. The presence of multipath echoes and volume reverberation generates false depth estimates. To improve the resulting bathymetry, this paper proposes and develops an adaptive filter, based on several original geometrical models. This multimodel approach makes it possible to track and separate the direction of arrival trajectories of multiple echoes impinging the array. Echo tracking is perceived as a model-based processing stage, incorporating prior information on the temporal evolution of echoes in order to reject cluttered observations generated by interfering echoes. The results of the proposed filter on simulated and real sonar data showcase the clutter-free and regularized bathymetric reconstruction. Model validation is carried out with goodness of fit tests, and demonstrates the importance of model-based processing for bathymetry reconstruction.

In this paper a 3-D reconstruction method is proposed of sea bottom topography, i.e. bathymetry, for sonar data in highly reverberant and multi-path underwater environments. Recent publications showcase the negative impact of waves involving sea surface reflections on the sea bottom imaging process. The novelty of our proposal is twofold: firstly it relies on the use of Markovian-model based processors for bahymetric reconstruction and involves data association filters. Secondly, we propose a nearest neighbor version of the Integrated Probabilistic Data Association filter, capable of filtering several echo trajectories in the presence of clutter with a relatively reduced complexity compared to other existent methods.

The aim of this paper is to study the bit-loading and power allocation problem in the presence of interference (Inter-carrier Interference (ICI) and Inter-Symbol Interference (ISI)) in Orthogonal Frequency Division Multiplexing (OFDM) systems. ISI and ICI significantly degrade the performance of OFDM systems and make the resource management optimized without the assumption of interference less efficient. To solve this problem, an initial solution based on the greedy approach is proposed in this paper. Then, several reduced complexity approaches, which yield a little degradation compared to the initial solution, have been developed. Simulation results presented in the context of Power Line Communication (PLC) show that the performance of proposed algorithms is tight with their upper bound. Moreover, these algorithms efficiently improve the system performance as compared to the constant power water-filling allocation algorithm as well as maximum power allocation algorithm.

In this paper, a novel design scheme for radar signal is proposed. It is based on a modification of Costas codes design obtained by increasing the frequency separation of frequency hops beyond the orthogonality condition. This condition states that frequency separation between frequency hops should be at most equal to the inverse of bit duration of the code. Indeed, increasing frequency separation beyond this condition is sometimes needed in radar applications. However, this leads to the appearance of grating lobes near the main temporal lobe of the autocorrelation function. This paper proposes overcoming this problem using sub-pulse coding. The originality of the proposed approach consists in choosing for Costas sub-pulses other Costas codes instead of constant frequency ones in order to lower autocorrelation grating lobes that would normally appear when the orthogonality condition is not satisfied. Indeed, we show that, under some conditions on Costas signal parameters in the sub-pulse, good elimination of grating lobes is achieved.

In this paper, a novel radar signal is proposed. It is based on a modification of Costas signals obtained by increasing the frequency separation of Costas frequency hops beyond the orthogonality condition. Indeed, Costas codes are well known for their good Range-Doppler properties. Their orthogonality condition states that frequency separation between frequency hops of the Costas code should be at most equal to the inverse of bit duration of the code. Increasing frequency separation beyond this condition is sometimes needed in radar applications. However, this leads to the appearance of grating lobes near the main temporal lobe of the autocorrelation function. The originality of the proposed approach, in this paper, consists in proposing a modification of Costas sub-pulses in order to overcome grating lobes problem. Indeed, we show that when introducing Phase coding in the sub-pulses, grating lobes are lowered significantly. The study is carried out for several phase codes. The proposed code design is validated through computer experiments.

In Voice Conversion (VC), the speech of a source speaker is modified to resemble that of a particular target speaker. Currently, standard VC approaches use Gaussian mixture model (GMM)-based transformations that do not generate high-quality converted speech due to “over-smoothing” resulting from weak links between individual source and target frame parameters. Dynamic Frequency Warping (DFW) offers an appealing alternative to GMM-based methods, as more spectral details are maintained in transformation; however, the speaker timbre is less successfully converted because spectral power is not adjusted explicitly. Previous work combines separate GMM- and DFW-transformed spectral envelopes for each frame. This paper proposes a more effective DFW-based approach that (1) does not rely on the baseline GMM methods, and (2) functions on the acoustic class level. To adjust spectral power, an amplitude scaling function is used that compares the average target and warped source log spectra for each acoustic class. The proposed DFW with Amplitude scaling (DFWA) outperforms standard GMM and hybrid GMM-DFW methods for VC in terms of both speech quality and timbre conversion, as is confirmed in extensive objective and subjective testing. Furthermore, by not requiring time-alignment of source and target speech, DFWA is able to perform equally well using parallel or nonparallel corpora, as is demonstrated explicitly.

This paper introduces new hybrid blind equalization algorithms for QAM signals, the first term of which is the constant modulus criterion (CMA) or its extended version (ECMA) and the second are a penalty term that vanishes at constellation points coordinates. Several penalties, based on cosine, Gaussian and polynomial lscr1-norm functions respectively are investigated. Simulations show the effectiveness of these algorithms.

This paper addresses the one-to-many mapping problem in Voice Conversion (VC) by exploring source-to-target mappings in GMM-based spectral transformation. Specifically, we examine differences using source-o nly versus joint source/target information in the classificati on stage of transformation, effectively illustrating a one-to- many in the traditional acoustically-based GMM. We propose combating this effect by using phonetic information in the GMM learning and classification. We then show the success of our proposed context-dependent modeling with transformation results using an objective error cri terion. Finally, we discuss implications of our work in ada pting current approaches to VC. Index Terms : Voice conversion, GMM, Spectral mapping.

In this paper a new method for speech synthesis is proposed. It relies on a source-filter decomposition of the speech signal by means of an ARX-LF model. This model allows the representation of the glottal signal as the sum of an LF waveform and a residual signal. The residual information is then analyzed by HNM. This signal representation enables high quality speech modification such as pitch, duration or even voice quality transformation. Experiments performed on a real speech database show the relevance of the proposed method as compared to other existing approaches.

An algorithm for GCI (glottal closure instants) estimation is presented in this paper. It relies on a source filter model of speech production using a LF model for the source component. From this source filter decomposition, a ratio which measures the goodness of fit of the LF source model is introduced in the GCI estimation procedure together with fundamental frequency constraints. Then a Viterbi algorithm is applied to extract the most likely GCI sequence. Experiments performed on a real speech database show the relevance of the proposed method

International audience

International audience

International audience

In seismic deconvolution, blind approaches must be considered in situations where the reflectivity sequence, the source wavelet signal and the noise power level are unknown. In the presence of long, non-minimum-phase, source wavelets, strong interference of the contributions of reflectors make the wavelet estimation and deconvolution procedure from recorded data complicated. We address this problem in a two step approach. First, a robust, but truncated, estimate of the wavelet is performed using a standard maximum likelihood approach. Then, improved wavelet estimation is achieved by fitting an ARMA model to the initial MA wavelet using the Prony algorithm. The algorithmic problem of wavelet initialization is also addressed. Simulation results and real data experiments show that a significant improvement is brought by this approach.

International audience

We propose a Bayesian approach for multipath channel time delay estimation when the signal is received by a square-law detector. Two different methods are presented. The first one is applied when the signal is supplied at the output of the detector. On certain approximations, the derivation of the MAP (maximum a posteriori) estimator leads to optimize a l/sub 1/-norm criterion. In the second method, the signal is observed after demodulation and matched-filtering. We introduce a Bernoulli-Gaussian model to account for the sparse properties of the channel impulse response and solve the deconvolution problem using Monte-Carlo simulations. Comparisons between both algorithms are presented on simulations. They show that the second method leads to more accurate estimators, while justifying the validity of the approximations used in the first one.

In this contribution we present an algorithm for estimating some parameters of offset in the case of incomplete data. This estimation cannot be performed directly with an EM or SEM method because the density of local extrema in the likelihood map grows exponentially with the number of observations and because the SEM method provides a monotonic sequence of estimates so that bad initialization cannot be recovered. We perform the estimation in the Fourier domain. The offsets in time domain are transformed into pulsations in the Fourier domain. We minimize a quadratic distance between the parametric and empirical sampled Fourier transform with an EM method. Contrary to the problems encountered in the time domain the asymptotic loglikelihood of the sampled empirical Fourier transform is continuous w.r.t. the parameters of offset. We discuss the influence of the frequencies at which the Fourier transform is sampled and we present a numerical study of convergence of the proposed algorithms.

We study the properties of the reflection coefficients of band-limited spectra. We show that they must belong to simple subsets of the unit disk. Then, the problem of band-limited spectrum modeling is addressed in a more appropriate way than with approaches based on correlation coefficients. In particular, we derive a new band-limited spectrum model for band-limited signals with first correlation coefficients matching the signal ones. The practical behavior of the model is illustrated with an example.

A new adaptive subspace estimation algorithm is presented, based on the maximisation of the likelihood functional. It requires little computational cost and the particular structure of the algorithm ensures the orthonormality of the estimated basis of eigenvectors. Application to moving sources localization shows the very good behavior of the algorithm when applied to problems of practical interest.

In this paper, we study band-limited spectrum estimation under band-limiting and correlation constraints. We use an approach based on reflection coefficients, that appears to be more convenient than those based on correlation coefficients. We study the properties of reflection coefficients of band-limited spectra and derive a band-limited spectrum estimator that can be seen as an extension to the band-limited case of the classical AR maximum entropy spectrum estimator. We check that this estimator yields good results in practice. Furthermore, we briefly explain how the results derived for a symmetric interval can be extended to the cases of a non symmetric interval and of several intervals.

We present a new direct adaptive subspace estimation (DASE) algorithm, based on direct parametrization of the eigenvectors with their coefficients. Its application to sources localization and blind channel identification is described. We also present the study of its asymptotic performance and show the good behavior of our algorithm, compared to another adaptive algorithm based on a Givens rotations parametrization.

International audience

The set M( Omega ) of all (discrete-time) power spectra supported on a known interval (- Omega , Omega ), (with Omega>