Locally-adapted convolution-based super-resolution of irregularly-sampled ocean remote sensing data

Super-resolution is a classical problem in image processing, with numerous applications to remote sensing image enhancement. Here, we address the super-resolution of irregularly-sampled remote sensing images. Using an optimal interpolation as the low-resolution reconstruction, we explore locally-adapted multimodal convolutional models and investigate different dictionary-based decompositions, namely based on principal component analysis (PCA), sparse priors and non-negativity constraints. We consider an application to the reconstruction of sea surface height (SSH) fields from two information sources, along-track altimeter data and sea surface temperature (SST) data. The reported experiments demonstrate the relevance of the proposed model, especially locally-adapted parametrizations with non-negativity constraints, to outperform optimally-interpolated reconstructions.Comment: 4 pages, 3 figure

Interference alignment for a multi-user SISO interference channel

International audienceOur work addresses the single-input single-output interference channel. The goal is to show that although interference alignment is suboptimal in the finite power region, it is able to achieve a significant overall throughput. We investigate the interference alignment scheme proposed by Choi et al. (IEEE Commun. Lett. 13(11): 847-849, 2009), which achieves a higher multiplexing gain at any given signal dimension than the scheme proposed by Cadambe and Jafar (IEEE Trans. Inform. Theory 54(8), 2008). Then, we try to modify the IA design in order to achieve enhanced sum-rate performance in the practical signal-to-noise ratio (SNR) region. Firstly, we introduce a way to optimize the precoding subspaces at all transmitters, exploiting the fact that channel matrices in the interference model of a single-input single-output channel are diagonal. Secondly, we propose to optimize jointly the set of precoder bases within their associated precoding subspaces. To this end, we combine each precoder with a new combination precoder, and this latter seeks the optimal basis that maximizes the network sum rate. We also introduce an improved closed-form interference alignment scheme that performs close to the other proposed schemes

Neural Koopman prior for data assimilation

With the increasing availability of large scale datasets, computational power and tools like automatic differentiation and expressive neural network architectures, sequential data are now often treated in a data-driven way, with a dynamical model trained from the observation data. While neural networks are often seen as uninterpretable black-box architectures, they can still benefit from physical priors on the data and from mathematical knowledge. In this paper, we use a neural network architecture which leverages the long-known Koopman operator theory to embed dynamical systems in latent spaces where their dynamics can be described linearly, enabling a number of appealing features. We introduce methods that enable to train such a model for long-term continuous reconstruction, even in difficult contexts where the data comes in irregularly-sampled time series. The potential for self-supervised learning is also demonstrated, as we show the promising use of trained dynamical models as priors for variational data assimilation techniques, with applications to e.g. time series interpolation and forecasting

ITERATIVE BLIND SOURCE SEPARATION BY DECORRELATION: ALGORITHM AND PERFORMANCE ANALYSIS

This paper presents an iterative blind source separation method using second order statistics (SOS) and natural gradient technique. The SOS of observed data is shown to be sufficient for separating mutually uncorrelated sources provided that the considered temporal coherence vectors of the sources are pairwise linearly independent. By applying the natural gradient, an iterative algorithm is derived that has a number of attractive properties including its simplicity and ’easy ’ generalization to adaptive or convolutive schemes. Asymptotic performance analysis of the proposed method is performed. Several numerical simulations are presented to demonstrate the effectiveness of the proposed method and to validate the theoretical expression of the asymptotic performance index. 1

Sparse channel estimation algorithms for OTFS system

International audienceOrthogonal 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 ℓ1-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

Multisensor Time–Frequency Signal Processing MATLAB package: An analysis tool for multichannel non-stationary data

The Multisensor Time–FrequencySignal Processing (MTFSP) Matlab package is an analysis tool for multichannel non-stationary signals collected from an array of sensors. By combining array signal processing for non-stationary signals and multichannel high resolution time–frequency methods, MTFSP enables applications such as cross-channel causality relationships, automated component separation and direction of arrival estimation, using multisensor time–frequency distributions (MTFDs). MTFSP can address old and new applications such as: abnormality detection in biomedical signals, source localization in wireless communications or condition monitoring and fault detection in industrial plants. It allows e.g. the reproduction of the results presented in Boashash and Aïssa-El-Bey (in press) [2]. Keywords: Multisensor time–frequency analysis, Direction of arrival, Automated component separation, Blind source separation, Non-stationary array processing, Cross-channel causality analysi

Joint Hybrid Precoding and Combining Design based Multi-Stage Compressed Sensing Approach for mmWave MIMO Channel Estimation

International audienceAlthough the design of hybrid precoders and combiners separately from the complete channel state information (CSI) offers satisfactory performance, the resulting spatial multiplexing channel may not always be orthogonal during communication. Also, acquiring CSI to design optimal precoders and combiners poses several challenges, particularly in millimeter wave (mmWave) channel estimation, and getting the sensing matrix is equivalent to designing the precoders and combiners. For this, we propose a new iterative method based on alternating minimization to design the optimal sensing matrix (incoherent projection matrix) with the given dictionary to minimize the mutual coherence values (µ mx , µ ave and µ all) simultaneously according to the equiangular tight frame (ETF) properties for achieving better-compressed sensing (CS) recovery performance. Then, in order to derive the best hybrid precoders and combiners jointly from the optimally designed sensing matrix, we formulate the optimization design problem as the nearest Kronecker product (NKP) problem. The proposed sensing matrix design works better at lowering the mutual coherence values concurrently with the straightforward shrinkage function, according to simulation findings of mutual coherence values evolution versus outer iteration numbers. In comparison to existing codebookbased hybrid precoder/combiner schemes, the proposed joint hybrid precoder and combiner design improves the performance of the simulation results obtained by multi-stage CS-based mmWave channel estimation in terms of channel estimation accuracy and spectral efficiency (SE). INDEX TERMS Millimeter-wave channel estimation, Multi-stage CS approach, Hybrid mmWave MIMO transceiver, Joint hybrid precoder and combiner design, Equiangular tight frame, Mutual coherence values, Incoherent projection matrix

Signal Metrics for Vertical Handoff Towards (Cognitive) WiMAX

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Identification of cognitive OFDM systems based on cyclostationary pilot signatures

Établir et maintenir la connectivité au sein des réseaux cognitifs tout en supportant des sauts de fréquence opportunistes est une tâche délicate inhérente à la radio cognitive. Une solution pour résoudre ce problème consiste à embarquer dans la couche PHY des signatures spécifiques à chaque réseau permettant de détecter les canaux fréquentiels utilisés par ceux-ci. Dans cet article, nous nous focalisons sur les systèmes OFDM et suggérons d'embarquer des signatures sur les tons pilotes. Plus précisement, la méthode proposée s'appuie sur la redondance périodique souvent induite entre les symboles pilotes. Nous démontrons que les systèmes OFDM peuvent être identifés grâce à cette redondance en conduisant un test d'hypothèse basé sur les statistiques du second ordre. Des exemples numériques détaillés montrent l'efficacité du critère proposé dans un environnement doublement dispersif