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Your search keyword '"Belouchrani, Adel"' showing total 14 results
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14 results on '"Belouchrani, Adel"'

1. ParamNet: A Multi-Layer Parametric Network for Joint Channel Estimation and Symbol Detection

Author

Choqueuse, Vincent, Frunza, Alexandru, Belouchrani, Adel, Azou, Stéphane, and Morel, Pascal

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

This paper proposes a parametric-based network architecture for joint channel estimation and data detection in communications systems with hardware impairments. This architecture is composed of a data-augmented layer, a custom soft thresholding function, and several linear layers modeling the effect of channel effects and hardware impairments. In the proposed network, the soft thresholding function softly constrains the detected data to be within the considered constellation. The latter depends only on one one parameter that is optimized during training. The benefit of the proposed approach is illustrated through a communication chain corrupted by multiple impairments and noises.

Published
2022

8. Extended Joint EVD Algorithm for Widely Linear ARMA Source Separation

Author

Meziani, Saliha, Mesloub, Ammar, Belouchrani, Adel, and Abed-Meraim, Karim

Abstract

In this article, a new approximate joint diagonalization problem is formulated for the blind separation of possibly dependent sources, modelled as widely linear autoregressive moving average (WL-ARMA) signals. The latter modelling gives rise to two sets of matrix parameters, sharing each a specific diagonal structure transform. A new method is proposed to achieve the desired source separation using a novel extended joint eigen value decomposition (JEVD) of both sets of matrices. The separation method proceeds in two steps, the first one identifies the WL-ARMA parameters, while the second step identifies the separation matrix through the extended JEVD of two particular matrix sets. We have developed a new Jacobi-like JEVD algorithm based on Shear and Givens rotations. This algorithm, referred to as Extended Optimal Phase Joint Eigen Value Decomposition, realizes the simultaneous diagonalization of two sets of matrices with different structures and outperforms the existing algorithms in difficult scenarios. Simulation results are provided to illustrate the effectiveness of the proposed algorithm.

Published
2023
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13. Informed Separation of Dependent Sources Using Joint Matrix Decomposition

Author

Boudjellal, Abdelwaheb, Abed-Meraim, Karim, Belouchrani, Adel, Ravier, Philippe, Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique (PRISME), Université d'Orléans (UO)-Ecole Nationale Supérieure d'Ingénieurs de Bourges (ENSI Bourges), and Ecole Nationale Polytechnique [Alger] (ENP)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; This paper deals with the separation problem of dependent sources. The separation is made possible thanks to side information on the dependence nature of the considered sources. In this work, we first show how this side information can be used to achieve desired source separation using joint matrix decomposition techniques. Indeed, in the case of statistically independent sources, many BSS methods are based on joint matrix diagonalization. In our case, we replace the target diagonal structure by appropriate non diagonal one which reflects the dependence nature of the sources. This new concept is illustrated with two simple 2 2 source separation examples where second-order-statistics and high order-statistics are used respectively.

Published
2014
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