Blind joint MIMO channel and data estimation based on regularized ML

• Published in: Digital Signal Processing
• Main Authors: Lassami, Nacerredine, Aissa El Bey, Abdeldjalil, Abed-Meraim, Karim
• Other Authors: Département Mathematical and Electrical Engineering (IMT Atlantique - MEE), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT), Equipe Communication System Design (Lab-STICC_COSYDE), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom Paris (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom Paris (IMT), Ecole Militaire Polytechnique Alger (EMP), Ministère de l'Enseignement Supérieur et de la Recherche Scientifique Algérie (MESRS)-Ministère de la Défense Nationale Algérie, Laboratoire pluridisciplinaire de recherche en ingénierie des systèmes, mécanique et énergétique (PRISME), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Ecole Polytechnique de l'Université d'Orléans, Université d'Orléans (UO)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2021
• Subjects: Blind MIMO identification; deterministic ML; regularized estimation; joint channel and data estimation; sparsity; simplicity; [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; DML
• Online Access: https://imt-atlantique.hal.science/hal-03322591; https://imt-atlantique.hal.science/hal-03322591/document; https://imt-atlantique.hal.science/hal-03322591/file/Elsevier_Regularized_Blind_joint_MIMO_channel_and_data_estimation_based_on_DML.pdf; https://doi.org/10.1016/j.dsp.2021.103201

International audience The problem of blind joint FIR-MIMO channel and data estimation is addressed in this paper. Based on a regularized DML (Deterministic Maximum Likelihood) formulation of the problem, a bilinear approach is used in order to estimate jointly the channel impulse responses and the input data. This regularization is introduced as a penalty function added to the classical DML criterion representing the a priori information about the problem in order to enhance the accuracy of the estimation. Two types of priors information are considered for the transmitted data: the finite alphabet simplicity or the sparsity. The sparsity prior was also considered for channel impulse responses. The key advantage of the proposed criteria is their convexity when optimized alternatively over the channel and the input data. The proposed approach allows to improve further the estimation accuracy of such a blind estimation problem but suffers from a relatively high computational cost. Hence, a reduced complexity implementation of the latter has been proposed at the end of the paper, in an adaptive scheme for high dimensional or streaming data situations.