Tensor train representation of MIMO channels using the JIRAFE method-Reference-Cited by-同舟云学术

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Tensor train representation of MIMO channels using the JIRAFE method

Published:2020-06 Issue: Volume:171 Page:107479
ISSN:0165-1684
Container-title:Signal Processing
language:en
Short-container-title:Signal Processing

Author:

Zniyed Yassine,Boyer Rémy^ORCID,de Almeida André L.F.,Favier Gérard

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software,Control and Systems Engineering

Reference21 articles.

1. Massive MIMO: survey and future research topics;Jo;IET Commun.,2016

2. Tensor-based channel estimation for dual-polarized massive MIMO systems;Qian;IEEE Trans. SP,2018

3. Foundations of the PARAFAC Procedure: Models and Conditions for an ’Explanatory’ Multi-Modal Factor Analysis;Harshman,1970

4. French SKA white book-the french community towards the square kilometer array;Ollier,2017

5. Era of big data processing: a new approach via tensor networks and tensor decompositions;Cichocki;CoRR,2010

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1. Optimal Estimation of the Canonical Polyadic Decomposition From Low-Rank Tensor Trains;2024

2. Attention-deep reinforcement learning jointly beamforming based on tensor decomposition for RIS-assisted V2X mmWave massive MIMO system;Complex & Intelligent Systems;2023-07-17

3. Semi-blind receivers based on a coupled nested Tucker-PARAFAC model for dual-polarized MIMO systems using combined TST and MSMKron codings;Digital Signal Processing;2023-06

4. 2D/3D ResNet Deep Neural Network for 4G and 5G NR Wireless Channel Estimation;2023 25th International Conference on Digital Signal Processing and its Applications (DSPA);2023-03-29

5. References;Matrix and Tensor Decompositions in Signal Processing;2021-08-27

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