Cargando…
Deep learning for 1-bit compressed sensing-based superimposed CSI feedback
In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, 1-bit compressed sensing (CS)-based superimposed channel state information (CSI) feedback has shown many advantages, while still faces many challenges, such as low accuracy of the downlink CSI recovery and l...
Autores principales: | Qing, Chaojin, Ye, Qing, Cai, Bin, Liu, Wenhui, Wang, Jiafan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8912209/ https://www.ncbi.nlm.nih.gov/pubmed/35271663 http://dx.doi.org/10.1371/journal.pone.0265109 |
Ejemplares similares
-
Transfer learning-based channel estimation in orthogonal frequency division multiplexing systems using data-nulling superimposed pilots
por: Qing, Chaojin, et al.
Publicado: (2022) -
Deep Learning-Based Joint CSI Feedback and Hybrid Precoding in FDD mmWave Massive MIMO Systems
por: Sun, Qiang, et al.
Publicado: (2022) -
Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator †
por: Riviello, Daniel Gaetano, et al.
Publicado: (2023) -
A CSI-Based Human Activity Recognition Using Deep Learning
por: Moshiri, Parisa Fard, et al.
Publicado: (2021) -
Utilizing deep learning models in CSI-based human activity recognition
por: Shalaby, Eman, et al.
Publicado: (2022)