Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems

Vista sencilla de ítem
dc.contributor.authorLe, H. A.
dc.contributor.authorVan Chien, T.
dc.contributor.authorNguyen, T. H.
dc.contributor.authorChoo, H.
dc.contributor.authorNguyen, V. D.
dc.date.accessioned2021-12-23T12:59:20Z
dc.date.available2021-12-23T12:59:20Z
dc.date.issued2021
dc.description.abstractChannel estimation plays a critical role in the system performance of wireless networks. In addition, deep learning has demonstrated significant improvements in enhancing the communication reliability and reducing the computational complexity of 5G-and-beyond networks. Even though least squares (LS) estimation is popularly used to obtain channel estimates due to its low cost without any prior statistical information regarding the channel, this method has relatively high estimation error. This paper proposes a new channel estimation architecture with the assistance of deep learning in order to improve the channel estimation obtained by the LS approach. Our goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile for simulations in 5G-and-beyond networks under the level of mobility expressed by the Doppler effects. The system model is constructed for an arbitrary number of transceiver antennas, while the machine learning module is generalized in the sense that an arbitrary neural network architecture can be exploited. Numerical results demonstrate the superiority of the proposed deep learning-based channel estimation framework over the other traditional channel estimation methods popularly used in previous works. In addition, bidirectional long short-term memory offers the best channel estimation quality and the lowest bit error ratio among the considered artificial neural network architectures.en_ES
dc.identifier.citationLe, H. A., Van Chien, T., Nguyen, T. H., Choo, H., & Nguyen, V. D. (2021). Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems. En Sensors (Basel) (Vol. 21, Número 14). https://doi.org/10.3390/s21144861en_ES
dc.identifier.issn1424-8220
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/3203
dc.language.isoen_USen_ES
dc.publisherSensors (Basel)en_ES
dc.subjectCOMMUNICATION LEAST-SQUARES ANALYSISen_ES
dc.subjectMACHINE LEARNINGen_ES
dc.subjectNEURAL NETWORKSen_ES
dc.subjectCOMPUTER REPRODUCIBILITY OF RESULTSen_ES
dc.subjectMIMO-OFDMen_ES
dc.subjectCHANNEL ESTIMATION FREQUENCYen_ES
dc.subjectSELECTIVE CHANNELSen_ES
dc.subjectMACHINE LEARNINGen_ES
dc.titleMachine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systemsen_ES
dc.typeArticuloen_ES
dc.ubicacionhttps://doi.org/10.3390/s21144861en_ES
uv.catalogadorSGGen_ES
uv.colectionBibliografía 5Gen_ES

Archivos

Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
Bib5G-43.pdf
Tamaño:
1.7 MB
Formato:
Adobe Portable Document Format
Descripción:
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
384 B
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar

Colecciones

BIBLIOGRAFIA 5G