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Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning

In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input–multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The...

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Detalles Bibliográficos
Autores principales: Liu, Qingli, Sun, Jiaxu, Wang, Peiling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453763/
https://www.ncbi.nlm.nih.gov/pubmed/37628162
http://dx.doi.org/10.3390/e25081131
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author Liu, Qingli
Sun, Jiaxu
Wang, Peiling
author_facet Liu, Qingli
Sun, Jiaxu
Wang, Peiling
author_sort Liu, Qingli
collection PubMed
description In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input–multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder–decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink’s magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions.
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spelling pubmed-104537632023-08-26 Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning Liu, Qingli Sun, Jiaxu Wang, Peiling Entropy (Basel) Article In order to solve the problem wherein too many base station antennas are deployed in a massive multiple-input–multiple-output system, resulting in high overhead for downlink channel state information feedback, this paper proposes an uplink-assisted channel feedback method based on deep learning. The method applies the reciprocity of the uplink and downlink, uses uplink channel state information in the base station to help users give feedback on unknown downlink information, and compresses and restores the channel state information. First, an encoder–decoder structure is established. The encoder reduces the network depth and uses multi-resolution convolution to increase the accuracy of channel state information extraction while reducing the number of computations relating to user equipment. Afterward, the channel state information is compressed to reduce feedback overhead in the channel. At the decoder, with the help of the reciprocity of the uplink and downlink, the feature extraction of the uplink’s magnitudes is carried out, and the downlink channel state information is integrated into a channel state information feature matrix, which is restored to its original size. The simulation results show that compared with CSINet, CRNet, CLNet, and DCRNet, indoor reconstruction precision was improved by an average of 16.4%, and outside reconstruction accuracy was improved by an average of 21.2% under all compressions. MDPI 2023-07-27 /pmc/articles/PMC10453763/ /pubmed/37628162 http://dx.doi.org/10.3390/e25081131 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Qingli
Sun, Jiaxu
Wang, Peiling
Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning
title Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning
title_full Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning
title_fullStr Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning
title_full_unstemmed Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning
title_short Uplink Assisted MIMO Channel Feedback Method Based on Deep Learning
title_sort uplink assisted mimo channel feedback method based on deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10453763/
https://www.ncbi.nlm.nih.gov/pubmed/37628162
http://dx.doi.org/10.3390/e25081131
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