Enhanced MIMO CSI Estimation Using ACCPM with Limited Feedback.
ACCPM
CSI
Gram–Schmidt process
MIMO
beamforming
channel model
downlink
Journal
Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366
Informations de publication
Date de publication:
19 Sep 2023
19 Sep 2023
Historique:
received:
27
07
2023
revised:
31
08
2023
accepted:
07
09
2023
medline:
28
9
2023
pubmed:
28
9
2023
entrez:
28
9
2023
Statut:
epublish
Résumé
Multiple Input and Multiple Output (MIMO) is a promising technology to enable spatial multiplexing and improve throughput in wireless communication networks. To obtain the full benefits of MIMO systems, the Channel State Information (CSI) should be acquired correctly at the transmitter side for optimal beamforming design. The analytical centre-cutting plane method (ACCPM) has shown to be an appealing way to obtain the CSI at the transmitter side. This paper adopts ACCPM to learn down-link CSI in both single-user and multi-user scenarios. In particular, during the learning phase, it uses the null space beamforming vector of the estimated CSI to reduce the power usage, which approaches zero when the learned CSI approaches the optimal solution. Simulation results show our proposed method converges and outperforms previous studies. The effectiveness of the proposed method was corroborated by applying it to the scattering channel and winner II channel models.
Identifiants
pubmed: 37766022
pii: s23187965
doi: 10.3390/s23187965
pmc: PMC10537774
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Références
Sensors (Basel). 2022 May 31;22(11):
pubmed: 35684816