Deep Phase-Transmittance RBF Neural Network for Beamforming With Multiple Users-Reference-Cited by-同舟云学术

Deep Phase-Transmittance RBF Neural Network for Beamforming With Multiple Users

Published:2022-07 Issue:7 Volume:11 Page:1498-1502
ISSN:2162-2337
Container-title:IEEE Wireless Communications Letters
language:
Short-container-title:IEEE Wireless Commun. Lett.

Author:

Mayer Kayol Soares¹^ORCID,Muller Candice²^ORCID,Soares Jonathan Aguiar¹,Castro Fernando Cesar Comparsi de²^ORCID,Arantes Dalton Soares¹^ORCID

Affiliation:

1. Department of Communications, School of Electrical and Computer Engineering, Digital Communications Laboratory, University of Campinas, Campinas, Brazil

2. Department of Electronics and Computing, Technology Center, Federal University of Santa Maria (UFSM), Santa Maria, Brazil

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/5962382/9823160/09779863.pdf?arnumber=9779863

Reference29 articles.

1. Generalization Characteristics of Complex-Valued Feedforward Neural Networks in Relation to Signal Coherence

2. Butterfly Neural Filter Applied to Beamforming

3. Deep Learning Based Beamforming Neural Networks in Downlink MISO Systems

4. Autoencoder Neural Network Based Intelligent Hybrid Beamforming Design for mmWave Massive MIMO Systems

5. Deep Learning–based Energy Beamforming with Transmit Power Control in Wireless Powered Communication Networks

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