Deep Learning-Aided SCMA

IEEE Communications Letters

Volumn 22, Issue 4, 2018, Pages 720-723

  Kim, Minhoe ^a   Kim, Nam I ^a   Lee, Woongsup ^b   Cho, Dong Ho ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

^b Gyeongsang National University   (South Korea)

Author keywords

autoencoder; deep learning; deep neural network (DNN); Sparse code multiple access (SCMA)

Indexed keywords

5G MOBILE COMMUNICATION SYSTEMS; BIT ERROR RATE; CODES (SYMBOLS); DECODING; DEEP NEURAL NETWORKS; LEARNING SYSTEMS; MAXIMUM LIKELIHOOD; PERSONNEL TRAINING; WIRELESS TELECOMMUNICATION SYSTEMS;

AUTO ENCODERS; MAXIMUM LIKELIHOOD DECODING; MOBILE COMMUNICATIONS; MULTIPLE ACCESS; NOMA;

DEEP LEARNING;

EID: 85041219177     PISSN: 10897798     EISSN: None     Source Type: Journal    
DOI: 10.1109/LCOMM.2018.2792019     Document Type: Article

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