TY - JOUR
T1 - Gradient Descent Optimization Algorithms for Decoding SCMA Signals
AU - Vidal-Beltrán, Sergio
AU - López Bonilla, José Luis
AU - Piñón, Fernando Martínez
AU - Yalja-Montiel, Jesús
N1 - Publisher Copyright:
© 2021 World Scientific Publishing Europe Ltd.
PY - 2021/3
Y1 - 2021/3
N2 - Recently, technologies based on neural networks (NNs) and deep learning have improved in different areas of Science such as wireless communications. This study demonstrates the applicability of NN-based receivers for detecting and decoding sparse code multiple access (SCMA) codewords. The simulation results reveal that the proposed receiver provides highly accurate predictions based on new data. Moreover, the performance analysis results of the primary optimization algorithms used in machine learning are presented in this study.
AB - Recently, technologies based on neural networks (NNs) and deep learning have improved in different areas of Science such as wireless communications. This study demonstrates the applicability of NN-based receivers for detecting and decoding sparse code multiple access (SCMA) codewords. The simulation results reveal that the proposed receiver provides highly accurate predictions based on new data. Moreover, the performance analysis results of the primary optimization algorithms used in machine learning are presented in this study.
KW - 5G
KW - NOMA
KW - SCMA
KW - neural networks
KW - optimization algorithms
KW - supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85093502951&partnerID=8YFLogxK
U2 - 10.1142/S1469026821500024
DO - 10.1142/S1469026821500024
M3 - Artículo
AN - SCOPUS:85093502951
SN - 1469-0268
VL - 20
JO - International Journal of Computational Intelligence and Applications
JF - International Journal of Computational Intelligence and Applications
IS - 1
M1 - 2150002
ER -