Performance Analysis of Uplink Sparse Code Multiple Access in Rician Fading

2019 ◽  
Author(s):  
Jinchen Bao ◽  
Rong Shi ◽  
Xuehua Wang ◽  
Chang Liu ◽  
Ke Deng ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Multiple Access ◽  
Sparse Code ◽  
Rician Fading

Performance analysis of sparse code multiple access with variant MIMO techniques

2020 ◽  
Vol 39 ◽  
pp. 101023
Author(s):  
Nareeman Jibreel ◽  
Salma Elkawafi ◽  
Abdelhamid Younis ◽  
Raed Mesleh
Keyword(s):  
Performance Analysis ◽  
Multiple Access ◽  
Sparse Code

Gradient Descent Optimization Algorithms for Decoding SCMA Signals

2020 ◽  
pp. 2150002
Author(s):  
Sergio Vidal-Beltrán ◽  
José Luis López Bonilla ◽  
Fernando Martínez Piñón ◽  
Jesús Yalja-Montiel
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Wireless Communications ◽  
Performance Analysis ◽  
Multiple Access ◽  
Gradient Descent ◽  
Optimization Algorithms ◽  
Sparse Code ◽  
Simulation Results

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.

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