Gradient Descent Optimization Algorithms for Decoding SCMA Signals

Abstract

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.