SCMA信号解码的梯度下降优化算法

Int. J. Comput. Intell. Appl. Pub Date : 2020-10-08 DOI:10.1142/s1469026821500024

S. Vidal-Beltrán, J. López-Bonilla, F. Martínez-Piñón, Jesús Yalja-Montiel

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引用次数: 3

摘要

最近，基于神经网络(NNs)和深度学习的技术在无线通信等不同科学领域取得了进步。本研究证明了基于神经网络的接收机在检测和解码稀疏码多址(SCMA)码字方面的适用性。仿真结果表明，该接收机在新数据的基础上提供了高精度的预测。此外，本研究还给出了用于机器学习的主要优化算法的性能分析结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Gradient Descent Optimization Algorithms for Decoding SCMA Signals

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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Int. J. Comput. Intell. Appl.

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