Gradient Descent Optimization Algorithms for Decoding SCMA Signals
| Autor: | Jesús Yalja-Montiel, Fernando Martínez Piñón, Sergio Vidal-Beltrán, José Luis López Bonilla |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Optimization algorithm business.industry Computer science Deep learning 05 social sciences Supervised learning 050801 communication & media studies 020206 networking & telecommunications 02 engineering and technology Computer Science Applications Theoretical Computer Science 0508 media and communications 0202 electrical engineering electronic engineering information engineering Wireless Artificial intelligence business Gradient descent Software 5G Decoding methods |
| Zdroj: | International Journal of Computational Intelligence and Applications. 20 |
| ISSN: | 1757-5885 1469-0268 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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