Palabras clave: Deep Learning Machine Learning Cognitive networks; Data-driven networks
Buenrostro-Mariscal, R., Santana-Mancilla, P. C., Montesinos-López, O. A., Nieto Hipólito, J. I., & Anido-Rifón, L. E. (2022). A Review of Deep Learning Applications for the Next Generation of Cognitive Networks. Applied Sciences, 12(12), 6262. https://doi.org/10.3390/app12126262
2022 Applied Sciences
URL: https://doi.org/10.3390/app12126262
Intelligence capabilities will be the cornerstone in the development of next-generation cognitive networks. These capabilities allow them to observe network conditions, learn from them, and then, using prior knowledge gained, respond to its operating environment to optimize network performance. This study aims to offer an overview of the current state of the art related to the use of deep learning in applications for intelligent cognitive networks that can serve as a reference for future initiatives in this field. For this, a systematic literature review was carried out in three databases, and eligible articles were selected that focused on using deep learning to solve challenges presented by current cognitive networks. As a result, 14 articles were analyzed. The results showed that applying algorithms based on deep learning to optimize cognitive data networks has been approached from different perspectives in recent years and in an experimental way to test its technological feasibility. In addition, its implications for solving fundamental challenges in current wireless networks are discussed.