Application of deep learning algorithms and architectures in the new generation of mobile networks
Operators of modern mobile networks are faced with significant challenges in providing the requested level of service to an ever increasing number of user entities. Advanced machine learning techniques based on deep architectures and appropriate learning methods are recognized as promising...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Faculty of Technical Sciences in Cacak
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/f3813343a491436085c73df7c1bf67ac |
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| Sumario: | Operators of modern mobile networks are faced with significant challenges in
providing the requested level of service to an ever increasing number of
user entities. Advanced machine learning techniques based on deep
architectures and appropriate learning methods are recognized as promising
ways of tackling the said challenges in many aspects of mobile networks,
such as mobile data and mobility analysis, network control, network security
and signal processing. Having firstly presented the background of deep
learning and related technologies, the paper goes on to present the
architectures used for deployment of deep learning in mobile networks. The
paper continues with an overview of applications and services related to the
new generation of mobile networks that employ deep learning methods.
Finally, the paper presents practical use case of modulation classification
as implementation of deep learning in an application essential for modern
spectrum management. We complete this work by pinpointing future directions
for research. |
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