Deep Learning on Network Traffic Prediction: Recent Advances, Analysis, and Future Directions
University of Luxembourg · National Institute of Informatics · +4 more institutions
Abstract
From the perspective of telecommunications, next-generation networks, or beyond 5G, will inevitably face the challenge of a growing number of users and devices. Such growth results in high-traffic generation with limited network resources. Thus, the analysis of the traffic and the precise forecast of user demands is essential for developing an intelligent network. In this line, Machine Learning (ML) and especially Deep Learning (DL) models can further benefit from the huge amount of network data. They can act in the background to analyze and predict traffic conditions more accurately than ever and help to optimize the design and management of network services. Recently, a significant amount of research effort…
Citation impact
- FWCI
- 44.90
- Percentile
- 100%
- References
- 126
Authors
4- OAOns AouediCorresponding
University of Luxembourg
- VAVan An Le
National Institute of Informatics, National Institute of Advanced Industrial Science and Technology
- KPKandaraj Piamrat
Laboratoire des Sciences du Numérique de Nantes, IMT Atlantique, Nantes Université
- YJYusheng Ji
National Institute of Informatics
Topics & keywords
- Deep learning
- Computer science
- Artificial intelligence
- Data science
- Machine learning