Deep Learning Applications in Mobile Networks

Izdanje: International Scientific Conference on Information Technology and Data Related Research

DOI: 10.15308/Sinteza-2019-553-560

Oblast: Intelligent Signal Processing

Stranice: 553-560

Apstrakt:
The expected deployment of 5G networks, growing percentages of mobile networks’ market penetration levels and increasing popularity of mobile applications induce new challenges and requirements for mobile network operators. One of the ways of tackling these new challenges is the deployment of advanced machine learning techniques in hopes of handling expected traffic volumes, performing real-time analytics and managing network resources. The paper presents the aspects of mobile networking in which deep learning methods can be implemented. After presenting the basic background and modern deep learning techniques and related technologies, paper presents an overview of the current advances made in these research areas. The paper also identifies the fields within the realm of mobile networking that show particular potential for new exploration.
Ključne reči: Deep learning, mobile networks, mobile data analysis, network security, signal processing
Priložene datoteke:
  • 553-560 ( veličina: 211,08 KB, broj pregleda: 319 )

Preuzimanje citata:

BibTeX format
@article{article,
  author  = {D. Dašić, M. Vučetić, G. HewAKee and M. Stanković}, 
  title   = {Deep Learning Applications in Mobile Networks},
  journal = {International Scientific Conference on Information Technology and Data Related Research},
  year    = 2020,
  pages   = {553-560},
  doi     = {10.15308/Sinteza-2019-553-560}
}
RefWorks Tagged format
RT Conference Proceedings
A1 Dejan Dašić
A1 Miljan Vučetić
A1 Gardelito HewAKee
A1 Miloš Stanković
T1 Deep Learning Applications in Mobile Networks
AD Univerzitet Singidunum, Beograd, Beograd, Srbija
YR 2020
NO doi: 10.15308/Sinteza-2019-553-560
Unapred formatirani prikaz citata
D. Dašić, M. Vučetić, G. HewAKee and M. Stanković, Deep Learning Applications in Mobile Networks, Univerzitet Singidunum, Beograd, 2020, doi:10.15308/Sinteza-2019-553-560