Publication detail

Exploring Potential of ML-aided Mobile Traffic Prediction for Energy-efficient Optimization of Network Resources Using Real World Dataset

KOLÁČKOVÁ, A. SEVGICAN, S. ULU, M. SADREDDIN, J. MAŠEK, P. HOŠEK, J. JEŘÁBEK, J. TUGCU, T.

Original Title

Exploring Potential of ML-aided Mobile Traffic Prediction for Energy-efficient Optimization of Network Resources Using Real World Dataset

Type

journal article in Web of Science

Language

English

Original Abstract

To meet the extremely stringent but diverse requirements of Beyond Fifth-Generation (B5G) networks, traffic-aware adaptive utilization of network resources is becoming essential. To cope with that, a detailed traffic data analysis enables opportunities for mobile network operators to improve the Quality of Service (QoS) in the next-generation mobile communication systems. This paper presents a comprehensive analysis of the real world data collected from an operator’s 4G+ and 5G infrastructure during a seven-month campaign. Efficient Machine Learning (ML) based network traffic predictions are presented together with a statistical model to develop optimal resource allocation strategies by using the data gathered during the pandemic, an era when the data volume, as well as the bandwidth requirements and the end users’ expectations, were significantly elevated in terms of QoS, given the huge shift to the online world. Data analysis confirmed the assumption that there are traffic changes during the day and the whole week, which helped us to find new research directions regarding resource allocation optimization of next-generation mobile networks. Furthermore, we introduce the Predictive Energy Saver for Baseband Units (PESBiU) algorithm, which utilizes traffic prediction and power consumption analysis to manage the power states (sleep or active) of BBUs in a network. The PESBiU algorithm utilizes the results from ML predictions to effectively balance energy efficiency and network performance, demonstrating its potential for practical deployment in future mobile communication networks by transitioning BBUs to sleep mode during low-traffic periods, thereby achieving significant power savings.

Keywords

Beyond 5G networks; Data analysis; Machine learning; Mobile traffic; Resource allocation; BBU Energy Saving

Authors

KOLÁČKOVÁ, A.; SEVGICAN, S.; ULU, M.; SADREDDIN, J.; MAŠEK, P.; HOŠEK, J.; JEŘÁBEK, J.; TUGCU, T.

Released

1. 7. 2024

Publisher

IEEE

Location

Online

ISBN

2169-3536

Periodical

IEEE Access

State

United States of America

Pages from

93606

Pages to

93622

Pages count

17

URL

https://ieeexplore.ieee.org/document/10579805

BibTex

@article{BUT188972,
  author="Aneta {Koláčková} and Salih {Sevgican} and Muhammet Fatih {Ulu} and Jale {Sadreddin} and Pavel {Mašek} and Jiří {Hošek} and Jan {Jeřábek} and Tuna {Tugcu}",
  title="Exploring Potential of ML-aided Mobile Traffic Prediction for Energy-efficient Optimization of Network Resources Using Real World Dataset",
  journal="IEEE Access",
  year="2024",
  volume="0",
  number="0",
  pages="93606--93622",
  doi="10.1109/ACCESS.2024.3421633",
  issn="2169-3536",
  url="https://ieeexplore.ieee.org/document/10579805"
}