Authors :
N. Sunil; Dr. K. Vijayan; S. Vinay; M. Gurusai
Volume/Issue :
Volume 8 - 2023, Issue 4 - April
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/419Jn7E
DOI :
https://doi.org/10.5281/zenodo.7902047
Abstract :
Cellular network traffic has grown rapidly as
a result of the development of cellular technology. In
order to achieve the most advantageous resource
allocation through practical bandwidth provisioning and
maintain the maximum network utilization, modelling
and forecasting of cellular network loading are crucial.
The goal of this is to create a model that can aid in the
intelligent prediction of load traffic onto the cellular
network. In this study, the model for predicting cellular
traffic is developed that incorporates Transverse LSTM,
PCA, and Discrete Wavelet. The main goal is to design a
greener and traffic-friendly 5G/IMT-2020 network
(SDN/NFV) with efficient resource allocation to ensure
good quality of service.
Keywords :
Prediction, Wireless mesh networks, Deep learning, Machine learning
Cellular network traffic has grown rapidly as
a result of the development of cellular technology. In
order to achieve the most advantageous resource
allocation through practical bandwidth provisioning and
maintain the maximum network utilization, modelling
and forecasting of cellular network loading are crucial.
The goal of this is to create a model that can aid in the
intelligent prediction of load traffic onto the cellular
network. In this study, the model for predicting cellular
traffic is developed that incorporates Transverse LSTM,
PCA, and Discrete Wavelet. The main goal is to design a
greener and traffic-friendly 5G/IMT-2020 network
(SDN/NFV) with efficient resource allocation to ensure
good quality of service.
Keywords :
Prediction, Wireless mesh networks, Deep learning, Machine learning
