Cellular traffic load prediction with LSTM and Gaussian process regression

W Wang, C Zhou, H He, W Wu… - ICC 2020-2020 IEEE …, 2020 - ieeexplore.ieee.org
Accurate cellular traffic load prediction is a pre-requisite for efficient and automatic network
planning and management. Considering diverse users' activities at different locations and …

Spatio-temporal hybrid graph convolutional network for traffic forecasting in telecommunication networks

M Kalander, M Zhou, C Zhang, H Yi, L Pan - arXiv preprint arXiv …, 2020 - arxiv.org
Telecommunication networks play a critical role in modern society. With the arrival of 5G
networks, these systems are becoming even more diversified, integrated, and intelligent …

Dynamic graph neural network for traffic forecasting in wide area networks

T Mallick, M Kiran, B Mohammed… - … Conference on Big …, 2020 - ieeexplore.ieee.org
Wide area networking infrastructures (WANs), particularly science and research WANs, are
the backbone for moving large volumes of scientific data between experimental facilities and …

Gated recurrent unit network-based cellular trafile prediction

EPL Shiang, WC Chien, CF Lai… - 2020 International …, 2020 - ieeexplore.ieee.org
With the development of 5G and big data, network traffic is growing exponentially year by
year. Effective computing resource allocation and network traffic control become an …

Hybrid prediction model for mobile data traffic: A cluster-level approach

BS Shawel, TT Debella, G Tesfaye… - … Joint Conference on …, 2020 - ieeexplore.ieee.org
Mobile data consumption is rapidly growing following the ever-increasing bandwidth-hungry
applications and improvements in network data rates. With the anticipated 5G right at the …

A comprehensive research on exponential smoothing methods in modeling and forecasting cellular traffic

QT Tran, L Hao, QK Trinh - Concurrency and Computation …, 2020 - Wiley Online Library
Traffic prediction based on time series analysis methods that are low‐cost and low
computational complexity can offer more efficient resource management and better QoS …

Metropolitan cellular traffic prediction using deep learning techniques

S Sudhakaran, A Venkatagiri… - 2020 IEEE …, 2020 - ieeexplore.ieee.org
With the advent of 5G networks, it is of paramount importance for machines to learn and
make decisions independently. An important area where machine learning can be used to …

Bsenet: A data-driven spatio-temporal representation learning for base station embedding

X Wang, T Yang, Y Cui, Y Jin, H Wang - IEEE Access, 2020 - ieeexplore.ieee.org
Base station (BS) plays a critical role in the wireless network. There has been some
research on exploring the spatio-temporal information of BS in different fields. However …

The Applications of Machine Learning Techniques in Networked Systems

S Jamshidi - 2020 - search.proquest.com
Many large networked systems ranging from the Internet to ones deployed atop the Internet
(eg, Amazon) play critical roles in our daily lives. In these systems, individual nodes (eg, a …

Rwanda exchange rate time series analysis using Garch model

E MUDAHOGORA Kazungu - 2020 - dr.ur.ac.rw
Volatility modeling and forecasts are essential tools to all financial sectors. This work
focuses on weekly exchange rate data of the Rwf referred to the USD for a period of seven …