Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation-Reference-Cited by-同舟云学术

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

Published:2023 Issue: Volume:11 Page:14533-14549
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Nabi Syed Tauhidun¹^ORCID,Islam Md. Rashidul¹^ORCID,Alam Md. Golam Rabiul¹^ORCID,Hassan Mohammad Mehedi²^ORCID,AlQahtani Salman A.³^ORCID,Aloi Gianluca⁴^ORCID,Fortino Giancarlo⁴^ORCID

Affiliation:

1. Department of Computer Science and Engineering, BRAC University, Dhaka, Bangladesh

2. Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

3. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

4. Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Rende, Italy

Funder

King Saud University, Riyadh, Saudi Arabia, through the Researchers Supporting Project

PNRR project FAIR - Future AI Research

Spoke 9 - Green-aware AI, under the NRRP MUR program funded by the NextGenerationEU

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10042176.pdf?arnumber=10042176

Reference49 articles.

1. Mobile Data Traffic Prediction by Exploiting Time-Evolving User Mobility Patterns

2. Mobile Network Traffic Forecasting Using Artificial Neural Networks

3. Network Traffic Overload Prediction with Temporal Graph Attention Convolutional Networks

4. Forecasting for Network Management with Joint Statistical Modelling and Machine Learning

5. Big Data Driven Mobile Traffic Understanding and Forecasting: A Time Series Approach

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