Real-time Traffic Prediction in 5G Networks Using LSTM Networks

Title

Real-time Traffic Prediction in 5G Networks Using LSTM Networks

Creator

Kumar H.; Al-Jawahry H.M.; Manohar M.; Karthika K.; Victor M.; Shetty A.D.

Description

This research explores the application of Long Short-Term Memory (LSTM) networks for real-time traffic prediction within 5G networks, aiming to address the critical need for accurate prediction models in dynamic network environments. Leveraging the sequential learning capabilities of LSTM networks, the proposed methodology encompasses dataset preparation, model architecture design, training, and evaluation. Experimental results demonstrate the effectiveness of the LSTM-based prediction model in capturing temporal dependencies and providing reliable predictions across various prediction horizons. While promising, further research is warranted to enhance the model's performance and address remaining challenges. This study contributes to advancing the state-of-the-art in traffic prediction methodologies, facilitating more efficient network management and optimization in 5G environments. 2024 IEEE.

Source

2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

5G networks; deep learning; LSTM networks; network management; real-time traffic prediction

Coverage

Kumar H., Chandigarh Group of Colleges, Chandigarh Engineering College, Department of Computer Application, Punjab, Mohali, India; Al-Jawahry H.M., The Islamic University, College Of Technical Engineering, Department Of Computers Techniques Engineering, Najaf, Iraq, The Islamic University Of Al Diwaniyah, College Of Technical Engineering, Department Of Computers Techniques Engineering, Al Diwaniyah, Iraq; Manohar M., Ies College of Technology, Department of Electronics & Communication Engineering, M.P., Bhopal, India; Karthika K., Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India; Victor M., Christ (Deemed to be University), School of Business and Management, India; Shetty A.D., (Deemed to be University) Nmam Institute of Technology (NMAMIT), Department of Cse Nitte, India

Rights

Restricted Access

Relation

ISBN: 979-835039075-9

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/IICCCS61609.2024.10763791

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214431884&doi=10.1109%2fIICCCS61609.2024.10763791&partnerID=40&md5=c05a3d9820768523968aebaaea9a75e0