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Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

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Published on: February 3, 2021

CLPREM: A real-time traffic prediction method for 5G mobile network.

Xiaorui Wu¹, Chunling Wu²

¹National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.

|April 1, 2024

Summary

This study introduces a Cluster-based Lightweight Prediction Model (CLPREM) for accurate 5G network traffic forecasting. CLPREM improves real-time prediction accuracy and efficiency, addressing limitations of traditional methods.

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Area of Science:

Computer Science
Telecommunications Engineering
Artificial Intelligence

Background:

Traditional network traffic forecasting methods are insufficient for large-scale networks and 5G.
Accurate real-time traffic prediction is crucial for network resource optimization and anomaly detection.

Purpose of the Study:

To propose a novel method for real-time traffic prediction in 5G mobile networks.
To enhance the robustness and accuracy of network traffic prediction models.

Main Methods:

Development of a Cluster-based Lightweight Prediction Model (CLPREM).
Integration of Long Short-Term Memory (LSTM) networks, data augmentation, clustering, and model compression.
Implementation of unique data processing and classification techniques for improved robustness.

Main Results:

CLPREM demonstrates higher accuracy compared to traditional prediction schemes.
The proposed model achieves a lower time cost for real-time traffic prediction.
An added preprocessing method further enhances CLPREM's accuracy and anomaly prediction capabilities.

Conclusions:

CLPREM effectively addresses the challenges of real-time traffic prediction in 5G networks.
The model offers a robust, accurate, and efficient solution for network monitoring and management.