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5G Traffic Prediction Based on Deep Learning.

¹Department of Information and Technology, Wenzhou Vocational College of Science and Technology, Wenzhou 325006, China.

Computational Intelligence and Neuroscience

|July 5, 2022

Summary

This summary is machine-generated.

This study introduces a Smoothed Long Short-Term Memory (SLSTM) model for accurate 5G network traffic prediction. The SLSTM model enhances forecasting accuracy, addressing the challenges of diverse and heterogeneous network traffic demands.

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Area of Science:

Computer Science
Telecommunications Engineering

Background:

Exponential growth in 5G network traffic presents significant forecasting challenges due to data diversity and heterogeneity.
Increasing demand for wireless access necessitates accurate network traffic prediction for efficient resource management.

Purpose of the Study:

To develop an accurate 5G network traffic prediction model.
To address the complexities of forecasting diverse and heterogeneous network traffic patterns.

Main Methods:

Developed a Smoothed Long Short-Term Memory (SLSTM) traffic prediction model.
Implemented an adaptive mechanism to adjust model layers and hidden units based on prediction accuracy.
Utilized seasonal differencing to stabilize the time series and reduce randomness in 5G traffic data.

Main Results:

The SLSTM algorithm demonstrated superior accuracy in 5G traffic prediction compared to traditional methods.
Experimental results validate the effectiveness of the proposed SLSTM model in improving prediction accuracy.
The seasonal time difference method effectively stabilized the traffic sequence, enhancing model performance.

Conclusions:

The SLSTM model provides an effective solution for accurate 5G network traffic forecasting.
The developed model can support informed decision-making in network management and resource allocation.
Accurate traffic prediction is crucial for managing the increasing demands of wireless access users.