Predicting Cellular Traffic in Real Time

Paper Title:

Cellular Traffic Prediction Using Online Prediction Algorithms

Published on:

8 May 2024

Primary Category:

Systems and Control

Paper Authors:

Hossein Mehri,

Hao Chen,

Hani Mehrpouyan

Bullets

Key Details

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Examines live cellular traffic prediction using machine learning models

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Compares performance of Fast LiveStream Prediction (FLSP) and rolling algorithms

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FLSP doubles bandwidth efficiency in asynchronous reporting

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FLSP enhances accuracy and reduces processing load

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Provides model analysis and recommendations for optimization

Explore the topics in this paper

bandwidth efficiency

cell networks

live prediction algorithms

machine learning models

traffic forecasting

AI generated summary

Predicting Cellular Traffic in Real Time

This paper investigates methods for accurately forecasting cellular network traffic volumes in real-time scenarios, examining two live prediction algorithms applied to machine learning models. The study reveals that the Fast LiveStream Prediction (FLSP) algorithm enhances bandwidth efficiency, accuracy, and processing load compared to traditional rolling algorithms when data is reported asynchronously across the network. Through analysis and simulation, the paper provides guidance on model selection and data gathering strategies to optimize prediction performance under constraints.