Cellular Traffic Prediction Using Online Prediction Algorithms

TL;DR

This paper evaluates online prediction algorithms, including the novel FLSP, for real-time cellular traffic forecasting in 5G networks, demonstrating improved accuracy and bandwidth efficiency under different data reporting scenarios.

Contribution

It introduces the FLSP algorithm for cellular traffic prediction and analyzes its performance and resource efficiency compared to existing methods.

Findings

FLSP halves bandwidth needs in asynchronous reporting.

FLSP improves prediction accuracy over conventional algorithms.

Analysis of complexity and memory requirements for various models.

Abstract

The advent of 5G technology promises a paradigm shift in the realm of telecommunications, offering unprecedented speeds and connectivity. However, the efficient management of traffic in 5G networks remains a critical challenge. It is due to the dynamic and heterogeneous nature of network traffic, varying user behaviors, extended network size, and diverse applications, all of which demand highly accurate and adaptable prediction models to optimize network resource allocation and management. This paper investigates the efficacy of live prediction algorithms for forecasting cellular network traffic in real-time scenarios. We apply two live prediction algorithms on machine learning models, one of which is recently proposed Fast LiveStream Prediction (FLSP) algorithm. We examine the performance of these algorithms under two distinct data gathering methodologies: synchronous, where all network cells report statistics simultaneously, and asynchronous, where reporting occurs across consecutive time slots. Our study delves into the impact of these gathering scenarios on the predictive performance of traffic models. Our study reveals that the FLSP algorithm can halve the required bandwidth for asynchronous data reporting compared to conventional online prediction algorithms, while simultaneously enhancing prediction accuracy and reducing processing load. Additionally, we conduct a thorough analysis of algorithmic complexity and memory requirements across various machine learning models. Through empirical evaluation, we provide insights into the trade-offs inherent in different prediction strategies, offering valuable guidance for network optimization and resource allocation in dynamic environments.