Traffic Prediction in 5G Networks Using Machine Learning
Authors: Mehri Hossein 
Published: February 2025

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, including
bursty patterns, varying user behaviors, and diverse applications, all of which
demand highly accurate and adaptable prediction models to optimize network
resource allocation and management. This dissertation investigates the intricate
domain of traffic prediction within 5G networks, addressing the specific
challenges posed by both massive machine type communication (mMTC) networks and
5G cellular networks.

The first segment of this research focuses on mMTC networks, where the
event-driven and bursty nature of traffic patterns poses a formidable obstacle
to accurate prediction. Forecasting bursty traffic in such environments is a
non-trivial task due to the inherent randomness of events, and these challenges
intensify within live network environments. Consequently, there is a compelling
imperative to design an efficient and agile framework capable of assimilating
continuously collected data from the network and accurately forecasting bursty
traffic in mMTC networks. The first section of this dissertation is an attempt
to addresses these challenges by presenting a machine learning-based framework
tailored for forecasting bursty traffic in multi-channel slotted ALOHA networks.
We develop a new low-complexity online prediction algorithm that dynamically
updates the states of the long-term short-term memory (LSTM) network by
leveraging frequently collected data from the mMTC network. Moreover, to
evaluate the performance of the proposed framework, we synthesized a realistic
mMTC traffic considering both uniform and bursty traffic patterns. In this
setup, we considered a single base station and thousands of devices organized
into groups with distinct traffic-generating characteristics.

Transitioning to the realm of 5G cellular networks, we explore the efficacy of
convolutional neural network (CNN)-LSTM and convolutional LSTM (ConvLSTM) models
for traffic prediction. Building upon insights from the preceding section, we
integrate the proposed live prediction algorithm into these models. Results
demonstrate a notable enhancements in prediction accuracy and computational
efficiency, signifying a promising avenue for traffic management in 5G cellular
networks. Moreover, we study the performance of the proposed live prediction
algorithm under the various data collection scenarios.

In the final section, we introduce an innovative compression framework to
mitigate the data transfer overhead between base stations and centralized nodes.
We propose a novel user-specific asymmetric autoencoder (AE)-based data
compression framework tailored for data transfer in 5G cellular networks.
Leveraging user-specific local AE models and a centralized joint decoder, our
framework aims to efficiently compress traffic data while preserving the
reconstruction accuracy. In the proposed framework, we utilize a simplified FFNN
models in local AEs and CNN layers in the centralized decoder to simultaneously
decode the data of all cells by leveraging the spatio-temporal correlations in
traffic patterns.

Simulation results and complexity analysis highlight the superiority of our
proposed live prediction algorithm in terms of both accuracy and computational
efficiency, making it well-suited for time-critical live scenarios. In the first
section, simulations conducted on synthesized mMTC traffic demonstrate the
remarkable accuracy of our machine learning approach in long-term predictions
compared to traditional methods, all while imposing minimal additional
processing load on the system. The application of our proposed live prediction
algorithm to forecast cellular traffic in the second section reveals its
superior robustness under both synchronous and asynchronous data gathering
scenarios, outperforming traditional methods. Moreover, in asynchronous data
gathering scenarios, our algorithm demonstrates the potential to halve the
required bandwidth for reporting traffic statistics, illustrating another
advantageous aspect of the proposed algorithm. Finally, the performance
evaluation of our proposed data compression method on the Telecom Italia dataset
in the third section underscores the effectiveness of our approach, achieving
superior performance compared to symmetric universal AEs. Furthermore, our
framework exhibits reduced complexity, positioning it as a promising solution
for practical applications in 5G networks.

In summary, this dissertation presents novel methodologies and frameworks aimed
at tackling the multifaceted challenges of traffic prediction within diverse 5G
network environments. Through the integration of advanced prediction algorithms
with innovative data compression techniques, the proposed solutions pave the way
for resilient and efficient traffic management in 5G networks, offering
promising avenues for future research and implementation.