Traffic Prediction in 5G Networks Using Machine Learning

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.