5G new radio and fog computing scalability and QoS management

Abstract

This thesis investigates the advancements in fifth-generation (5G) mobile technology, with a particular focus on the 5G Fog Radio Access Network (5G-FRAN) architecture and resource optimization. The proliferation of Internet of Things (IoT) deployments has heightened the demand for latency-efficient applications positioned closer to end-users, necessitating a critical evaluation of resource management in 5G networks. The research begins with a comprehensive literature review of Cloud Radio Access Network (CRAN) architectures and their variations. CRAN is pivotal for reducing operational and maintenance costs for mobile network operators. However, the exponential growth of IoT devices, surpassing 900 million by 2023, exposes the limitations of CRAN’s centralized network structure, particularly its vulnerability to fronthaul congestion. To address these challenges, the 5G-FRAN architecture offers significant enhancements, including improved mobility, optimized radio resource allocation, superior service quality, and low latency. Despite these advancements, challenges such as resource allocation, end-to-end latency, and energy efficiency persist. Consequently, this thesis proposes machine learning-based resource management schemes to mitigate these challenges and meet industrial requirements effectively. Thus, a novel cell association technique is developed to manage bandwidth in 5G-FRANs by accounting for subscriber mobility. The proposed approach employs probability distributions of time intervals and cell identifications to construct a Hidden Semi-Markov Model (HsMM) that predicts the optimal next cell for meeting downstream rate requirements. The model addresses critical issues, including ping-pong effects, missing data, and restrictive wireless environments, thereby enhancing user traffic prediction. Results demonstrate that the HsMM-based method achieves high accuracy with respect to observation time intervals and significantly improves user satisfaction compared to existing systems. Furthermore, Eigen decomposition (ED)-based feature extraction techniques, specifically Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are applied to attributes derived from the dataset. The extracted features are then utilized in machine learning models to detect and classify service segments. Hence, in the thesis a detailed description of the dataset and the implementation of the ED-based feature extraction techniques is provided. The workflow of the machine learning models is illustrated using block diagrams, followed by an in-depth explanation of the experimental design. Experiments were conducted using a range of machine learning algorithms, including Decision Tree (DT), Support Vector Machines (SVM), K-Nearest Neighbours (K-NN), Random Forest (RF), Artificial Neural Networks (ANN), and their combinations with PCA and LDA. These models were tested on fog computing application datasets featuring multi-class classification scenarios and noise-injected environments. Thereafter, a thorough analysis of the experimental results, focusing on the impact of dimensionality reduction on model accuracy, efficiency, and resilience to noise in various machine learning configurations. This thesis makes several notable contributions. It provides an in-depth analysis of the latest 5G network architectures and demonstrates the efficacy of HsMM in optimizing limited spectrum resources using machine learning techniques. Additionally, the study introduces and evaluates two ED-based feature extraction techniques (PCA and LDA) across multiple machine learning models, significantly reducing computational complexity while improving model performance. These techniques enable efficient processing of large datasets, enhancing the accuracy, reliability, and scalability of machine learning models in 5G-FRAN applications.