LITERATURE REVIEW ON AI-DRIVEN OPTIMIZATION IN BROADBAND NETWORKS

Abstract

The increasing demand for high-quality broadband services has necessitated the adoption of Artificial Intelligence (AI) techniques to enhance network performance and ensure optimal Quality of Service (QoS). This literature review explores the role of AI in broadband network optimization, focusing on machine learning models such as Support Vector Machines (SVM), Random Forests (RF), and K-Nearest Neighbors (KNN), along with reinforcement learning approaches like Q-learning. The review discusses how AI-driven methodologies contribute to improved network latency, jitter, throughput, and packet loss, while also examining future directions in integrating AI with 5G and edge computing to create self-optimizing broadband networks.