PREDICTING BROADBAND NETWORK PERFORMANCE WITH AI-DRIVEN ANALYSIS

Abstract

This review article analyses the substantial influence of artificial intelligence (AI) in forecasting the performance of broadband networks. The examination encompasses crucial components such as network performance metrics, artificial intelligence approaches, challenges, and future prospects. Key metrics of a broadband network, such as throughput, latency, jitter, packet loss, scalability, and reliability, offer a fundamental comprehension of the aspects that impact the quality of the network. AI techniques, ranging from machine learning algorithms to deep learning models and hybrid approaches, are investigated for their potential to revolutionize network performance prediction. Real-world applications and case studies illustrate successful implementations across telecommunication service providers, content delivery networks, and edge computing environments. Despite these advancements, challenges persist, including data quality, model interpretability, and scalability. Solutions and advancements, such as enhanced data pre-processing and explainable AI, are discussed to address these challenges. Future trends, including AI for 6G networks and self-adaptive systems, offer insights into the evolving landscape of AI-driven broadband network optimization.

In simple terms, the fusion of artificial intelligence (AI) and network performance prediction signifies a fundamental shift in the management of connection. As researchers and industry specialists work on solving problems and investigating new developments, the possibility of a smarter, more secure, and more efficient broadband network system becomes more real. This sets the foundation for a new era of connectivity and communication.

Keywords: Artificial intelligence, broadband, performance, machine learning, deep learning