PREDICTING BROADBAND NETWORK PERFORMANCE WITH AI-DRIVEN ANALYSIS

Abstract

Broadband network performance prediction is a critical task for optimizing bandwidth allocation, ensuring efficient data transmission, and enhancing the overall user experience. Traditional prediction methods such as linear regression and support vector machines (SVMs) are often ineffective in dynamic network environments due to their inability to adapt quickly to changes in network traffic patterns. This paper proposes an AI-driven framework for broadband network performance prediction, which leverages a Cuckoo Search (CS)-optimized neural network. We model network traffic as a time series and apply AI techniques to predict future traffic patterns, enabling proactive management of network resources. The proposed hybrid optimization algorithm fine-tunes the neural network's hyperparameters to enhance predictive accuracy and robustness. Extensive simulations demonstrate that our model outperforms conventional machine learning techniques in terms of accuracy, efficiency, and adaptability to evolving network conditions.