Predicting Broadband Network Performance with AI-Driven Analysis

Abstract

Broadband network performance prediction is essential for optimizing bandwidth allocation, ensuring efficient data transmission, and enhancing user experience. Traditional prediction methods often lack accuracy and adaptability in dynamic network environments. This paper proposes an AI-driven framework for broadband network performance prediction, leveraging a Cuckoo Search (CS) optimized neural network. We model network traffic as a time series and apply AI techniques to forecast future traffic patterns. The proposed hybrid optimization algorithm fine-tunes the neural network’s hyperparameters, enhancing predictive accuracy and robustness. Extensive simulations demonstrate that our model outperforms conventional machine learning approaches in terms of accuracy, efficiency, and adaptability to evolving network conditions.