Machine Learning for Operational Cost Optimization in Optical and Wireless Networks

Abstract

Machine learning methods are emerging as essential tools for controlling operational expenses in optical and wireless networks. These networks are growing in scale and complexity as demand for bandwidth and low latency continues to rise. Traditional cost optimization strategies often rely on static rules or limited heuristics that do not adapt well to dynamic traffic behaviors. Machine learning offers new abilities to forecast demand, classify load conditions, and recommend intelligent resource adjustments that reduce energy use, minimize congestion, and improve system reliability. This work investigates how diverse models can be applied to real operational challenges in optical and wireless infrastructures and presents a comprehensive methodology for integrating cost aware intelligence into the network fabric.