AI Driven Optimization in WDM (Wavelength Division Multiplexing) Systems

Wavelength Division Multiplexing (WDM) systems have transformed optical communication by facilitating the simultaneous transmission of multiple data streams over a single optical fibre. However, as data demands increase, traditional approaches to managing system performance face limitations. Artificial Intelligence (AI) presents an innovative solution, offering dynamic optimization for fault prediction, adaptive wavelength allocation, and real-time network reconfiguration. This paper explores the application of AI-driven techniques in enhancing WDM systems' efficiency, addressing key challenges such as nonlinear effects, polarization mode dispersion, and energy efficiency. Advanced AI algorithms ensure robustness, scalability, and seamless integration with next-generation optical networks.