Design and Improvement of the Dense Wavelength-Division Multiplexing (DWDM) for Radio over Fiber system with Machine Learning

This proposed study explores the incorporation of Dense Wavelength-Division Multiplexing (DWDM) technology with Machine Learning (ML) to improve Radio over Fiber (RoF) systems for high-speed optical communications. An evaluation of a 16-channel DWDM-RoF configuration was conducted using OptiSystem simulations, concentrating on metrics such as Bit Error Rate (BER) and Q-factor. The study attained substantial enhancements in system performance by the implementation of TensorFlow-based machine learning algorithms, resulting in optimized frequency allocation, diminished bit error rate (BER), and elevated Q-factor. These achievements underscore the capability of machine learning to dynamically adjust to network conditions, alleviate transmission impairments, and enhance scalability and dependability. The results highlight the feasibility of DWDM-RoF systems as a fundamental component of contemporary telecommunications, providing creative ways to address the increasing demand for efficient optical communication networks.