Deep learning—a route to WDM high-speed optical networks

The evolution of Internet and communication systems is exponentially increasing the complexity in communication networks. This paved the way for the incorporation of artificial intelligence (AI), machine learning (ML), and recently deep learning (DL), in various aspects so as to improve the intelligence in communication networks. As DL techniques are superior on finding solutions to complex problems, they are been utilized for optical network applications. This paper aims to review the progress of AI in optical communication and the advancements from ML to DL. The paper also presents a review of nine research papers that utilized conventional DL techniques along with their contribution in optical and wavelength division multiplexing (WDM) networks. Toward the end of this paper, the DL algorithm is studied and its performance parameters are compared to evaluate the simulation outputs of its variants. The comparative analysis shows that in future, improvements in the outputs of WDMs can be made by applying DL-based algorithms.