Machine learning methods for optical communications

Radio over Fiber (RoF) technology has been realized in different forms ranging from analog to more complex forms [1-6]. The enhancement of capacity and wireless coverage has posed significant challenges to the existing optical and wireless access networks. Machine Learning (ML) methods have given a new direction to meet the ever-increasing challenges in fiber-optic communications. Since, ML-based methods are well known to perform exceptionally well in scenarios where it is too difficult to explicitly describe the underlying physics and mathematics of the problem and the numerical procedures available require significant computational resources/time.