Machine-Learning-Aided Optical Fiber Communication System

Abstract

The fiber optical network offers high speed, large bandwidth, and a high degree of reliability. However, the development of optical communication technology has hit a bottleneck due to several challenges such as energy loss, cost, and system capacity approaching the Shannon limit. As a powerful tool, machine learning technology provides a strong driving force for the development of various industries and greatly promotes the development of society. Machine learning also provides a new possible solution to achieve greater transmission capacities and longer transmission distances in optical communications. In this article, we introduce the application of machine learning in optical communication network systems. Three use cases are presented to evaluate the feasibility of our proposed architecture. In the transmission layer, the principal-component-based phase estimation algorithm is used for phase noise recovery in coherent optical systems, and the K-means algorithm is adopted to reduce the influence of nonlinear noise in probabilistic shaping systems. As for the network layer, the long short-term memory algorithm and the genetic algorithm are suitable for making traffic predictions and determining reasonable placement locations of remote radio heads in centralized radio access networks. Extensive simulations and experiments are conducted to evaluate the proposed algorithm in comparison to the state-of-the-art schemes. The results show the performance of three use cases. Machine learning algorithms applied to the transmission layer can greatly promote the performance of digital signal processing without increasing the complexity. Machine learning algorithms applied to the network layer can provide a more appropriate channel allocation plan in the era of high-speed communication. Ultimately, the intent of this article is to serve as a basis for stimulating more research in machine learning in optical communications.