Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey

Abstract

The trade-off between more user bandwidth and quality of service requirements introduces unprecedented challenges to the next generation smart optical networks. In this regard, the use of optical performance monitoring (OPM) and modulation format identification (MFI) techniques becomes a common need to enable the development of next-generation autonomous optical networks, with ultra-low latency and selfadaptability. Recently, machine learning (ML)-based techniques have emerged as a vital solution to many challenging aspects of OPM and MFI in terms of reliability, quality, and implementation efficiency. This paper surveys ML-based OPM and MFI techniques proposed in the literature. First, we address the key advantages of employing ML algorithms in optical networks. Then, we review the main optical impairments and modulation formats being monitored and classified, respectively, using ML algorithms. Additionally, we discuss the current status of optical networks in terms of MFI and OPM. This includes standards, monitoring parameters, and the available commercial products with their limitations. Second, we provide a comprehensive review of the available ML-based techniques for MFI, OPM, and joint MFI/OPM, describing their performance, advantages, and limitations. Third, we give an overview of the exiting ML-based OPM and MFI techniques for the emerging optical networks such as the new fiber-based networks that use future space division multiplexing techniques (e.g. few-mode fiber), the hybrid radioover-fiber networks, and the free space optical networks. Finally, we discuss the open issues, potential future research directions, and recommendations for the potential implementation of MLbased OPM and MFI techniques. Some lessons learned are presented after each section throughout the paper to help the reader identifying the gaps, weaknesses, and strengths in this field.

Bayesian Optimization for Nonlinear System Identification and Pre-Distortion in Cognitive Transmitters

“…Initially designed for digital radio communication, these schemes mostly consisted of training ANNs to learn the generalization of component/medium-induced distortions, which permitted the mitigation of impairments that cannot be analytically modeled. In the field of optical communications, the use of ML approaches has gained massive attention over the past decade, predominantly targeting tasks, such as fiber and transceiver nonlinearity mitigation [12], [22]- [24], but also extensively explored in optical performance monitoring techniques [25], [26] and network resource allocation strategies [27].…”

Optical Wireless Performance Monitoring Using Asynchronous Amplitude Histograms

“…This is achieved by maximizing the separation distance between the classes within the dataset. For regression, the obtained hyperplane is utilized as the estimation function [2]. This algorithm has the advantage of scaling well to high dimensional data.…”

“…This makes FSO a preferred solution in some applications when fiber installation is impossible or very expensive, such as installation in private properties, congested roads, rivers, etc. Monitoring fiber impairments, such as chromatic dispersion (CD) and polarization mode dispersion (PMD), have been extensively studied in the literature [2]. Because of signal transmission over free space, FSO signal is subjected to outdoor environmental conditions, which cause different impairments than fibers' impairments.…”

“…Recent studies show the power of using machine learning (ML) for OPM and baud rate identification in optical networks [2]. However, applying ML for FSO is still in its infancy, where only a few studies are reported in the literature.…”

Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning

“…Among them, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), deep reinforcement learning (DRL), end-to-end learning based on autoencoder, and their variants have made a distinctive contribution to fields such as machine vision, natural language processing, drug discovery, genomics, speech recognition, information retrieval, affective computing, and automatic deriving (Deng, 2014). Meanwhile, to promote the development of artificial intelligence (AI) in optical communication, the evolution from ML to DL is making major advances in a wide variety of applications in both physical and network layers (Fan et al, 2020;Häger and Pfister, 2020;Saif et al, 2020).…”