Overcoming degradation in spatial multiplexing systems with stochastic nonlinear impairments

Meta-ensemble learning for OPM in FMF systems

Optical performance monitoring (OPM) is crucial for facilitating the management of future few-mode fiber (FMF)-based transmissions. OPM deploys fault detection and link diagnosis by measuring the physical layer states and provides feedback to the controller. Recently, machine learning (ML) has gained a lot of attention for OPM, and various ML algorithms were developed, wherein the selection of the proper method is a challenge. Ensemble learning (EL) solves this challenge by combining different ML models; however, this simultaneous employment suffers from increased complexity and dependency on the performance of each individual model. Meta-ensemble learning (MEL) provides a promising solution by intelligently selecting the proper ensemble at each instance. In this work, we employ MEL for OPM in FMF systems. We compare the proposed MEL-based OPM method with naive EL (NEL), which is a well-known EL method. The obtained results indicate that proposed MEL-based OPM method provides better performance with the loss data set size compared with NEL-based OPM. Furthermore, the proposed MEL-based OPM method does not need the feature preprocessing, which is an essential step in other ML algorithms such as NEL-based OPM.

Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review

This paper provides an overview of latest progress on the novel advanced digital signal processing (DSP) techniques for long-haul mode division multiplexing (MDM) systems with high capacity. Space-division multiplexing (SDM) techniques have been developed for a period to increase the capacity of optical communication system by at least one order of magnitude through MDM techniques using few-mode fibers (FMFs) or multi-core multiplexing (MCM) using multi-core fibers (MCFs). The signals in MDM links are mainly impaired by the linear and nonlinear effects in FMFs, making DSP techniques become necessary to undo these impairments. In this paper, we not only review the advanced multiple-input multiple-output (MIMO) DSP techniques for compensating linear impairments in FMFs, but also enclose the state of the art of novel DSP techniques to deal with nonlinear effects. Firstly, we introduce the MIMO schemes for equalizing modal crosstalk and modal dispersion. Then, we focus on the fast tracking of time-varying (TV) channels in FMF links through frequency-domain (FD) recursive least square (RLS) algorithm. Besides, we also cover the mainstream DSP solutions for mode-dependent loss (MDL) and several possible methods to compensate nonlinearity in FMF. Moreover, artificial intelligence (AI) technologies are also discussed for its high nonlinearity tolerance and may bring a revolution in MDM systems on the process of channel equalization, link monitoring, etc. In the end, a brief conclusion and perspective will be provided.