A Blind Nonlinearity Compensator Using DBSCAN Clustering for Coherent Optical Transmission Systems

Elias Giacoumidis,Liam P Barry,Sean O’Duill,Kevin Mcguinness,Paul F Whelan,Yi Lin,Mutsam Jarajreh

doi:10.3390/app9204398

Abstract

Coherent fiber-optic communication systems are limited by the Kerr-induced nonlinearity. Benchmark optical and digital nonlinearity compensation techniques are typically complex and tackle deterministic-induced nonlinearities. However, these techniques ignore the impact of stochastic nonlinear distortions in the network, such as the interaction of fiber nonlinearity with amplified spontaneous emission from optical amplification. Unsupervised machine learning clustering (e.g., K-means) has recently been proposed as a practical approach to the blind compensation of stochastic and deterministic nonlinear distortions. In this work, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is employed, for the first time, for blind nonlinearity compensation. DBSCAN is tested experimentally in a 40 Gb/s 16 quadrature amplitude-modulated system at 50 km of standard single-mode fiber transmission. It is shown that at high launched optical powers, DBSCAN can offer up to 0.83 and 8.84 dB enhancement in Q-factor when compared to conventional K-means clustering and linear equalisation, respectively.

Highlights

Coherent optical communications have been proposed as a viable solution for maximising the signal capacity in both short-reach and long-haul communications [1]
Unsupervised machine learning clustering has been recently introduced in optical communications for blind nonlinear equalisation (BNLE)
16 quadrature amplitude modulation coherent signal transmitted at km, incorporating machine learning clustering

Summary

Introduction

Coherent optical communications have been proposed as a viable solution for maximising the signal capacity in both short-reach and long-haul communications [1]. Unsupervised machine learning clustering has been recently introduced in optical communications for blind (training data-free) nonlinear equalisation (BNLE) Such unsupervised algorithms can tackle stochastic nonlinearities and include, for example, fuzzy logic C-means [11], K-means [11,12], hierarchical [11], affinity propagation [13], and Gaussian mixture [14] clustering. The steps related to the conventional and modified DBSCAN listed below, where theif conventional is considered (step 5 points below—1st algorithm converges until all constellation haveloop) been[17,18]: allocated to a cluster or labelled as “noisy”. Core point, the DBSCAN algorithm “scans” for the rest of the unvisited constellation.

DBSCAN

Experimental Setup

Results

Results indicate that

Conclusions