<em></em>Nonlinearities Diminution in 40 Gb/s 256 QAM Radio over Fiber Link via Machine Learning Method

Abstract

Machine learning (ML) methodologies have been looked upon recently as a potential candidate for mitigating nonlinearity issues in optical communications. In this paper, we experimentally demonstrate a 40-Gb/s 256-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator. By utilizing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.

Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods

“…RL-SARSA method [13] and SVM methods [11] have been separately studied recently however, the comparison together with conventional method has not been evaluated. The experimental setup utilized is demonstrated in Fig.…”

“…The mitigation of nonlinearities in optical communications with ML is the most important application these days. Recently, RL-SARSA based ML method has been evaluated in [11][12] while SVM and KNN based ML methods have been utilized too in [13][14][15]. SBP method has also been evaluated for reducing the impairments in optical communication systems [16].…”

Machine learning methods for optical communications

“…Recently, RL-SARSA based ML method has been evaluated in [11][12] while SVM and K-Nearest Neighbours (KNN) based ML methods have been utilized too in [13][14][15]. SBP method has also been evaluated for reducing the impairments in optical communication systems [16].…”

Experimental evaluation of real-time sigma-delta radio over fiber system for fronthaul applications

“…During this era, C-RAN was proposed [2] to autonomies the baseband processing units and amalgamate them into a centralized baseband unit (BBU) pool, which simplifies each base station to a remote radio head (RRH), possibly relying on non-conventional beamforming strategies, which exploit the time [3, 4] as well as the frequency [5, 6] as an additional degree of freedom. In addition, C-RAN also enables the radio coordination among multiple cells [7–10]. This divides the architecture into two main segments, i.e.…”