RL-SARSA Machine Learning Based Analog Radio over Fiber System

Abstract

We propose a 10-Gb/s 64-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes Reinforcement Learning (RL) SARSA based decision method to indicate an effective decision which mitigates nonlinearity. By utilizing RL-SARSA algorithm, the results demonstrate that significant reduction can be obtained in terms of bit error rate.

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].…”