Article
Version 1
Preserved in Portico This version is not peer-reviewed
RL-SARSA Machine Learning Based Analog Radio over Fiber System
Version 1
: Received: 15 September 2019 / Approved: 16 September 2019 / Online: 16 September 2019 (10:37:01 CEST)
How to cite: Hadi, M.U. RL-SARSA Machine Learning Based Analog Radio over Fiber System. Preprints 2019, 2019090159. https://doi.org/10.20944/preprints201909.0159.v1 Hadi, M.U. RL-SARSA Machine Learning Based Analog Radio over Fiber System. Preprints 2019, 2019090159. https://doi.org/10.20944/preprints201909.0159.v1
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.
Keywords
radio over fiber; nonlinearities mitigation; reinforcement learning (RL) method
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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