Preprint Article Version 1 This version is not peer-reviewed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 (doi: 10.20944/preprints201909.0159.v1). Hadi, M.U. RL-SARSA Machine Learning Based Analog Radio over Fiber System. Preprints 2019, 2019090159 (doi: 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.

Subject Areas

radio over fiber; nonlinearities mitigation; reinforcement learning (RL) method

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