KNN, k-Means and Fuzzy c-Means for 16-QAM Demodulation in Coherent Optical Systems

2019 IEEE Colombian Conference on Communications and Computing (COLCOM) Pub Date : 2019-06-05 DOI:10.1109/ColComCon.2019.8809116

Alejandro Escobar Pérez, J. J. Granada Torres

引用次数: 2

Abstract

In this paper, Machine Learning (ML) techniques such as k-Nearest Neighbors (KNN), k-Means and Fuzzy c-Means (FCM) are implemented in coherent optical system with DSP-based receiver. Nyquist single carrier optical transmission at 32 Gbaud is simulated in VPIDesignSuite Software in co-simulation with Matlab. Simulations results shows gains up to 1 dB and 2 dB using ML techniques at 50 km of optical link with 100 kHz and 25 kHz of laser linewidth, respectively. Besides, it is demonstrated that ML techniques can be effectively used as a nonsymmetrical demodulation (NSD) method.

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相干光学系统16-QAM解调的KNN、k-Means和模糊c-Means

本文将k-最近邻(KNN)、k-均值(k-Means)和模糊c-均值(FCM)等机器学习技术应用于基于dsp的相干光学系统中。在VPIDesignSuite软件中与Matlab联合仿真，模拟了32 Gbaud下的奈奎斯特单载波光传输。仿真结果表明，在激光线宽为100 kHz和25 kHz的50 km光链路上，使用ML技术分别获得了1 dB和2 dB的增益。此外，还证明了机器学习技术可以有效地用作非对称解调(NSD)方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2019 IEEE Colombian Conference on Communications and Computing (COLCOM)

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