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In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction.
Datacenter networks are becoming crucial foundations for our information technology based society. However, commercial datacenter infrastructure is often unavailable to researchers for conducting experiments. In this work, we therefore elaborate on the possibility of combining commercial hardware and simulation to illustrate the scalability and performance of datacenter networks. We simulate a Datacenter...
Gaussian process regression is numerically and experimentally investigated to predict the bit error rate of a 24 × 28 GBd QPSK WDM system. The proposed method produces accurate predictions from multi-dimensional and sparse measurement data.
Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, whereas the nonlinear signal processing algorithms, from the machine learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal detection. In this paper, a brief overview of the various machine learning methods and their application in...
Linear signal processing algorithms are effective in combating linear fibre channel impairments. We demonstrate the ability of machine learning algorithms to combat nonlinear fibre channel impairments and perform parameter extraction from directly detected signals.
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