Abstract
A support vector machine (SVM) based detection is applied to different equalization schemes for a data center interconnect link using coherent 64 GBd 64-QAM over 100 km standard single mode fiber (SSMF). Without any prior knowledge or heuristic assumptions, the SVM is able to learn and capture the transmission characteristics from only a short training data set. We show that, with the use of suitable kernel functions, the SVM can create nonlinear decision thresholds and reduce the errors caused by nonlinear phase noise (NLPN), laser phase noise, I/Q imbalances and so forth. In order to apply the SVM to 64-QAM we introduce a binary coding SVM, which provides a binary multiclass classification with reduced complexity. We investigate the performance of this SVM and show how it can improve the bit-error rate (BER) of the entire system. After 100 km the fiber-induced nonlinear penalty is reduced by 2 dB at a BER of 3.7 × 10 − 3 . Furthermore, we apply a nonlinear Volterra equalizer (NLVE), which is based on the nonlinear Volterra theory, as another method for mitigating nonlinear effects. The combination of SVM and NLVE reduces the large computational complexity of the NLVE and allows more accurate compensation of nonlinear transmission impairments.
Highlights
The use of machine learning techniques in optical communication networks is currently a popular research topic [1]
For single-carrier transmission self-phase modulation (SPM), caused by the Kerr effect and nonlinear phase noise (NLPN), which results from the interaction between the amplified spontaneous emission (ASE) noise of inline optical amplifiers and SPM can be regarded as the most limiting nonlinear distortions [3]
We investigate the I/Q imbalances in a B2B scenario according to Figure 3a at an optical signal-to-noise ratio (OSNR)
Summary
The use of machine learning techniques in optical communication networks is currently a popular research topic [1]. For single-carrier transmission self-phase modulation (SPM), caused by the Kerr effect and nonlinear phase noise (NLPN), which results from the interaction between the amplified spontaneous emission (ASE) noise of inline optical amplifiers and SPM can be regarded as the most limiting nonlinear distortions [3] These impairments cannot be compensated with conventional FFE structures. Previous approaches for the compensation of these nonlinear impairments focused on replacing the FFE by a nonlinear Volterra equalizer (NLVE) [4] or, if the fiber parameters are known, to replace the EDC with a digital backpropagation algorithm to compensate for linear and nonlinear effects simultaneously [5] After using these methods, a signal detection with conventional linear decision thresholds takes place. We show that the combination of SVM and NLVE can reduce the computational complexity of the NLVE and that this combination allows a more accurate compensation of the impairments that arise in an optical transmission system that is operated in the nonlinear regime
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