Machine Learning Techniques to Mitigate Nonlinear Phase Noise in Moderate Baud Rate Optical Communication Systems

Nonlinear phase noise (NLPN) is the most common impairment that degrades the performance of radio-over-fiber networks. The effect of NLPN in the constellation diagram consists of a shape distortion of symbols that increases the symbol error rate due to symbol overlapping when using a conventional demodulation grid. Symbol shape characterization was obtained experimentally at a moderate baud rate (250 MBd) for constellations impaired by phase noise due to a mismatch between the optical carrier and the transmitted radio frequency signal. Machine learning algorithms have become a powerful tool to perform monitoring and to identify and mitigate distortions introduced in both the electrical and optical domains. Clustering-based demodulation assisted with Voronoi contours enables the definition of non-Gaussian boundaries to provide flexible demodulation of 16-QAM and 4+12 PSK modulation formats. Phase-offset and in-phase and quadrature imbalance may be detected on the received constellation and compensated by applying thresholding boundaries obtained from impairment characterization through statistical analysis. Experimental results show increased tolerance to the optical signal-to-noise ratio (OSNR) obtained from clustering methods based on k-means and fuzzy c-means Gustafson-Kessel algorithms. Improvements of 3.2 dB for 16-QAM, and 1.4 dB for 4+12 PSK in the OSNR scale as a function of the bit error rate are obtained without requiring additional compensation algorithms. A classifier, is introduced and range A proposal of a bit-based SVM as a non-parameter nonlinear mitigation approach in the millimeter-wave Radio-over-Fiber (mm-RoF) system for different modulation formats is demonstrated. Experimental results outperform the k -means algorithm and show improvements of 1.2 dB for 16-QAM, 1.3 dB for 64-QAM, 1.8 dB for 16-APSK, and 1.3 dB for 32-APSK at BER of 1e-3 with the SVM detector, respectively. Convolutional An intelligent constellation diagram analyzer is proposed to implement both modulation format recognition (MFR) and OSNR estimation by using a CNN-based deep learning technique. The experimental results showed that the OSNR estimation errors for all the signals were less than 0.7 dB and the accuracy of MFR was 100%, proving the feasibility of the proposed scheme. Maximization of capacity over deployed links require operation regime estimation based on precise understanding of transmission conditions through linear and nonlinear SNR from the received signal. The extraction of NLPN and second-order statistical moments by a neural network is trained to estimate SNR from extensive realistic fiber transmissions. Measured performance of 0.04 and 0.2 dB of standard error for the linear and nonlinear SNRs, respectively,