Free space optic channel monitoring using machine learning.

Free space optic (FSO) is a type of optical communication where the signal is transmitted in free space instead of fiber cables. Because of this, the signal is subject to different types of impairments that affect its quality. Predicting these impairments help in automatic system diagnosis and building adaptive optical networks. Using machine learning for predicting the signal impairments in optical networks has been extensively covered during the past few years. However, for FSO links, the work is still in its infancy. In this paper, we consider predicting three channel parameters in FSO links that are related to amplified spontaneous emission (ASE) noise, turbulence, and pointing errors. To the best of authors knowledge, this work is the first to consider predicting FSO channel parameters under the effect of more than one impairment. First, we report the performance of predicting the FSO parameters using asynchronous amplitude histogram (AAH) and asynchronous delay-tap sampling (ADTS) histogram features. The results show that ADTS histogram features provide better prediction accuracy. Second, we compare the performance of support vector machine (SVM) regressor and convolutional neural network (CNN) regressor using ADTS histogram features. The results show that CNN regressor outperforms SVM regressor for some cases, while for other cases they have similar performance. Finally, we investigate the capability of CNN regressor for predicting the channel parameters for three different transmission speeds. The results show that the CNN regressor has good performance for predicting the OSNR parameter regardless of the value of transmission speed. However, for the turbulence and pointing errors, the prediction under low speed transmission is more accurate than under high speed transmission.

Modulation format identification of optical signals: an approach based on singular value decomposition of Stokes space projections.

In this paper, two Stokes space (SS) analysis schemes for modulation format identification (MFI) are proposed. These schemes are based on singular value decomposition (SVD) and Radon transform (RT) for feature extraction. The singular values (SVs) are extracted from the SS projections for different modulation formats to discriminate between them. The SS projections are obtained at different optical signal-to-noise ratios (OSNRs) ranging from 11 to 30 dB for seven dual-polarized modulation formats. The first scheme depends on the SVDs of the SS projections on three planes, while the second scheme depends on the SVDs of the RTs of the SS projections. Different classifiers including support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN) for MFI based on the obtained features are used. Both simulation and experimental setups are arranged and tested for proof of concept of the proposed schemes for the MFI task. Complexity reduction is studied for the SVD scheme by applying the decimation of the projections by two and four to achieve an acceptable classification rate, while reducing the computation time. Also, the effect of the variation of phase noise (PN) and state of polarization (SoP) on the accuracy of the MFI is considered at all OSNRs. The two proposed schemes are capable of identifying the polarization multiplexed modulation formats blindly with high accuracy levels up to 98%, even at low OSNR values of 12 dB, high PN levels up to 10 MHz, and SoP up to 45°.