A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> A. Peak search, c-means, hieratical clustering <s> A Fractionally-Spaced Clustering Based Equalizer is proposed for the electronic equalization of optical channels. Equalization is treated as a classification task. The proposed approach outperforms the recursive Volterra DFE, as it is demonstrated by simulation. <s> BIB001 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> A. Peak search, c-means, hieratical clustering <s> In this Letter, we develop the Stokes space-based method for modulation format identification by combing power spectral density and a cluster analysis to identify quadrature amplitude modulation(QAM) and phase-shift keying(PSK) signals. Fuzzy c-means and hierarchical clustering algorithms are used for the cluster analysis.Simulations are conducted for binary PSK, quadrature PSK, 8PSK, 16-QAM, and 32-QAM signals. The results demonstrate that the proposed technique can effectively classify all these modulation formats, and that the method is superior in lowering the threshold of the optical signal-to-noise ratio. Meanwhile, the proposed method is insensitive to phase offset and laser phase noise. <s> BIB002 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> A. Peak search, c-means, hieratical clustering <s> A Stokes-space modulation format classification (MFC) technique is proposed for coherent optical receivers by using a non-iterative clustering algorithm. In the clustering algorithm, two simple parameters are calculated to help find the density peaks of the data points in Stokes space and no iteration is required. Correct MFC can be realized in numerical simulations among PM-QPSK, PM-8QAM, PM-16QAM, PM-32QAM and PM-64QAM signals within practical optical signal-to-noise ratio (OSNR) ranges. The performance of the proposed MFC algorithm is also compared with those of other schemes based on clustering algorithms. The simulation results show that good classification performance can be achieved using the proposed MFC scheme with moderate time complexity. Proof-of-concept experiments are finally implemented to demonstrate MFC among PM-QPSK/16QAM/64QAM signals, which confirm the feasibility of our proposed MFC scheme. <s> BIB003 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> A. Peak search, c-means, hieratical clustering <s> Fiber-induced intra- and interchannel nonlinearities are experimentally tackled using blind nonlinear equalization (NLE) by unsupervised machine-learning-based clustering (MLC) in ∼46-Gb/s single-channel and ∼20-Gb/s (middle-channel) multichannel coherent multicarrier signals (orthogonal frequency-division multiplexing (OFDM) based). To that end, we introduce, for the first time, hierarchical and fuzzy-logic C -means (FLC)-based clustering in optical communications. It is shown that among the two proposed MLC algorithms, FLC reveals the highest performance at optimum launched optical powers (LOPs), while at very high LOPs, hierarchical can compensate more effectively nonlinearities only for low-level modulation formats. When employing binary phase-shift keying and quaternary phase-shift keying, FLC outperforms K-means, fast-Newton support vector machines, supervised artificial neural networks, and NLE with deterministic Volterra analysis. In particular, for the middle channel of a QPSK wavelength-division multiplexing coherent optical OFDM system at optimum −5 dBm of LOP and 3200 km of transmission, FLC outperforms Volterra-NLE by 2.5 dB in Q-factor. However, for a 16-QAM single-channel system at 2000 km, the performance benefit of FLC over inverse Volterra-series transfer function reduces to ∼0.4 dB at a LOP of 2 dBm (optimum). Even when using novel sophisticated clustering designs in 16 clusters, no more than additional ∼0.3-dB Q-factor enhancement is observed. Finally, in contrast to the deterministic Volterra-NLE, MLC algorithms can partially tackle the stochastic parametric noise amplification . <s> BIB004
Definition: In spite of supervised algorithms which are famous by their names (e.g. SVM, ANN, etc.), clustering algorithms, are mostly defined under the shelf of clustering, and mostly the programmer decides the algorithm what to do (a) (b) (c) Fig.14. kNN. a. initial data, b. calculate distance, and c. finding neighbors and voting for labels. (based on some logical rules). For example, in non-iterative density peak search clustering algorithm (Fig.17.c) BIB003 two simple parameters are used for seeking the cluster centers without any iteration, in contrast Fuzzy c-means does the same thing iteratively; in opposite to both of these techniques, Hieratical clustering solves the problem blindly BIB002 . In another clustering algorithm, called fractionally spaced clustering, Viterbi type procedure along with Mahalanobis distance metric is used, dictated by the specific transitions among clusters BIB001 . So, the flexibility of unsupervised (clustering) algorithms is high, and in addition they do not require label, which makes them really helpful. However, they have one single aim; find the center and grow up the cluster. Fiber OC: The k-means ( Fig.18 .a) and Principle Component Analysis are the mostly used unsupervised algorithms in OC; however, there are some other techniques, which could not lie in a major group e.g. Hierarchical ( Fig.17.a) and Fuzzylogic C-means ( Fig.17.b) clustering. These techniques try to find hidden patterns or groups in data, and have lower complexity than supervised algorithms; however, their drawback is taking more convergence time. In spite of the generality and wideness of these techniques, they have done well compared with conventional methods in MFI BIB003 BIB002 , NLE BIB001 , etc. Furthermore, in contrast to traditional algorithms (e.g. deterministic Volterra NLE), these techniques could combat stochastic parametric noise amplification, and reduce complexity and processing time by deploying blindly BIB004 .