A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Expectation Maximization <s> In this paper, tools from machine learning community, such as Bayesian filtering and expectation maximization parameter estimation, are presented and employed for laser amplitude and phase noise characterization. We show that phase noise estimation based on Bayesian filtering outperforms conventional time-domain approach in the presence of moderate measurement noise. Additionally, carrier synchronization based on Bayesian filtering, in combination with expectation maximization, is demonstrated for the first time experimentally. <s> BIB001 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> B. Expectation Maximization <s> Techniques from the machine learning community are reviewed and employed for laser characterization, signal detection in the presence of nonlinear phase noise, and nonlinearity mitigation. Bayesian filtering and expectation maximization are employed within nonlinear state-space framework for parameter tracking. <s> BIB002
Definition: The drawback of K-means clustering is its hard decision boundaries that assign data point only to one cluster; however, it might lie between two or more clusters. The EM clustering assigns a probability of belonging to a cluster to the data point rather than completely assigning it to a particular cluster. This technique assumes that the data points have a superposition of K jointly Gaussian probability distributions with different means and covariance (Gaussian mixture model) (GMM) (Fig. 18.d) . Actually, in both of them, n iterative refinement approach along with cluster centers are used to model the data; however, clusters of k-means have comparable spatial extent, but EM clusters have different shapes. Fiber OC: Bayesian filtering in combination with EM is used for mitigating the laser amplitude and phase noise BIB001 , as well as nonlinearity compensation, carrier recovery, and nano-scale device characterization BIB002 . These technique use Bayesian analysis and evaluate the content of an incoming optical signal and determine the probability that it constitutes amplitude or phase noise.