A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> F. Random Forest <s> We investigate a machine-learning technique that predicts whether the bit-error-rate of unestablished lightpaths meets the required threshold based on traffic volume, desired route and modulation format. The system is trained and tested on synthetic data. <s> BIB001 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> F. Random Forest <s> Predicting the quality of transmission (QoT) of a lightpath prior to its deployment is a step of capital importance for an optimized design of optical networks. Due to the continuous advances in optical transmission, the number of design parameters available to system engineers (e.g., modulation formats, baud rate, code rate, etc.) is growing dramatically, thus significantly increasing the alternative scenarios for lightpath deployment. As of today, existing (pre-deployment) estimation techniques for light-path QoT belong to two categories: “exact” analytical models estimating physical-layer impairments, which provide accurate results but incur heavy computational requirements, and margined formulas, which are computationally faster but typically introduce high link margins that lead to underutilization of network resources. In this paper, we explore a third option, i.e., machine learning (ML), as ML techniques have already been successfully applied for optimization and performance prediction of complex systems where analytical models are hard to derive and/ or numerical procedures impose high computational burden. We investigate a ML classifier that predicts whether the bit error rate of unestablished lightpaths meets the required system threshold based on traffic volume, desired route, and modulation format. The classifier is trained and tested on synthetic data and its performance is assessed over different network topologies and for various combinations of classification features. Results in terms of classifier accuracy are promising and motivate further investigation over real field data. <s> BIB002 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> F. Random Forest <s> Abstract This paper presents a new approach when dealing with availability and reliability of wireless optics by employing several prediction methods for RSSI parameter in hard FSO/RF switching methodology. Despite the fact that FSO link signal and its overall communication is strongly affected by weather conditions (fog, heavy rains, snow, etc.), the strength of RSSI will finally result in link availability or a signal fade. Eventually, there is almost unlimited number of weather states which have either positive or negative impact on RSSI and FSO link, respectively. However, the way that nature and surrounding world are organized is behavioral patterns which can be found everywhere. Finding appropriate pattern is mostly matter of scale of observed group of parameters. Machine learning, data mining or big data are interchangeable terms used in pattern finding processes. We have decided to leverage the power of machine learning algorithms to predict RSSI parameter base on measured weather parameters in this paper. First chapter is dedicated to a deep analysis of negative effects encountered when light propagates through an atmospheric channel and deteriorate optical signal. In following, we paid attention to practical design and assembly of a monitoring device. The further discussion evaluates the methodologies of so called soft and hard switching between FSO and RF (Radio Frequency) link. The primary attention was paid to the hard switching technique and to relations between the received signal strength indicator with the parameters influencing atmospheric channel. Selected machine learning methods and their modifications (AdaBoostClassifier with DecisionTreeClassifier, RandomForestClassifier, GradientBoostingClassifier) were applied in this analysis in order to estimate received optical power parameter based on series of weather parameters. Such a process involves classification and regression methods. This paper presents finding out an optimal scheme for input matrix as an essential for machine learning training process. Considered time and calculation requirements are compensated by employing automation tools in overall process. Finally, we conclude and evaluate the outcomes of machine learning models in FSO/RF hard switching process. <s> BIB003
Definition: When the input point of estimated point belongs to the insensitive cluster, the KNN could not determine the neighborhood of the detection point, so a method that does not rely on distance calculation should be used. Random forest algorithm is a bagging algorithm based on decision tree model, a set of decision trees = { 1 2 ⋯ }. Random forest is an ML technique that develops training based on the constructed decision trees (forests); its output is the mode of the trees output classes (in classification) or the mean of the trees output predictions (in regression) (Fig. 15.) . Network: Nowadays, considering convenience of ANN, and SVM, other ML techniques are not so much considered in ML for OC applications. However, some decision trees Classifier ML algorithms such as Ada Boost, Decision Tree, Random Forest, and Gradient Boosting. Random forest could be used for estimation of BER of an unestablished link or QoT BIB001 BIB002 , and estimation of received optical power parameter BIB003 . Random Forest does not have any parameter to be tuned and its performance is completely related to the structure of the presented trees and forest.