# Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems
###### Abstract
Such limitations degrade the optical communication system's performance. Therefore, it is important to have a feature in the system to monitor the amount and type of distortion in the optical channel. Such a feature could be used in improving the system performance using techniques such as adaptive modulation on the transmitter side or designing appropriate mitigation algorithms on the receiver side based on the type and amount of the distortion. This leads to having a self-aware and self-adaptive communication system. Various conventional techniques were proposed in the literature for monitoring different channel impairments such as ASE, CD, and polarization mode dispersion (PMD) [8]. However, such techniques have technical limitations and/or are costly. Recently, machine learning (ML) techniques are exploited in many applications [8; 9; 10; 11]. One application is to monitor the optical network performance. The use of ML techniques holds great potential for enhancing the intelligence of optical network nodes. By utilizing ML, network nodes can gather insights from network conditions, which can then be applied to optimize network resources in the future. Various ML techniques have been suggested in the scientific literature for monitoring optical performance in both fiber-based optical links and FSO-based links that exploit IM/DD. Performance monitoring of IM/DD fiber-based optical communication links using an artificial neural network (ANN) model was investigated in [12; 13; 14; 15]. In [12], the ANN model was proposed for monitoring three parameters, which are optical signal-to-noise ratio (OSNR), CD, and differential group delay (DGD). The model was trained using features extracted from the eye diagrams of the received signal such as the root mean square and the jitter. However, this technique requires precise timing, which increases the system's complexity. In [13], the same parameters are monitored using an ANN model but trained with an asynchronous amplitude histogram (AAH). Using ANN was also proposed in [14; 15] for monitoring the OSNR, CD, and DGD parameters, using empirical moments and the asynchronous delay-tap sampling (ADTS) technique, respectively. A conventional neural network (CNN) model was proposed in [16] for monitoring OSNR and CD parameters. In this work, the input data for the CNN model are 2-D images generated using the ADTS technique. However, using images increases the complexity of the system. In [17], a deep neural network (DNN) model trained on features extracted from the signal eye diagram was proposed for monitoring the OSNR, CD, and DGD parameters. Monitoring FSO channel parameters was reported in [18; 19; 20; 21] using a CNN model. In [18; 19], turbulence impairment monitoring is investigated using orbital angular momentum (OAM) modes as a training feature for the model. Similarly, the OAM modes were used in [20] to monitor three impairment parameters, which include OSNR, turbulence, and pointing errors. In [21], the OAM modes are exploited for monitoring the visibility range under dusty weather conditions. The work in [22] used the support vector machine (SVM) technique to monitor the turbulence and OSNR parameters in addition to the visibility range under fog weather conditions. Table 1 summarizes the literature work on monitoring the performance of IM/DD optical fiber and FSO systems. In this work, we consider monitoring three main channel parameters that arise in the hybrid all-optical fiber/FSO channels; these are turbulence, OSNR, and CD. To the best of the authors' knowledge, this is the first study that addresses monitoring these impairments in a hybrid optical system of this kind. To achieve this goal, Gaussian process regression (GPR) is utilized as an ML model to predict the three parameters. The model's performance is evaluated using the root mean square error (RMSE) metric and the R-squared metric. Additionally, boxplot graphs are used as a visualization tool to evaluate the model's performance.