# Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks
###### Abstract
The purpose of using Back Propagation in a BPNN is to optimize the spectrum allocation algorithm based on information on previous connections, such as arrival time and hold time. Therefore, a collection of sample data is needed for the development of such a neural network. The proposed neural network consists of several layers. Each layer operates based on a set of variables: weight and threshold. In each layer, each node calculates a weighted sum of the input parameters. The weight variables are the coefficients of each input parameter. Then, the node triggers and excites the next layer if the result of the sum is greater than the threshold variable. If a neuron is triggered, its information can be used in  
Figure 2: Frequency slot spectrum allocation with a window size of 20 FSs. At t = t1, there are five connections (C1 to C5). At t = t2, C2 and C4 have departed. At t = t3, C6 and C7 are set up. the next layers; otherwise, the output of the neuron is zero and has no effect on the process of the next layer. The value of the weights and thresholds for each neuron is determined during the learning process. In [13], the Back Propagation technique is used to train the neural network (NN). In this technique, the input sampled data are given to the NN with an arbitrary set of thresholds and weights, and the output results of the NN are compared with the target value. The difference between these values (called error data) is used for improving the weights and thresholds of the neurons in the NN. This process is extended for multiple cycles, and gradually the difference between the network's output and the desired values is reduced. The weight values are updated during each cycle (epoch) of training, which is called the step size or the learning rate. In order to accommodate nonlinear changes and attain the anticipated result, a BPNN is used for prediction in EONs. The authors in [14] analyze two ML approaches that predict traffic in Elastic Optical Networks showing the benefits of using them with dynamic routing algorithms. First, a Monte Carlo Tree Search (MCTS) is presented for traffic prediction. This technique builds a sparse search tree and searches for the best traffic distribution and path pairs within this tree. Second, an unsupervised self-learning Artificial Neural Network (ANN) is used to forecast traffic fluctuations. The combination of these two methods can be used to provide an optimum arrangement for any specific demand. Many attempts have been made to efficiently predict network blocking by quantifying spectrum fragmentation in EONs [9; 16], but it becomes difficult to obtain a direct relationship between fragmentation and blocking. This is mainly due to the wavelength continuity and contiguity constraints, which make it very difficult to have a straight measure of the path's potential blocking likelihood. Observing the network and predicting the traffic using machine learning is the new trend in network traffic control [17]. Survivability and uninterrupted data flow are two critical aspects of a network. This means that machine learning methods play key roles for EONs. These methods are able to process data from traffic, predict blocking quickly, and prevent potential failures [17]. Several attempts have been made to achieve traffic and blocking prediction using neural networks, KNN, SVM, linear regression, principal component analysis, and linear time series [17; 18; 19]. The authors in [20] utilize the CNN model to predict dynamic link blockings in a 6G wireless communication network. Using specific spectral status snapshots, which lead to previous blocking events, while finding hidden patterns by means of a CNN seems to be a feasible solution for predicting connection blocking during EON operation. In this work, the application of a CNN to predict blocking in EONs is investigated. To the best of our knowledge, this is the first paper that introduces a CNN as a method for blocking prediction in Elastic Optical Networks.