Özet:

In this study, using the cognitive radio (CR)-based non-orthogonal multiple access (NOMA) technique, the total ergodic capacity value of the close user is estimated with high accuracy and fast training times through different training algorithms with the proposed feedforward backpropagation artificial neural network (ANN) and nonlinear autoregressive network with exogenous inputs (NARX) model. The data set used in the neural network was obtained from the CR-NOMA system model, which was modeled with the exponential fading channel characteristic. By training the input and output data to the ANN, which was designed using the supervised learning method, ergodic capacity estimates of the close user were made over the test data. While evaluating the performance of ANN and NARX neural networks, the training time, the number of iterations, and the conditions of the network not reaching saturation were taken into consideration. The actual ergodic capacity value of the close user and the predicted values of feedforward backpropagation ANN and NARX networks were compared. The performance analysis of the proposed neural networks under Levenberg-Marquardt, Bayesian and Scaled-Conjugate training algorithms has been examined in terms of epoch value graph, error histogram analysis, and training state analysis where the error reaches the minimum.

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