Managing the Resources of LTE Networks using Multi-orthogonal Access based on Deep Learning

Abstract

Abstract One of the topics discussed in telecommunications systems is joint subcarrier and power allocation in the uplink of an NOMA system that we study. Due to this reason a novel radio resource management framework is presented based on code-domain and a deep learning algorithm for uplink and downlink transmissions, such that the neural network is trained by Bayesian regularization back propagation and the mean squared error ) MSE) are the training criterion . Therefore, the presented method determines number of scheduled users. Keywords: Bayesian Regularization Algorithm (BRA), Mean Squared Error ) MSE), Multi-User Detection (MUD), NOMA, Random Repetition Algorithm (RIA). Cite this Article Hassan Naraghi. Managing the Resources of LTE Networks using Multi-orthogonal Access based on Deep Learning. Journal of Telecommunication, Switching Systems and Networks . 2020; 7(2): 19–26p.